BMC Neurosci 2020, 21(Suppl 1):54
https://doi.org/10.1186/s12868-020-00593-1

BMC Neuroscience

MEETING ABSTRACTSOpen Access29th Annual Computational Neuroscience
Meeting: CNS*2020
Virtual. 18–22 July 2020
Published: 21 December 2020

K1
Deep reinforcement learning and its neuroscientific implications
Matthew Botvinick
Google DeepMind, Neuroscience Research, London, United Kingdom
Correspondence: Matthew Botvinick (botvinick@google.com)
BMC Neuroscience 2020, 21(Suppl 1):K1
The last few years have seen some dramatic developments in artificial
intelligence research. What implications might these have for neuroscience? Investigations of this question have, to date, focused largely
on deep neural networks trained using supervised learning, in tasks
such as image classification. However, there is another area of recent
AI work which has so far received less attention from neuroscientists,
but which may have more profound neuroscientific implications: deep
reinforcement learning. Deep RL offers a rich framework for studying
the interplay among learning, representation and decision-making,offering to the brain sciences a new set of research tools and a wide
range of novel hypotheses. I’ll provide a high-level introduction to
deep RL, discuss some recent neuroscience-oriented investigations
from my group at DeepMind, and survey some wider implications forresearch on brain and behavior.
K2
A new computational framework for understanding vision in ourbrain
Zhaoping Li
Max Planck Institute for Biological Cybernetics, Tübingen, Germany
Correspondence: Zhaoping Li (li.zhaoping@tuebingen.mpg.de)
BMC Neuroscience 2020, 21(Suppl 1):K2
Visual attention selects only a tiny fraction of visual input information
for further processing. Selection starts in the primary visual cortex(V1), which creates a bottom-up saliency map to guide the fovea toselected visual locations via gaze shifts. This motivates a new framework that views vision as consisting of encoding, selection, and decoding stages, placing selection on center stage. It suggests a massive lossof non-selected information from V1 downstream along the visual
pathway. Hence, feedback from downstream visual cortical areas toV1 for better decoding (recognition), through analysis-by-synthesis,
should query for additional information and be mainly directed at the

foveal region. Accordingly, non-foveal vision is not only poorer in spatial resolution, but also more susceptible to many illusions.
K3
Information and decision‑making
Daniel Polani
University of Hertfordshire, School of Computer Science, Hatfield, United
Kingdom
Correspondence: Daniel Polani (d.polani@herts.ac.uk)
BMC Neuroscience 2020, 21(Suppl 1):K3
In recent years it has become increasingly clear that (Shannon) information is a central resource for organisms, akin in importance toenergy. Any decision that an organism or a subsystem of an organism
takes involves the acquisition, selection, and processing of information and ultimately its concentration and enaction. It is the consequences of this balance that will occupy us in this talk.
This perception-action loop picture of an agent’s life cycle is wellestablished and expounded especially in the context of Fuster’s sensorimotor hierarchies. Nevertheless, the information-theoretic perspective drastically expands the potential and predictive power of theperception-action loop perspective.
On the one hand information can be treated - to a significant extent- as a resource that is being sought and utilized by an organism. On
the other hand, unlike energy, information is not additive. The intrinsic structure and dynamics of information can be exceedingly complex
and subtle; in the last two decades one has discovered that Shannon information possesses a rich and nontrivial intrinsic structurethat must be taken into account when informational contributions,information flow or causal interactions of processes are investigated,
whether in the brain or in other complex processes.
In addition, strong parallels between information and control theory
have emerged. This parallelism between the theories allows one toobtain unexpected insights into the nature and properties of theperception-action loop. Through the lens of information theory, one
can not only come up with novel hypotheses about necessary conditions for the organization of information processing in a brain, butalso with constructive conjectures and predictions about what behaviours, brain structure and dynamics and even evolutionary pressures
one can expect to operate on biological organisms, induced purely by
informational considerations.

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﻿BMC Neurosci 2020, 21(Suppl 1):54

K4
Computational models of neural development
Geoffrey J. Goodhill
University of Queensland, Queensland Brain Institute;of Mathematics and Physics, St Lucia, Australia
Correspondence: Geoffrey J Goodhill (g.goodhill@uq.edu.au)
BMC Neuroscience 2020, 21(Suppl 1):K4

Page 2 of 123

School

Unlike even the most sophisticated current forms of artificial intelligence, developing biological organisms must build their neural hardware from scratch. Furthermore they must start to evade predatorsand find food before this construction process is complete. I will discuss an interdisciplinary program of mathematical and experimental
work which addresses some of the computational principles underlying neural development. This includes (i) how growing axons navigate
to their targets by detecting and responding to molecular cues intheir environment, (ii) the formation of maps in the visual cortex andhow these are influenced by visual experience, and (iii) how patternsof neural activity in the zebrafish brain develop to facilitate precisely
targeted hunting behaviour. Together this work contributes to ourunderstanding of both normal neural development and the etiologyof neurodevelopmental disorders.
F1
Delineating reward/avoidance decision process
in the impulsive‑compulsive spectrum disorders
through a probabilistic reversal learning task
Xiaoliu ­Zhang1, Chao ­Suo2, Amir D­ ezfouli3, Ben J ­Harrison4, Leah
­Braganza2, Ben ­Fulcher2, Lenardo F­ ontenelle2, Carsten M­ urawski5, Murat­Yucel2
1
Monash University, Monash Biomedical Imaging, Melbourne, Australia;
2
Monash University, BrainPark, Turner Institute for Brian and Mental
Health, School of Psychological Science, Melbourne, Australia; 3Machine
Learning Research Group, Data61, CSIRO, Sydney, Australia; 4Universityof Melbourne and Melbourne Health, Melbourne Neuropsychiatry Centre,
Department of Psychiatry, Melbourne, Australia; 5University of Melbourne,
Department of Finance, Melbourne, Australia
Correspondence: Xiaoliu Zhang (smile.in.sjtu@gmail.com)
BMC Neuroscience 2020, 21(Suppl 1):F1
Impulsivity and compulsivity are behavioural traits that underlie many
aspects of decision-making and form the characteristic symptoms ofObsessive Compulsive Disorder (OCD) and Gambling Disorder (GD).
The neural underpinnings of aspects of reward and avoidance learning under the expression of these traits and symptoms are only partially understood.
The present study combined behavioural modelling and neuroimaging technique to examine brain activity associated with critical phasesof reward and loss processing in OCD and GD.
Forty-two healthy controls (HC), forty OCD and twenty-three GD participants were recruited in our study to complete a two-session reinforcement learning (RL) task featuring a “probability switch (PS)” withimaging scanning. Finally, 39 HC (20F/19M, 34 yrs±9.47), 28 OCD(14F/14M, 32.11 yrs±9.53) and 16 GD (4F/12M, 35.53yrs±12.20) were
included with both behavioural and imaging data available. The functional imaging wasconducted by using 3.0-T SIEMENS MAGNETOM
Skyra syngo MR D13C at Monash Biomedical Imaging. Each volume
compromised 34 coronal slices of 3 mm thickness with 2000ms TR and
30ms TE. A total of 479 volumes were acquired for each participant ineach session in an interleaved-ascending manner.
The standard Q-learning model was fitted to the observed behaviouraldata and the Bayesian model was used for the parameter estimation.
Imaging analysis was conducted using SPM12 (Welcome Department
of Imaging Neuroscience, London, United Kingdom) in the Matlab(R2015b) environment. The pre-processing commenced with theslice timing, realignment, normalization to MNI space according to
T1-weighted image and smoothing with a 8 mm Gaussian kernel.

The frontostriatal brain circuit including the putamen and medial
orbitofrontal (mOFC) were significantly more active in response toreceiving reward and avoiding punishment compared to receiving anaversive outcome and missing reward at p < 0.001 with FEW correction at cluster level; While the right insula showed greater activation in
response to missing rewards and receiving punishment. Compared tohealthy participants, GD patients showed significantly lower activationin the left superior frontal and posterior cingulum at p < 0.001 for thegain omission.
The reward prediction error (PE) signal was found positively correlated with the activation at several clusters expanding across cortical and subcortical region including the striatum, cingulate, bilateralinsula, thalamus and superior frontal at p < 0.001 with FWE correction
at cluster level. The GD patients showed a trend of decreased reward
PE response in the right precentral extending to left posterior cingulatecompared to controls at p < 0.05 with FWE correction. The aversive PE
signal was negatively correlated with brain activity in regions including bilateral thalamus, hippocampus, insula and striatum at p < 0.001
with FWE correction. Compared with the control group, GD group
showed an increased aversive PE activation in the cluster encompassing right thalamus and right hippocampus, and also the right middle
frontal extending to the right anterior cingulum at p < 0.005 with FWE
correction.
Through the reversal learning task, the study provided further support of the dissociable brain circuits for distinct phases of reward andavoidance learning. Also, the OCD and GD are characterised by aberrant patterns of reward and avoidance processing.
F2
Using evolutionary algorithms to explore single‑cell heterogeneityand microcircuit operation in the hippocampus
Andrea Navas‑Olive, Liset M de la Prida
Cajal Institute, Madrid, Spain
Correspondence: Andrea Navas‑Olive (acnavasolive@gmail.com)
BMC Neuroscience 2020, 21(Suppl 1):F2
The hippocampus-entorhinal system is critical for learning and memory. Recent cutting-edge single-cell technologies from RNAseq toelectrophysiology are disclosing a so far unrecognized heterogeneity
within the major cell types [1]. Surprisingly, massive high-throughput
recordings of these very same cells identify low dimensional microcircuit dynamics [2,3]. Reconciling both views is critical to understandhow the brain operates.
The CA1 region is considered high in the hierarchy of the entorhinalhippocampal system. Traditionally viewed as a single layered structure, recent evidence has disclosed an exquisite laminar organization
across deep and superficial pyramidal sublayers at the transcriptional,
morphological and functional levels [1,4,5]. Such a low-dimensionalsegregation may be driven by a combination of intrinsic, biophysicaland microcircuit factors but mechanisms are unknown.
Here, we exploit evolutionary algorithms to address the effect of
single-cell heterogeneity on CA1 pyramidal cell activity [6]. First,
we developed a biophysically realistic model of CA1 pyramidal cells
using the Hodgkin-Huxley multi-compartment formalism in the
Neuron+Python platform and the morphological database Neuromorpho.org. We adopted genetic algorithms (GA) to identify passive,
active and synaptic conductances resulting in realistic electrophysiological behavior. We then used the generated models to explore thefunctional effect of intrinsic, synaptic and morphological heterogeneity during oscillatory activities. By combining results from all simulations in a logistic regression model we evaluated the effect of up/
down-regulation of different factors. We found that multidimensional
excitatory and inhibitory inputs interact with morphological andintrinsic factors to determine a low dimensional subset of output features (e.g. phase-locking preference) that matches non-fitted experimental data (Fig. 1).

﻿BMC Neurosci 2020, 21(Suppl 1):54

Fig. 1 Conceptualization of mechanisms operating over intrinsic andsynaptic factors to restrict neuronal firing of CA1 pyramidal cells dur‑ing theta oscillations. Non-linear dendritic integration of multidimen‑
sional excitatory and inhibitory inputs results in segregated firing
Acknowledgments: Andrea Navas-Olive is supported by PhD Fellowship FPU17/03268.

Page 3 of 123

A neuron with tens of thousands of synapses must regulate its own
excitability to compensate for changes in input. The time requirement for global feedback can introduce critical phase lags in a neuron’s response to perturbation. The severity of phase lag increases
with neuron size. Further, a more expansive morphology worsens
cell responsiveness and scaling accuracy, especially in distal regionsof the neuron. Local pools of reserve receptors improve efficiency,
potentiation, and scaling, but this comes at a cost. Trafficking large
quantities of receptors requires time, exacerbating the phase lag andinstability. Local homeostatic feedback mitigates instability, but thistoo comes at the cost of reducing scaling accuracy.
Realization of the phase lag instability requires a unified model of synaptic scaling, regulation, and transport. We present such a model with
global and local feedback in realistic neuron morphologies (Fig. 1).
This combined model shows that neurons face a tradeoff between stability, accuracy, and efficiency. Global feedback is required for synaptic
scaling but favors either system stability or efficiency. Large receptor
pools improve scaling accuracy in large morphologies but worsen
both stability and efficiency. Local feedback improves the stability-efficiency tradeoff at the cost of scaling accuracy. This project introduces
unexplored constraints on neuron size, morphology, and synaptic
scaling that are weakened by an interplay between global and local
feedback.

References
1. Cembrowski MS, Spruston N. Heterogeneity within classical cell types is
the rule: lessons from hippocampal pyramidal neurons. Nature Reviews
Neuroscience. 2019; 20(4): 193-204.
2. Chaudhuri R, Gerçek B, Pandey B, Peyrache A, Fiete I. The intrinsic attractor
manifold and population dynamics of a canonical cognitive circuit acrosswaking and sleep. Nature Neuroscience. 2019; 22(9): 1512-1520.
3. Guo W, Zhang JJ, Newman JP, Wilson MA. Latent learning drives sleepdependent plasticity in distinct CA1 subpopulations. bioRxiv. 2020; Preprint
at: https://doi.org/10.1101/2020.02.27.967794
4. Bannister NJ, Larkman AU. Dendritic morphology of CA1 pyramidal neu‑rones from the rat hippocampus: I. Branching patterns. Journal of Compara‑tive Neurology. 1995; 360: 150–160.
5. Valero M, et al. Determinants of different deep and superficial CA1 pyrami‑
dal cell dynamics during sharp-wave ripples. Nature Neuroscience. 2015;18(9): 1281-1290.
6. Navas-Olive A, et al. Multimodal determinants of phase-locked dynamics
across deep-superficial hippocampal sublayers during theta oscillations.
Nature Communications. 2020; 11(1): 1-4.

F3
Neuronal morphology imposes a tradeoff between stability,
accuracy and efficiency of synaptic scaling
Adriano Bellotti, Saeed Aljaberi, Fulvio Forni, Timothy O’Leary
University of Cambridge, Department of Engineering, Cambridge, United
Kingdom
Correspondence: Adriano Bellotti (adriano.bellotti@gmail.com)
BMC Neuroscience 2020, 21(Suppl 1):F3
Synaptic scaling is a homeostatic normalization mechanism that preserves relative synaptic strengths by adjusting them with a common
factor. This multiplicative change is believed to be critical, since synaptic strengths are involved in learning and memory retention. Further,
this homeostatic process is thought to be crucial for neuronal stability, playing a stabilizing role in otherwise runaway Hebbian plasticity[1-3]. Synaptic scaling requires a mechanism to sense total neuron
activity and globally adjust synapses to achieve some activity set-point[4]. This process is relatively slow, which places limits on its ability tostabilize network activity [5]. Here we show that this slow response isinevitable in realistic neuronal morphologies. Furthermore, we revealthat global scaling can in fact be a source of instability unless responsiveness or scaling accuracy are sacrificed.

Fig. 1 Schematic representation of unified model of AMPA receptor
transport, potentiation, and scaling with global and local feedback
Acknowledgements: The authors are supported by European
Research Council Grant FLEXNEURO (716643), Abu Dhabi National Oil
Company, NIH OxCam Scholars program and Gates Cambridge Trust.
References
1. Royer S, Paré D. Conservation of total synaptic weight through balanced
synaptic depression and potentiation. Nature. 2003; 422(6931): 518-522.
2. Chen JY, et al. Heterosynaptic plasticity prevents runaway synaptic dynam‑
ics. Journal of Neuroscience. 2013; 33(40): 15915-15929.
3. Chistiakova M, Bannon NM, Chen JY, Bazhenov M, Volgushev M. Homeo‑
static role of heterosynaptic plasticity: models and experiments. Frontiers in
Computational Neuroscience. 2015; 9: 89.
4. Turrigiano GG. The self-tuning neuron: synaptic scaling of excitatory syn‑apses. Cell. 2008; 135(3): 422-435.
5. Zenke F, Hennequin G, Gerstner W. Synaptic plasticity in neural networks
needs homeostasis with a fast rate detector. PLoS Computational Biology.
2013; 9(11).

﻿BMC Neurosci 2020, 21(Suppl 1):54

F4
Who can turn faster? Comparison of the head direction circuitof two species
Ioannis ­Pisokas1, Stanley ­Heinze2, Barbara ­Webb1
1
University of Edinburgh, School of Informatics, Edinburgh, United
Kingdom; 2Lund University, Department of Biology, Lund, Sweden
Correspondence: Ioannis Pisokas (i.pisokas@sms.ed.ac.uk)
BMC Neuroscience 2020, 21(Suppl 1):F4
Ants, bees and other insects have the ability to return to their nest or hive
using a navigation strategy known as path integration. Similarly, fruit flies
employ path integration to return to a previously visited food source. An
important component of path integration is the ability of the insect tokeep track of its heading relative to salient visual cues. A highly conserved
brain region known as the central complex has been identified as being of
key importance for the computations required for an insect to keep trackof its heading [1,2]. However, the similarities or differences of the underlying heading tracking circuit between species are not well understood.
We sought to address this shortcoming by using reverse engineering
techniques to derive the effective underlying neuronal circuits of two evolutionary distant species, the fruit fly and the locust. Our analysis revealed
that regardless of the anatomical differences between the two species theessential circuit structure has not changed. Both effective neuronal circuits
have the structural topology of a ring attractor with an eight-fold radial
structure (Fig. 1). However, despite the strong similarities between the tworing attractors, there remain differences. Using computational modelling
we found that two apparently small anatomical differences have significant functional effect on the ability of the two circuits to track fast rotational movements and to maintain a stable heading signal [6]. In particular,
the fruit fly circuit responds faster to abrupt heading changes of the animal
while the locust circuit maintains a heading signal that is more robust to
inhomogeneities in cell membrane properties and synaptic weights. We
suggest that the effects of these differences are consistent with the behavioural ecology of the two species. On the one hand, the faster response of
the ring attractor circuit in the fruit fly accommodates the fast body saccades that fruit flies are known to perform. On the other hand, the locust
is a migratory species, so its behaviour demands maintenance of a definedheading for a long period of time. Our results highlight that even seemingly small differences in the distribution of dendritic fibres can have asignificant effect on the dynamics of the effective ring attractor circuit withconsequences for the behavioural capabilities of each species. These differences, emerging from morphologically distinct single neurons highlightthe importance of a comparative approach to neuroscience.

Fig. 1 A-F Schematics and example neuron anatomy of the fruit fly
and the desert locust (Reproduced with permission from [3,4,5]). G, H
Effective circuit in the fruit fly and the locust, respectively. I Maximum
rate of heading change sustained by each model for different magni‑
tudes of heading change. J Ring attractor stability as function of noisein the cell membrane parameters

Page 4 of 123

References
1. Neuser K, Triphan T, Mronz M, Poeck B, Strauss R. Analysis of a spatial orien‑
tation memory in Drosophila. Nature. 2008; 453(7199): 1244–1247.
2. Pfeiffer K, Homberg U. Organization and functional roles of the central com‑plex in the insect brain. Annual Review of Entomology. 2013; 59(1):165–184.
3. Wolff T, Rubin G M. Neuroarchitecture of the Drosophila central complex:a catalog of nodulus and asymmetrical body neurons and a revision of the
protocerebral bridge catalog. Journal of Comparative Neurology. 2018;
526(16): 2585–2611.
4. Vitzthum H, Homberg U. Immunocytochemical demonstration of locus‑
tatachykinin-related peptides in the central complex of the locust brain.
Journal of Comparative Neurology. 1998; 390(4): 455–469.
5. Beetz MJ, el Jundi B, Heinze S, Homberg U. Topographic organization andpossible function of the posterior optic tubercles in the brain of the desert
locust Schistocerca gregaria. Journal of Comparative Neurology. 2015;
523(11): 1589–1607.
6. Pisokas I, Heinze S, Webb B. The head direction circuit of two insect species.
bioRxiv 2019.

O1
Dopamine role in learning and action inference
Rafal Bogacz
University of Oxford, MRC Brain Network Dynamics Unit, Oxford, United
Kingdom
Correspondence: Rafal Bogacz (rafal.bogacz@ndcn.ox.ac.uk)
BMC Neuroscience 2020, 21(Suppl 1): O1
Much evidence suggests that some dopaminergic neurons respond to
unexpected rewards, and computational models have suggested thatthese neurons encode reward prediction error, which drives learning about rewards. However, these models do not explain recently
observed diversity of dopaminergic responses, and dopamine function in action planning, evident from movement difficulties in Parkinson’s disease. The presented work aims at extending existing models
to account for these data. It proposes that a more complete description of dopaminergic activity can be achieved by combining reinforcement learning with elements of other recently proposed theories
including active inference.
The presented model describes how the basal ganglia network infers
actions required to obtained reward using Bayesian inference. The
model assumes that a likelihood of reward given action in encodedby the goal-directed system, while the prior probability of making aparticular action in a given context is provided by the habit system.
It is shown how the inference of the optimal action can be achieved
through minimization of free-energy, and how this inference canbe implemented in a network with an architecture bearing a striking resemblance to the known anatomy of the striato-dopaminergic
circuit. In particular, this network includes nodes encoding prediction errors, which are connected with other nodes in the network ina way resembling the “ascending spiral” structure of dopaminergic
connections.
In the proposed model, dopaminergic neurons projecting to different
parts of the striatum encode errors in predictions made by the corresponding systems within the basal ganglia. These prediction errors areequal to differences between rewards and expectations in the goaldirected system, and to differences between the chosen and habitual
actions in the habit system. The prediction errors enable learning
about rewards resulting from actions and habit formation. During
action planning, the expectation of reward in the goal-directed system
arises from formulating a plan to obtain that reward. Thus dopaminergic neurons in this system provide feedback on whether the current
motor plan is sufficient to obtain the available reward, and they facilitate action planning until a suitable plan is found. Presented models
account for dopaminergic responses during movements, effects of
dopamine depletion on behaviour, and make several experimental
predictions.

﻿BMC Neurosci 2020, 21(Suppl 1):54

O2
Neural manifold models for characterising brain circuit dynamics
in neurodegenerative disease
Seigfred ­Prado1, Simon R. ­Schultz2, Mary A. G
­ o1
1Imperial College London, Department of Bioengineering, London, United
Kingdom; 2Imperial College London, London, United Kingdom
Correspondence: Seigfred Prado (s.prado17@imperial.ac.uk)
BMC Neuroscience 2020, 21(Suppl 1): O2
Although much is known about neural circuits and molecular pathways required for normal hippocampal functions, the processes bywhich neurodegenerative diseases, such as Alzheimer’s Disease (AD),disable the functioning of the hippocampus and connected structures
remain to be determined. In order to make substantial advances in thetreatment of such diseases, we must improve our understanding of
how neural circuits process information and how they are disrupted
during the progression of these diseases. Recent advances in opticalimaging technologies that allow simultaneous recording of large populations of neurons in deeper structures [1] have shown great promise for revealing circuit dynamics during memory tasks [2]. However,to date, no study has revealed how large numbers of neurons in hippocampal-cortical circuits act together to encode, store and retrieve
memories in animal models of AD. In this work, we explored the useof neural manifold analysis techniques to characterising brain circuit
dynamics in neurodegenerative disease. To understand more precisely
the basis of memory and cognitive impairments in AD, we extracted
the underlying neural manifolds in large-scale neural responses ofhippocampal circuits involved in spatial cognition of behaving mice.
For validation, we simulated a model that generates a set of data thatmimics the neural activity of hippocampal cells of mouse models running on a linear circular track, while taking into account the effects ofamyloid-beta plaques on circuit dynamics [3]. We compare our modelwith real data obtained by multiphoton imaging of hippocampal CA1
cells in mice engaged in a spatial memory task. We used recurrence
analysis to show how neural manifolds evolve over time during memory encoding, storage and recall processes in a repetitive memorytask. This work will help with understanding how amyloid-beta proteins affect the neural manifolds for spatial memory, which is particularly disturbed during AD.

Fig. 1 Analyses for a mouse during a 4-minute recording session.
a Cell activity and mouse position along the linear circular track. b
Place map showing neuronal tuning curves. c Cumulative fraction ofvariance explained by manifolds of increasing dimensionality. d-f The
distribution of data points in the reduced dimensional space using
MDS, PCA and LEM, respectively. g-i Recurrence plots of d-fPage 5 of 123

References
1. Schultz SR, Copeland CS, Foust AJ, Quicke P, Schuck R. Advances in twophoton scanning and scanless microscopy technologies for functional
neural circuit imaging. Proceedings of the IEEE. 2016; 105(1): 139-157.
2. Low RJ, Lewallen S, Aronov D, Nevers R, Tank DW. Probing variability in a
cognitive map using manifold inference from neural dynamics. bioRxiv.
2018; 418939.
3. Busche MA, et al. Clusters of hyperactive neurons near amyloid plaques in amouse model of Alzheimer’s disease. Science. 2008; 321(5896): 1686-1689.

O3
Coupled experimental and modeling representationof the mechanisms of epileptic discharges in rat brain slices
Anton ­Chizhov1, Dmytry ­Amakhin2, Elena S­ mirnova2, Aleksey ­Zaitsev2
1
Ioffe Institute, Saint‑Petersburg, Russia; 2Sechenov Instituteof Evolutionary Physiology and Biochemistry of the Russian Academyof Sciences, Laboratory of Molecular Mechanisms of Neural Interactions,Saint Petersburg, Russia
Correspondence: Anton Chizhov (anton.chizhov@mail.ioffe.ru)
BMC Neuroscience 2020, 21(Suppl 1):O3
Epileptic seizures and interictal discharges (IIDs) are determined byneuronal interactions and ionic dynamics and thus help to reveal
valuable knowledge about the mechanisms of brain functioning in
not only pathological but also normal state. As synchronized pathological discharges are much simpler to study than normal functioning,
we were able to accomplish their description with a set of electrophysiological evidences constrained by a biophysical mathematicalmodel. In the combined hippocampal-entorhynal cortex slices of rat
in high potassium, low magnesium and 4-AP containing solution weevaluated separate AMPA, NMDA and GABA-A conductances for different types of IIDs, using an original experimental technique [1]. The
conductances have shown that the first type of the discharges (IID1)
is determined by activity of only GABA-A channels due to their pathologically depolarized reversal potential. The second type (IID2) is determined by an early GABA-A followed by AMPA and NMDA components.
The third type is pure glutamatergic discharges observed in case of
disinhibition. Our mathematical model of interacting neuronal populations reproduces the recorded synaptic currents and conductances
for IIDs of the three types [2,3], confirming the major role of interneuron synchronization for IID1 and IID2, and revealing that the durationof IIDs is determined mainly by synaptic depression. IIDs occur spontaneously and propagate as waves with a speed of about a few tens ofmm/s [4]. IDs are clusters of IID-like discharges and are determined by
the ionic dynamics [5]. To reveal only major processes, main ions andvariables, we have formulated a reduced mathematical model “Epileptor-2”, which is a minimal model that reproduces both IDs and IIDs [6](Fig. 1). It shows that IIDs are spontaneous bursts that are governedby the membrane depolarization and synaptic resource, whereas IDs
represent bursts of bursts. Important is the role of the Na/K-ATPhase.
Potassium accumulation governs the onset of each ID. The sodium
accumulates during the ID and activates the sodium-potassium pump,which terminates the ID by restoring the potassium gradient and thus
repolarizing the neurons. A spatially-distributed version of the Epileptor-2 model reveals that it is not extracellular potassium diffusion but
synaptic connectivity determines the speed of the ictal wavefront [7],which is consistent with our optogenetic experiments. The revealed
factors are to be potential targets for antiepileptic medical treatment.

﻿BMC Neurosci 2020, 21(Suppl 1):54

Fig. 1 Minimal model Epileptor-2 reproduces ictal events observedin slices. Intracellular sodium and extracellular potassium concentra‑
tions and membrane potential of a representative neuron are shown
Acknowledgments: This work was supported by the Russian Science
Foundation (project 16-15-10201).
References
1. Amakhin DV, Ergina JL, Chizhov AV, Zaitsev AV. Synaptic conductances
during interictal discharges in pyramidal neurons of rat entorhinal cortex.
Frontiers in Cellular Neuroscience. 2016; 10: 233.
2. Chizhov A, Amakhin D, Zaitsev A. Computational model of interictal dis‑charges triggered by interneurons. PLoS ONE. 2017; 12(10): e0185752.
3. Chizhov AV, Amakhin DV, Zaizev AV, Magazanik LG. AMPAR-mediated Inter‑
ictal Discharges in Neurons of Entorhinal Cortex: Experiment and Model.
Doklady Biological Sciences. 2018; 479(1): 47-50.
4. Chizhov AV, Amakhin DV, Zaitsev AV. Spatial propagation of interictal
discharges along the cortex. Biochemical and Biophysical Research Com‑
munications. 2019; 508(4): 1245-1251.
5. Chizhov AV, Amakhin DV, Zaitsev AV. Mathematical model of Na-K-Cl home‑ostasis in ictal and interictal discharges. PLoS ONE. 2019; 14(3): e0213904.
6. Chizhov AV, Zefirov AV, Amakhin DV, Smirnova EY, Zaitsev AV. Minimalmodel of interictal and ictal discharges “Epileptor-2”. PLoS Computational
Biology. 2018; 14(5): e1006186.
7. Chizhov AV, Sanin AE. A simple model of epileptic seizure propagation:
Potassium diffusion versus axo-dendritic spread. PLoS ONE. 2020; 15(4):e0230787.

O4
Towards multipurpose bio‑realistic models of cortical circuits
Anton Arkhipov, Yazan N Billeh, Binghuang Cai, Sergey L Gratiy, Kael Dai,
Ramakrishnan Iyer, Nathan W Gouwens, Reza Abbasi‑Asl, Xiaoxuan Jia,
Joshua H Siegle, Shawn R Olsen, Stefan Mihalas, Christof Koch
Allen Institute for Brain Science, Seattle, Washington State, United Statesof America
Correspondence: Anton Arkhipov (antona@alleninstitute.org)
BMC Neuroscience 2020, 21(Suppl 1): O4
One of the central questions in neuroscience is how structure of brain
circuits determines their activity and function. To explore such structure-function relations systematically, we integrate information fromlarge-scale experimental surveys into data-driven, bio-realistic modelsof brain circuits, with the current focus on the mouse cortex.
Our 230,000-neuron models of the mouse cortical area V1 [1] were constructed at two levels of granularity—using either biophysically-detailed
or point-neurons. These models systematically integrated a broadarray of experimental data [1-3]: the information about distribution andPage 6 of 123

morpho-electric properties of different neuron types in V1; connection
probabilities, synaptic weights, axonal delays, and dendritic targeting rules
inferred from a thorough survey of the literature; and a sophisticated representation of visual inputs into V1 from the Lateral Geniculate Nucleus,
fit to in vivo recordings. The model activity has been tested against largescale in vivo recordings of neural activity [4]. We found a good agreement
between these experimental data and the V1 models for a variety of metrics, such as direction selectivity, as well as less good agreement for othermetrics, suggesting avenues for future improvements. In the process ofbuilding and testing models, we also made predictions about the logic ofrecurrent connectivity with respect to functional properties of the neurons,some of which have been verified experimentally [1].
In this presentation, we will focus on the model’s successes in quantitative matching of multiple experimental measures, as well as failuresin matching other metrics. Both successes and failures shed light on
the potential structure-function relations in cortical circuits, leading
to experimentally testable hypotheses. Our models are shared freely
with the community: https://portal.brain-map.org/explore/models/
mv1-all-layers. We also freely share our software tools – the Brain Modeling ToolKit (BMTK; alleninstitute.github.io/bmtk/), which is a software suite for model building/simulation [5], and the SONATA [6] fileformat (github.com/allenInstitute/sonata).
References
1. Billeh YN, et al. Systematic integration of structural and functional data into
multi-scale models of mouse primary visual cortex. Neuron. 2020.
2. Gouwens NW, et al. Classification of electrophysiological and morphologicalneuron types in the mouse visual cortex. Nature neuroscience. 2019; 22(7):1182-1195.
3. Gouwens NW, et al. Systematic generation of biophysically detailed models
for diverse cortical neuron types. Nature communications. 2018; 9(1): 1-13.
4. Siegle JH, et al. A survey of spiking activity reveals a functional hierarchy ofmouse corticothalamic visual areas. bioRxiv. 2019; 805010 [preprint].
5. Gratiy SL, et al. BioNet: A Python interface to NEURON for modelling largescale networks. PLoS One. 2019; 13(8): e0201630.
6. Dai K, et al. The SONATA data format for efficient description of large-scale
network models. PLoS Computational Biology. 2020; 16(2): e1007696.

O5
How stimulus statistics affect the receptive fields of cells in primaryvisual cortex
Ali ­Almasi1, Hamish M­ effin2, Shi S­ un3, Michael R I­bbotson41
National Vision Research Institute, Melbourne, Australia; 2Universityof Melbourne, Biomedical Engineering, Melbourne, Australia; 3Universityof Melbourne, Optometry and Vision Sciences, Melbourne, Australia;
4Australian College of Optometry, The National Vision Research Institute,
Carlton, Australia
Correspondence: Ali Almasi (aalmasi@aco.org.au)
BMC Neuroscience 2020, 21(Suppl 1): O5
Our understanding of sensory coding in the visual system is largely
derived from parametrizing neuronal responses to basic stimuli.
Recently, mathematical tools have developed that allow estimating
the parameters of a receptive field (RF) model, which are typically acascade of linear filters on the stimulus, followed by static nonlinearities that map the output of the filters to the neuronal spike rates. However, how much do these characterizations depend on the choice ofthe stimulus type?
We studied the changes that neuronal RF models undergo due to thechange in the statistics of the visual stimulus. We applied the nonlinear input model (NIM) [1] to the recordings of single units in cat primary visual cortex (V1) in response to white Gaussian noise (WGN)
and natural scenes (NS). These two stimulus types were matched intheir global RMS contrast; however, they are fundamentally different in terms of second- and higher-order statistics, which are abundant in natural scenes but do not exist in white noise. We estimatedfor each cell the spatial filters constituting the neuronal RF and theircorresponding nonlinear pooling mechanism, while making minimal
assumptions about the underlying neuronal processing.
We found that cells respond differently to these two stimulus types, withmostly higher spike rates and shorter response latencies to NS than to﻿BMC Neurosci 2020, 21(Suppl 1):54

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WGN. The most striking finding was that NS stimuli resulted in around
twice as many uncovered RF filters compared to using WGN stimuli. Via
careful analysis of the data, we discovered that this difference between
the number of identified RF filters is not related to the higher spike rates
of cells to NS stimuli. Instead, we found it to be attributed to the difference in the contrast levels of specific features that exhibit different
prevalence in NS versus WGN. These features correspond to the V1 RF
filters recovered in the model. This specific feature-contrast attains muchhigher values in NS compared to WGN stimuli. When the feature-contrast
is controlled for, it explains the differences in the number of RF filters
obtained. Our findings imply that a greater extent of nonlinear processing in V1 neurons can be uncovered using natural scene stimulation.
We also compared the identified RF filters under the two stimulation
regimes in terms of their spatial characteristics. Population analysisof the RF filters revealed a statistically significant bias towards higher
spatial frequency filters with narrower spatial frequency bandwidth
under the NS stimulation regime (p-value < 0.0025).
Acknowledgements: The authors acknowledge the support the Australian Research Council Centre of Excellence for Integrative Brainfunction (CE140100007), the National Health and Medical ResearchCouncil (GNT1106390), and Lions Club of Victoria.

Next, we set out to model the responses of AOB neurons. Briefly,
AOB responses to a given stimulus are modelled as dot products ofrandom tuning profiles to specific chemicals and the actual level of
those chemicals in the stimulus. In this manner we derive a population of AOB responses, which we can then compare to the measured
responses. Our analysis thus far reveals several important insights.
First, neuronal response properties are best accounted for by sampling of protein/peptide components, but not by volatile urinary
components. This is consistent with the known physiology of the VNS.
Second, several response features (population level neuronal distances, sparseness, distribution of receptive field types) are best reproduced in the model with random sampling of multiple, rather thansingle molecules per neuron. This suggests that the sampling mode of
AOB neurons may mitigate some of the consequences of random sampling. Finally, we note that random sampling of molecules provides areasonable fit for some, but not all metrics of the observed responses.
Our ongoing work aims to identify which changes must be made to
our initial simplistic model in order to account for these features.
Acknowledgement: This work is funded by GIF and DFG grants to
Marc Spehr and Yoram Ben-Shaul.

References
1. McFarland JM, Cui Y, Butts DA. Inferring nonlinear neuronal computation
based on physiologically plausible inputs. PLoS Computational Biology.
2013; 9(7).

O7‘Awake delta’ and theta‑rhythmic modes of hippocampal network
activity track intermittent locomotor behaviors in rats
Nathan ­Schultheiss1, Tomas ­Guilarte2, Tim ­Allen1
1
Florida International University, Psychology, Miami, United Statesof America; 2Florida International University, Robert Stempel Collegeof Public Health and Social Work & Environmental Health Sciences, Miami,
United States of America
Correspondence: Nathan Schultheiss (nschulth@fiu.edu)
BMC Neuroscience 2020, 21(Suppl 1):O7

O6
Analysis and modelling of response features of accessory olfactory
bulb neurons
Yoram Ben‑Shaul1, Rohini B­ ansal1, Romana S­ topkova2, Maximilian ­Nagel3,
Pavel ­Stopka2, Marc ­Spehr3
1
The Hebrew University, Medical Neurobiology, Jerusalem, Israel; 2Charles
University Prague, Zoology, Prague, Czechia; 3RWTH Aachen University,
Chemosensation, Aachen, Germany
Correspondence: Yoram Ben‑Shaul (yoramb@ekmd.huji.ac.il)
BMC Neuroscience 2020, 21(Suppl 1):O6
The broad goal of this work is to understand how consistency on amacroscopic scale can be achieved despite random connectivity at thelevel of individual neurons.
A central aspect of any sensory system is the manner by which features of the external world are represented by neurons at various processing stages. Yet, it is not always clear what these features are, how
they are represented, and how they emerge mechanistically. Here, weinvestigate this issue in the context of the vomeronasal system (VNS),
a vertebrate chemosensory system specialized for processing of cues
from other organisms. We focus on the accessory olfactory bulb AOB,
which receives all vomeronasal sensory neuron inputs. Unlike themain olfactory system, where MTCs sample information from a single
receptor type, AOB MTCs sample information from a variable numberof glomeruli, in a manner that seems largely random. This apparently
random connectivity is puzzling given the presumed role of this system in processing cues with innately relevant significance.
We use multisite extracellular recordings to measure the responses ofmouse AOB MTCs to controlled presentation of natural urine stimuli
from male and female mice from various strains, including from wild
mice. Crucially, we also measured the levels of both volatile and peptide chemical components in the very same stimulus samples thatwere presented to the mice. As subjects, we used two genetically distinct mouse strains, allowing us to test if macroscopic similarity canemerge despite variability at the level of receptor expression.
First, we then explored neuronal receptive fields, and found that neurons selective for specific strains (regardless of sex), or a specific sex(regardless of strain), are less common than expected by chance. Thisis consistent with our previous findings indicating that high levelstimulus features are represented in a distributed manner in the AOB.
We then compared various aspects of neuronal responses across thetwo strains, and found a high degree of correlation among them, suggesting that despite apparent randomness and strain specific genetic
aspects, consistent features emerge at the level of the AOB.

Delta-frequency activity in the local field potential (LFP) is widelybelieved to correspond to so-called ‘cortical silence’ during phases of
non-REM sleep, but delta in awake behaving animals is not well understood and is rarely studied in detail. By integrating novel analyses of
the hippocampal (HC) LFP with simultaneous behavioral tracking, weshow for the first time that HC synchronization in the delta frequency
band (1-4 Hz) is related to animals’ locomotor behaviors during free
exploration and foraging in an open field environment. In contrast to
well-established relationships between animals’ running speeds andthe theta rhythm (6-10 Hz), we found that delta was most prominent
when animals were stationary or moving slowly (i.e. when theta andfast gamma (65-120 Hz) were weak). Furthermore, delta synchronization often developed rapidly when animals paused briefly between
intermittent running bouts.
Next, we developed an innovative strategy for identifying putativemodes of network function based on the spectral content of the LFP.
By applying hierarchical clustering algorithms to time-windowed
power spectra throughout behavioral sessions (i.e. the spectrogram),
we categorized moment-by-moment estimations of the power
spectral density (PSD) into spectral modes of HC activity. That is, weoperationalized putative functional modes of network computation as
spectral modes of LFP activity. Delta and theta power were strikingly
orthogonal across the resultant spectral modes, suggesting the possibility that delta- and theta-dominated hippocampal activity patterns
represent distinct modes of HC function during navigation. Delta andtheta were also remarkably orthogonal across precisely-defined boutsof running and stationary behavior, indicating that the stops-andstarts that compose rats’ locomotor trajectories are accompanied byalternating delta- and theta-dominated HC states.
We then asked whether the incidence of delta and theta modes was
related to the coherence between recording sites in hippocampus
or between hippocampus and medial prefrontal cortex (mPFC). We
found that intrahippocampal coherences in both the delta-band
and the theta-band were monotonically related to theta-delta ratios
across modes. Furthermore, in two rats implanted with dual-site
recording arrays, we found that theta coherence between HC andmPFC increased during running, and delta-band coherence between
mPFC and HC increased during stationary bouts. Taken together,﻿BMC Neurosci 2020, 21(Suppl 1):54

our findings suggest that delta-dominated network modes (and corresponding mPFC-HC couplings) represent functionally-distinct
circuit dynamics that are temporally and behaviorally interspersed
among theta-dominated modes during spatial navigation. As such,delta modes could play a fundamental role in coordinating mnemonic functions including encoding and retrieval mechanisms, ordecision-making processes incorporating prospective or retrospective representations of experience, at a timescale that segments event
sequences within behavioral episodes.
O8
Finite element simulation of ionic electrodiffusion in cellular
geometries
Ada J Ellingsrud
Simula Research Laboratory, Scientific Computing and Numerical Analysis,
Oslo, Norway
Correspondence: Ada J Ellingsrud (ada@simula.no)
BMC Neuroscience 2020, 21(Suppl 1):O8
Electrical conduction in brain tissue is commonly modeled using classical bidomain models. These models fundamentally assume that thediscrete nature of brain tissue can be represented by homogenizedequations where the extracellular space, the cell membrane, andthe intracellular spare are continuous and exist everywhere. Consequently, they do not allow simulations highlighting the effect of anonuniform distribution of ion channels along the cell membrane or
the complex morphology of the cells. In this talk, we present a moreaccurate framework for cerebral electrodiffusion with an explicit representation of the geometry of the cell, the cell membrane and theextracellular space. To take full advantage of this framework, a numerical solution scheme capable of efficiently handling three-dimensional,
complicated geometries is required. We propose a novel numericalsolution scheme using a mortar finite element method, allowing forthe coupling of variational problems posed over the non-overlapping
intra and extracellular domains by weakly enforcing interface conditions on the cell membrane. This solution algorithm flexibly allows forarbitrary geometries and efficient solution of the separate subproblems. Finally, we study ephaptic coupling induced in an unmyelinatedaxon bundle and demonstrate how the presented framework can givenew insights in this setting. Simulations of 9 idealized, tightly packed
axons show that inducing action potentials in one or more axons
yields ephaptic currents that have a pronounced excitatory effect onneighboring axons, but fail to induce action potentials there [1].
Acknowledgements: This project has received funding from theEuropean Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme under grant agreement 714892 (Waterscales), and from the Research Council of Norway(BIOTEK2021 Digital Life project ‘DigiBrain’, project 248828).
References
1. Ellingsrud AJ, Solbrå A, Einevoll GT, Halnes G, Rognes ME. Finite element
simulation of ionic electrodiffusion in cellular geometries. Frontiers in
Neuroinformatics. 2020; 14: 11.

O9
Discovering synaptic mechanisms underlying the propagationof cortical activity: a model‑driven experimental and data analysis
approach
Heidi ­Teppola1, Jugoslava ­Acimovic1, Marja‑Leena ­Linne2
1
Tampere University, Faculty of Medicine and Health Technology, Tampere,
Finland; 2Tampere University, Medicine and Health Sciences, Tampere,
Finland
Correspondence: Ada J Ellingsrud (heidi.teppola@tuni.fi)
BMC Neuroscience 2020, 21(Suppl 1):O9
Spontaneous, synchronized activity is a well-established feature ofcortical networks in vitro and in vivo. The landmark of this activity is therepetitive emergence of bursts propagating across networks as spatio-temporal patterns. Cortical bursts are governed by excitatory andinhibitory synapses via AMPA, NMDA and G­ ABAA receptors. Although

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spontaneous activity is a well-known phenomenon in developing networks, its specific underlying mechanisms in health and disease are
not fully understood. In order to study the synaptic mechanisms regulating the propagation of cortical activity it is important to combine
the experimental wet-lab studies with in silico modeling and build
detailed, realistic, computational models of cortical network activity. Moreover, experimental studies and analysis of microelectrodearray (MEA) data are not typically designed to support computationalmodeling. We show here how the synaptic AMPA, NMDA and ­GABAA
receptors shape the initiation, propagation and termination of the cortical burst activity in rodent networks in vitro and in silico and develop
model-driven data analysis workflow to support the development of
spiking and biophysical network models in silico [1].
We created a model-driven data analysis workflow with multiple steps
to examine the contributions of synaptic receptors to burst dynamics
both in vitro and in silico neuronal networks (Fig. 1). First, the cortical networks were prepared from the forebrains of the postnatal rats
and maintained on MEA plates. Second, network-wide activity wasrecorded by MEA technique under several pharmacological conditionsof receptor antagonists. Third, multivariate data analysis was conducted in a way that supports both neurobiological questions as wellas the fitting and validation of computational models to quantitatively
produce the experimental results. Fourth, the computational models
were simulated with different parameters to test putative mechanisms
responsible for network activity.

Fig. 1 Model-driven data analysis workflow for discovering synaptic
mechanisms underlying the propagation of cortical activity in vitro
and in silico
The experimental results obtained in this study show that AMPA
receptors initiate bursts by rapidly recruiting cells whereas NMDA
receptors maintain them. ­GABAA receptors inhibit the spiking frequency of AMPA receptor-mediated spikes at the onset of bursts
and attenuate the NMDA receptor-mediated late phase. These findings highlight the importance of both excitatory and inhibitory synapses in activity propagation and demonstrate a specific interaction
between AMPA and G­ ABAA receptors for fast excitation and inhibition.
In the presence of this interaction, the spatio-temporal propagation
patterns of activity are richer and more diverse than in its absence.
Moreover, we emphasize the systematic data analysis approach withmodel-driven workflow throughout the study for comparison of
results obtained from multiple in vitro networks and for validation ofdata-driven model development in silico. A well-defined workflow canreduce the amount of biological experiments, promote more reliable
and efficient use of the MEA technique, and improve the reproducibility of research. It helps reveal in detail how excitatory and inhibitory

﻿BMC Neurosci 2020, 21(Suppl 1):54

synapses shape cortical activity propagation and dynamics in rodent
networks in vitro and in silico.
References
1. Teppola H, Aćimović J, Linne M-L. Unique features of network bursts
emerge from the complex interplay of excitatory and inhibitory receptors in
rat neocortical networks. Frontiers in Cellular Neuroscience. 2019; 13(377):
1-22.

O10
Neural flows: estimation of wave velocities and identificationof singularities in 3D+t brain data
Paula Sanz‑Leon1, Leonardo L G­ ollo2, James A ­Roberts3
1
University of Sydney, QIMR Berghofer, Brisbane, Australia; 2QIMR
Berghofer/ Monash University, Melbourne, Australia; 3QIMR Berghofer,
Computational Biology, Brisbane, Australia
Correspondence: Matthew Botvinick (pmsl.academic@gmail.com)
BMC Neuroscience 2020, 21(Suppl 1):O10
Background: Neural activity organizes in constantly evolving spatiotemporal patterns of activity, also known as brain waves [1]. Indeed,wave-like patterns have been observed across multiple neuroimaging
modalities and across multiple spatiotemporal scales [2-4]. However,
due to experimental constraints most attention has thus far been
given to localised wave dynamics in the range of micrometers to afew centimeters, rather than at the global or large-scale that would
encompass the whole brain. Existing toolboxes [2,4] are geared particularly for 2D spatial domains (e.g., LFPs or VSDs on structured rectangular grids). No tool exists to study spatiotemporal waves naturally
unfolding in 3D+t as recorded with different non-invasive neuroimaging techniques (e.g, EEG, MEG, and fMRI). In this work, we present
results of using our toolbox neural flows (Fig. 1).

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Conclusions: Representation of neural activity based on singularities (commonly known as critical points) is essentially a dimensionality
reduction framework to understand large-scale brain dynamics. The distribution of singularities in physical space allows us to simplify the complex structure of flows into areas with similar dynamical behavior (e.g.,fast versus slow, stagnant, laminar, or rotating). For modelling work, thiscompact representation allows for an intuitive and systematic understanding of the effects of various parameters in brain network dynamics such as spatial heterogeneity, lesions and noise. For experimental
work, neural flows enable a rational understanding of large-scale brain
dynamics directly in anatomical space which facilitates the interpretation and comparison of results across multiple modalities. Toolbox
capabilities are presented in the accompanying figure. Watch this space
for the open-source code: https://github.com/brain-modelling-group.
References
1. Roberts JA, et al. Metastable brain waves. Nature Communications. 2019;10(1): 1-17.
2. Muller L, et al. Rotating waves during human sleep spindles organize global
patterns of activity that repeat precisely through the night. eLife. 2016; 5:e17267.
3. Contreras D, Destexhe A, Sejnowski TJ, Steriade M. Spatiotemporal patternsof spindle oscillations in cortex and thalamus. Journal of Neuroscience.
1997; 17: 1179-1196.
4. Destexhe A, Contreras D, Steriade M. Spatiotemporal analysis of local fieldpotentials and unit discharges in cat cerebral cortex during natural wakeand sleep states. Journal of Neuroscience. 1999; 19(11), 4595-4608.
5. Sanz-Leon, et al. Neuroimage toolbox 2020 [in prep].
6. Breakspear M. Dynamic models of large-scale brain activity. Nature Neuro‑science. 2018; 20(3): 340-352.
7. Townsend RG, Gong P. Detection and analysis of spatiotemporal patterns inbrain activity. PLoS computational biology. 2018; 14(12): e1006643.

O11
Experimental and computational characterization of intervalvariability in the sequential activity of the Lymnaea feeding CPG
Alicia Garrido‑Peña1, Irene ­Elices1, Rafael L­ evi1, Francisco B R­ odriguez2,
Pablo ­Varona2
1
Universidad Autonoma Madrid, Grupo de Neurocomputacion Biologica,
Madrid, Spain; 2Universidad Autónoma Madrid, Ingeniería Informática,
Madrid, Spain
Correspondence: Alicia Garrido‑Peña (alicia.garrido@uam.es)
BMC Neuroscience 2020, 21(Suppl 1):O11

Fig. 1 The toolbox has five main capabilities. In the core module:
interpolation; 1 estimation of flows; 2 detection of singularities; and,in the analysis module: 3 classification, 4 quantification and trackingof singularities; and, 5 classification of large-scale patterns with modal
decomposition
Methods and Results: Our toolbox handles irregularly sampled datasuch as those produced via brain network modelling [5,6] or sourcereconstructed M/EEG, and regularly sampled data such as voxel-based
fMRI. The toolbox performs the following steps: 1) Estimation of neural flows [5,7]. 2) Detection of 3D singularities (i.e., points of vanishingflow). 3) Classification of 3D singularities. In that regard, the key flow
singularities detected so far had been sources and sinks (from where
activity emerges and vanishes, respectively), but no methods or tools
existed to detect 3D saddles (around which activity is redirected toother parts of the brain). 4) Quantification of singularity statistics. 5)
Finally, modal decomposition of neural flow dynamics. This decomposition allows for the detection and prediction of the most common
spatiotemporal patterns of activity found in empirical data.

Central Pattern Generators (CPG) generate and coordinate motor
movements by producing rhythms composed of patterned sequencesof activations in their constituent neurons. These robust rhythms areyet flexible and the time intervals that build the neural sequences
can adapt as a function of the behavioral context. We have recently
revealed the presence of robust dynamical invariants in the form ofcycle-by-cycle linear relationships between two specific intervals of
the crustacean pyloric CPG sequence and the period [1]. Following
the same strategy, the present work characterizes the intervals thatbuild the rhythm and the associated sequence of the feeding CPGof the mollusk Lymnaea Stagnalis. The study entails both the activity obtained in electrophysiological recordings of living neurons andthe rhythm produced by a realistic conductance-based model. The
analysis reported here first assesses the quantification of the variability of the intervals and the characterization of relationships between
the intervals that build the sequence and the period, which allows
the identification of dynamical invariants. To induce variability in the
CPG model, we use current injection ramps in individual CPG neurons
following the stimulation used in experimental recordings in [2]. Our
work extends previous analyses characterizing the Lymnaea feeding
CPG rhythm from experimental recordings and from modeling studies
by considering all intervals that build the sequence [3]. We report thepresence of distinct variability in the sequence time intervals and theexistence of dynamical invariants, which depend on the neuron being
stimulated. The presence of dynamical invariants in CPG sequences,
not only in the model but also in two animal species, points out theuniversality of this phenomena.

﻿BMC Neurosci 2020, 21(Suppl 1):54

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Acknowledgements: We acknowledge support from AEI/FEDER
PGC2018-095895-B-I00 and TIN2017-84452-R.
References
1. Elices I, Levi R, Arroyo D, Rodriguez FB, Varona P. Robust dynamical invariants
in sequential neural activity. Scientific Reports. 2019; 9(1): 1-13.
2. Elliott CJ, Andrew T. Temporal analysis of snail feeding rhythms: a threephase relaxation oscillator. Journal of Experimental Biology. 1991; 157(1):391– 408.
3. Vavoulis DV, et al. Dynamic control of a central pattern generator circuit: acomputational model of the snail feeding network. European Journal of
Neuroscience. 2007; 25(9): 2805–2818.

O12
A spatial developmental generative model of human brain
structural connectivity
Stuart ­Oldham1, Ben ­Fulcher2, Kevin ­Aquino3, Aurina Arnatkevičiūtė3,
Rosita ­Shishegar3, Alex ­Fornito3
1
Monash University, Clayton, Australia; 2The University of Sydney, Schoolof Physics, Sydney, Australia; 3Monash University, The Turner Institutefor Brain and Mental Health, School of Psychological Sciences and Monash
Biomed, Clayton, Australia
Correspondence: Stuart Oldham (sjold1@student.monash.edu)
BMC Neuroscience 2020, 21(Suppl 1):O12
The human connectome has a complex topology that is thought to enable adaptive function and behaviour. Yet the mechanisms leading to theemergence of this topology are unknown. Generative models can shedlight on this question, by growing networks in silico according to specificwiring rules and comparing properties of model-generated networks
to those observed in empirical data [1]. Models involving trade-offs
between the metabolic cost and functional value of a connection can
reproduce topological features of human brain networks at a statisticallevel, but are less successful in replicating how certain properties, mostnotably hubs, are spatially embedded [2,3]. A potential reason for thislimited predictive ability is that current models assume a fixed geometry
based on the adult brain, ignoring the major changes in shape and sizethat occur early in development, when connections form.
To address this limitation, we developed a generative model that
accounts for developmental changes in brain geometry, informed
by structural MRIs obtained from a public database of foetal scansacquired from 21–38 weeks gestational age [4]. We manually segmented the cortical surface of each brain and registered each surface
to an adult template surface using Multimodal Surface Matching [5,6].
This procedure allowed us to map nodes to consistent spatial locations through development and measure how distances between
nodes (a proxy for connectome wiring cost) change through development. We evaluated the performance of classic trade-off models[2] that either assume a fixed, adult brain geometry (static), or thosewhere cost-value trade-offs dynamically change in accordance with
developmental variations in brain shape and size (growth). We usedconnectomes generated from 100 healthy adults with diffusion MRI tobenchmark model performance. Model fit was calculated by comparing model and empirical distributions of topological properties. An
optimisation procedure was used to find the optimal parameters andbest-fitting models for each individual adult brain network [2]. For faircomparison of model fit across models of varying parametric complexity, we used a leave-one out cross-validation procedure.
Spatial models (sptl; which include only distance information) produced
poorer fits than those involving distance–topology trade-offs. Homophily models (matching, neighbours; where connections form between
nodes with common neighbours) were among the best fitting. Growthmodels produced slightly better fits than static models overall. These
results still generally held when the cross-validation procedure was
employed (Fig. 1a). Neither growth nor static models reproduced thespatial topography of network hubs, but growth models are associatedwith a less centralized anatomical distribution of hubs across the brain,
which is more consistent with the empirical data (Fig. 1b).

Fig. 1 a Cross-validation results for static and growth models. Differ‑ent models are shown on the x-axis and the shading of the boxplot
indicates the model type: static models are darker while growthmodels are lighter. b Hub distribution under different models. The
size and colour show the degree of each node (blue/small indicates alow degree node, red/large indicates a high degree node)In summary, we introduce a new framework for examining how developmental changes in brain geometry influence brain connectivity. Our
results suggest that while such changes influence network topology,
they are insufficient to explain how complex connectivity patterns
emerge in brain networks.
References
1. Betzel RF, Bassett DS. Generative models for network neuroscience: pros‑
pects and promise. Journal of The Royal Society Interface. 2017; 14(136):
20170623.
2. Betzel RF, et al. Generative models of the human connectome. Neuroimage.
2016; 124: 1054-64.
3. Zhang X, et al. Generative network models identify biological mechanisms ofaltered structural brain connectivity in schizophrenia. bioRxiv. 2019; 604322.
4. Gholipour A, et al. A normative spatiotemporal MRI atlas of the fetal brain
for automatic segmentation and analysis of early brain growth. Scientific
Reports. 2017; 7: 476.
5. Robinson EC, et al. MSM: a new flexible framework for multimodal surface
matching. Neuroimage. 2014; 100: 414-426.
6. Robinson EC, et al. Multimodal surface matching with higher-order smooth‑ness constraints. Neuroimage. 2018; 167: 453-465.

O13
Cortical integration and segregation explained by harmonic modesof functional connectivity
Katharina ­Glomb1, Gustavo ­Deco2, Morten L K­ ringelbach3, Patric­Hagmann4, Joel P­ earson5, Selen A­ tasoy3
1
Centre Hospitalier Universitaire Vaudois, Department of Radiology,
Lausanne, Switzerland; 2Universitat Pompeu Fabra, Barcelona, Spain;
3
University of Oxford, Department of Psychiatry, Oxford, United Kingdom;
4
University Hospital of Lausanne and University of Lausanne, Departmentof Radiology, Lausanne, Switzerland; 5University of New South Wales,
School of Psychology, Sydney, Australia
Correspondence: Katharina Glomb (katharina.glomb@upf.edu)
BMC Neuroscience 2020, 21(Suppl 1):O13
The idea that harmonic modes - basis functions of the Laplace operator- are meaningful building blocks of brain function are gaining attention[1–3]. We extracted harmonic modes from the Human Connectome Project’s (HCP) dense functional connectivity (dFC), an average over 812 participants’ resting state fMRI dFC matrices. In this case, harmonic modes
give rise to functional harmonics. Each functional harmonic is a connectivity gradient [4] that is associated with a different spatial frequency, andthus, functional harmonics provide a frequency-ordered, multi-scale,multi-dimensional description of cortical functional organization.
We propose functional harmonics as an underlying principle of integration and segregation. Figure 1a shows 2 functional harmonics on
the cortical surface. In harmonic 11 (ψ11), the two functional regions

﻿BMC Neurosci 2020, 21(Suppl 1):54

that correspond to the two hands are on opposite ends of the gradient (different colors on the surface) and are thus functionally segregated. In contrast, in harmonic 7 (ψ7), the two areas are on the sameend of the gradient, and are thus integrated. This way, functional harmonics explain how two brain regions can be both functionally integrated and segregated, depending on the context.

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Figure 1a illustrates how specialized areas emerge from the smooth
gradients of functional harmonics: the two hand areas occupy wellseparated regions of the space spanned by ψ7 and ψ11. Thus, functional harmonics unify two perspectives, a view where the brain isorganized in discrete modules, and one in which function varies gradually [4].
The borders drawn on the cortex correspond to functional areas in the
HCP’s multimodal parcellation [5]. In this example, the isolines of thegradients of the functional harmonics follow the borders. We quantified how well, in general, the first 11 functional harmonics follow theborders of cortical areas by comparing the variability of the functional
harmonics within and between the areas given by the HCP parcellation; i.e. we computed the silhouette value (SH), averaged over all 360
cortical areas. The SH lies between 0 and 1, where 1 means perfectcorrespondence between isolines and parcels. We found average SHs
between 0.65 (ψ10) and 0.85 (ψ1), indicating a very good correspondence. Thus, functional harmonics capture the “modular perspective” ofbrain function.
On the other hand, several functional harmonics are found to capture topographic maps and thus, gradually varying function. One
important example is retinotopic organization of the visual cortex.
Figure 1b shows functional harmonic 8 (ψ8) as an example in which
both angular and eccentricity gradients are present [6]. Topographicorganization is also found in the somatosensory/motor cortex, known
as somatotopy. This is shown in Figure 1a, where several somatotopic
body areas are reproduced.
Taken together, our results show that functional specialization,
topographic maps, and the multi-scale, multi-dimensional nature offunctional networks are captured by functional harmonics, thereby
connecting these empirical observations to the general mathematicalframework of harmonic eigenmodes.
References
1. Atasoy S, Donnelly I, Pearson J. Human brain networks function in connec‑
tome-specific harmonic waves. Nature Communications. 2016; 7: 10340.
2. Robinson PA, et al. Eigenmodes of brain activity: Neural field theory predic‑
tions and comparison with experiment. Neuroimage. 2016; 142: 79-98.
3. Tewarie P, et al. How do spatially distinct frequency specific MEG networks
emerge from one underlying structural connectome? The role of the struc‑tural eigenmodes. NeuroImage. 2019; 186: 211-220.
4. Margulies DS, et al. Situating the default-mode network along a principal
gradient of macroscale cortical organization. Proceedings of the National
Academy of Sciences. 2016; 113(44): 12574-12579.
5. Glasser MF, et al. A multi-modal parcellation of human cerebral cortex.
Nature. 2016; 536(7615): 171–178.
6. Benson NC, et al. The Human Connectome Project 7 Tesla retinotopy data‑
set: Description and population receptive field analysis. Journal of Vision.
2018; 18(13): 23-23.

O14
Reconciling emergences: an information‑theoretic approach
to identify causal emergence in multivariate data
Pedro ­Mediano1, Fernando R­ osas2, Henrik J­ ensen3, Anil S­ eth4, Adam
­Barrett4, Robin Carhart‑Harris2, Daniel B­ or1
1
University of Cambridge, Department of Psychology, Cambridge, United
Kingdom; 2Imperial College London, Department of Medicine, London,
United Kingdom; 3Imperial College London, Department of Mathematics,
London, United Kingdom; 4University of Sussex, Department of Informatics,
Brighton, United Kingdom
Correspondence: Pedro Mediano (pam83@cam.ac.uk)
BMC Neuroscience 2020, 21(Suppl 1):O14

Fig. 1 a Specialized brain regions emerge from continuous gradientsin multiple dimensions. In this example, somatotopic areas arise
from functional harmonics 3 and 11. b Certain functional harmonics(here, functional harmonic 8) capture retinotopy. Vertices in V1-V4 areplotted in retinotopic space [6] in the same colors as on the flattenedsurface of the early visual cortex

The broad concept of emergence is instrumental in various key open
scientific questions – yet, few quantitative theories of what constitutes
emergent phenomena have been proposed. We introduce a formal
theory of causal emergence in multivariate systems, which studies
the relationship between the dynamics of parts of a system and macroscopic features of interest. Our theory provides a quantitative
definition of downward causation, and introduces a complementary
modality of emergent behaviour, which we refer to as causal decoupling. Moreover, we provide criteria that can be efficiently calculated

﻿BMC Neurosci 2020, 21(Suppl 1):54

in large systems, making the theory applicable in a range of practicalscenarios. We illustrate our framework in a number of case studies,including Conway’s Game of Life and ECoG data from macaques during a reaching task, which suggest that the neural representation of
motor behaviour may be causally decoupled from cortical activity.
P1
Homeostatic recognition circuits emulating network‑wide burstingand surprise
Tsvi Achler
Optimizing Mind, Palo Alto, CA, United States of America
Correspondence: Tsvi Achler (achler@gmail.com)
BMC Neuroscience 2020, 21(Suppl 1):P1
Understanding the circuits of recognition is essential to build a deeper
understanding of virtually all of the brains behaviors and circuits.
The goal of this work is to capture simultaneous findings on both theneural and behavioral levels, namely Network Wide Bursting (NWB)
dynamics with surprise (unexpected inputs), using a hypothesized recognition circuit based on the idea of homeostasis flow.
If real neural brains at a resting state are presented with an unexpected or new stimulus, the brain network shows a fast network-wide
increase in activation (NWB of many neurons) followed by a slower
inhibition, until the network settles again to a resting state. Bursting
phenomena during recognition is found ubiquitously in virtually every
type of organism, within isolated brain dissections and even neuraltissue grown in a dish (Fig. 1). Its source and function remain poorly
understood. Behavioral manifestation of surprise can be observed if
the input is much unexpected and may involve multiple brain regions.

Fig. 1 Network-Wide Bursting. Left: A 26 node Homeostatic network
trained on MNIST digit recognition data & its response to a numeral
digit 1 presented at time 0. The nodes for all digits 0-9 briefly burst
but then settle down, leaving only node 1 active. Right: example neu‑ron recordings from audio cortex & surrounding regions in response
to sound stimuli (modified from Lakatos et al. 2005)
The homeostatic flow model posits that activation from inputs is balanced with top down pre-synaptic regulatory feedback from output
neurons. Information is projected from inputs to outputs with forward connections then back to inputs with backwards homeostaticconnections which inhibits the inputs. This effectively acts to balance
the inputs & outputs (homeostasis) and generates an internal errordependent input. This homeostatic input is then projected again to
outputs and back again until output values relate recognition. Thisoccurs during recognition and no weights are learned.
When a surprise or unexpected input stimulus is presented, NWB
occurs because the homeostatic balance is disturbed with the newstimulus. The system subsequently calms down as it settles back to a
new homeostasis.

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In comparing to existing models, this circuit is different from Adaptive Resonance Theory because: 1) no lateral connections are required(inhibitory or otherwise) 2) all neurons feed backwards pre-synaptically at the same time 3) there is no vigilance parameter. It is different
from Hopfield networks because instead of top-down feedback being
positive, it is negative (inhibitory & homeostatic). This changes thefunctions and dynamics of the model making it stable: its dynamics
eventually converge to steady state as long as inputs do not change.
The homeostatic feedback should not be confused with error of learning algorithms since: 1) it is implemented during recognition 2) does
not adjust any weights at any time 3) not generated using training
data. It is different from generative and predictive coding models
because 1) it is primarily used during recognition not learning 2) thegenerative and recognition components are inseparable and contained within a single integrated homeostatic circuit.
The network is connectionist but approximates a Bayesian networkby: 1) homeostatic weights are roughly equivalent to Bayesian likelihood values 2) output values can behave as Bayesian priors if they are
maintained externally or if inputs suddenly change. Maintaining priors
changes circuit recognition and dynamics without changing weights.
Learning can be achieved with simple Hebbian learning, obtaining
weights that are similar to Bayesian likelihood. Both directions of thehomeostatic process learn the same weights. Single layer learning isdemonstrated with standard MNIST digits while capturing the neuralfindings of NWB.
P2
The monosynaptic inference problem: linking statistics
and dynamics in ground truth models
Zach ­Saccomano1, Rodrigo ­Pena2, Sam M­ ckenzie3, Horacio R­ otstein4,
Asohan ­Amarasingham5
1
City University of New York, Biology, New York, United States of America;
2
New Jersey Institute of Technology, Federated Department of Biological
Sciences, Newark, United States of America; 3New York University,
Neuroscience Institute, New York, United States of America; 4New Jersey
Institute of Technology, Federated Department of Biological Sciences,
NJIT / Rutgers University, Newark, United States of America; 5New Jersey
Institute of Technology, Biological Sciences, New York, United Statesof America
Correspondence: Zach Saccomano (zsaccomano@ccny.cuny.edu)
BMC Neuroscience 2020, 21(Suppl 1):P2
Given the ability to record spike trains from populations of neurons, anatural aim in neuroscience is to infer properties of synapses, including connectivity maps, from such recordings. These inferences canderive from observations of strong millisecond-timescale correlations
among spike train pairs, as typically reflected by a sharp, short-latency
peak in the causal direction of cross-correlograms (CCG) between
the reference and target neurons. However, such sharp peaks may
also occur when two disconnected neurons systematically fire close
together in time in the absence of a direct monosynaptic connection.
A further confound is that a monosynapse likely influences the postsynaptic cell on broader timescales as well. These observations motivate a systematic analysis of how a monosynapse exerts influence on
the intrinsic dynamics of its postsynaptic target and how this affects
the properties of the CCG and the ability to infer the monosynaptic
properties. In previous work [1], we adapted a statistical framework
for monosynaptic inference based on a (statistical) separation-of-timescale principle, in which monosynaptic interactions are systematicallyassumed to drive spike-spike correlations at finer timescales than nonmonosynaptic interactions. We examined this principle in a simplified
ground truth neuron model with minimal intrinsic dynamics, such as
the leaky integrate-and-fire (LIF) model with an adaptive threshold. In
this work, we extend these ideas to more realistic models and multiple time scales. We use a generalized LIF model with two-dimensional
subthreshold dynamics and multiple (dynamic) time scales. The model
describes the nonlinear dynamics of the voltage and a slower adaptation gating variable. These subthreshold dynamics also describe theonset of spikes, but not the spiking dynamics. Spikes are added manually. Our previous work exploited our ability to study counterfactual

﻿BMC Neurosci 2020, 21(Suppl 1):54

causal inferences in simulations. For example, in simulations with two
neurons and comodulated noise, how much would the peak of the
CCG change if the monosynapse were deleted? Here we extend thisapproach to the more complex models where the properties of the
CCG are affected by the model nonlinearities and time scales. In this
scenario, the model’s slow time scale (captured by the adaptation time
constant) affects the CCG time scales (an emergent property of themonosynaptic interaction). Finally, we assess how bias induced by theseparation-of-timescale principle (in the statistical sense) depends on
the intrinsic dynamics of postsynaptic cells, in particular on the separation of time scales in the dynamic modeling sense.
References
1. Platkiewicz J, Saccomano Z, McKenzie S, English D, Amarasingham A.
Monosynaptic inference via finely-timed spikes. arXiv. 2019; 1909.08553[preprint].

P3
Using higher‑order networks to analyze non‑linear dynamics
in neural network models
Xerxes Arsiwalla
Pompeu Fabra University, Barcelona, Spain
Correspondence: Xerxes Arsiwalla (x.d.arsiwalla@gmail.com)
BMC Neuroscience 2020, 21(Suppl 1):P3
In linear dynamical systems, one has an elegant way to analyze thesystem’s dynamics using a network representation of the state transition matrix, obtained from a state space formulation of the system
of ODEs. However, in non-linear systems, there is no state space formulation to begin with. In recent work, we have established a correspondence between non-linear dynamical systems and higher-order
networks [1] (see also [2]). The latter refer to graphs that include links
between nodes and edges, as well as links between two edges. It turns
out that such networks have a rich structure capable of representing
non-linearities in the vector field of dynamical systems. To do this, one
has to first dimensionally unfold a system of non-linear ODEs such that
non-linear terms in the vector field can be re-expressed using auxiliary
dynamical variables. This results in an unfolded dynamical system with
only polynomial non-linearities. This operation works for a large classof non-linear systems. It turns out that once we have a polynomial vector field, the system can then be expressed in generalized state spaceform. This is what ultimately admits a graphical representation of thesystem. However, the resulting graph consists of higher-order edges.
This generalizes the more common usage of networks with dyadicedges to networks with compounded edges. Here, we show an application of these graphs to analyze neural networks built from sigmoidal
rate models as well as mean-field models. Higher-order graphs enable
one to systematically decompose contributions of various non-linear
terms to the dynamics as well as analyze stability and control of thesystem using network properties.
References
1. Arsiwalla XD. A functorial correspondence between non-linear dynamicalsystems and higher-order networks. 2020 [submitted].
2. Baez JC, Pollard BS. A compositional framework for reaction networks.
Reviews in Mathematical Physics. 2017; 29(09): 1750028.

P4
Self‑organization of connectivity in spiking neural networks
with balanced excitation and inhibition
Jihoon ­Park1, Yuji ­Kawai2, Minoru ­Asada2
1
Osaka University, Suita, Japan; 2Osaka University, Institute for Openand Transdisciplinary Research Initiatives, Suita, Japan
Correspondence: Jihoon Park (jihoon.park@otri.osaka‑u.ac.jp)
BMC Neuroscience 2020, 21(Suppl 1):P4
Atypical neural activity and structural network changes have been
detected in the brains of autism spectrum disorder (ASD) [1]. It has
been hypothesized that an imbalance in the activity of excitatory andinhibitory neurons causes the pathological changes in autistic brains,denoted by the E/I balance hypothesis [2]. In this study, we investigate

Page 13 of 123

the effect of E/I balance on the self-organization of network connectivity and neural activity using a model approach. Our model follows
the Izhikevich spiking neuron model [3], and consists of three neuron
groups, each composed of 800 excitatory neurons and NI inhibitory
neurons (Fig. 1A). Each excitatory neuron had 100 intraconnections
with randomly selected neurons in the same neuron group, and 42
inter-connections with randomly selected neurons in its neighboring
neuron group. These synaptic weights were modified using the Spiketiming-dependent plasticity rule [3]. Each inhibitory neuron had 100
intraconnections with randomly selected excitatory neurons in thesame neuron group, but they did not have any interconnections nor
plasticity. We simulated the model with different and inhibitory synaptic weights (WI) in one neuron group (neuron group 1 in Fig. 1A) tochange the degree of inhibition in the neuron group. NI and WI in theother groups (2 and 3 in Fig. 1A) were set to 200 and -5, respectively.
The simulation results show greater intraconnections in all neuron
groups when NI and WI were lower values, i.e., the E/I ratio increased
compared to those in the typical E/I ratio (Fig. 1B). Moreover, asymmetric interconnections between neuron groups emerged where
the synaptic weights from neuron groups 2 to 1 were higher thanwhen the connectivity was in the opposite direction (Fig. 1C), where
the E/I ratio was found to increase. Furthermore, the phase coherence between the average potentials of neuron groups was foundto be weak with an increased E/I ratio (Fig. 1D). These results indicatethat the disruption of the E/I balance, especially the weak inhibitory,
induces excessive local connections and asymmetric intergroup connections. Therefore, the synchronization between neuron groups
decreases, i.e., there is a weak long-range functional connectivity.
These results suggest that the E/I imbalance might cause strong local
anatomical connectivity and weak long-range functional connectivity
in the brains of ASD [1].

Fig. 1 Model overview and results. A A model consisting of neuron
groups. Neuron group 1 has controlled inhibitory neurons and does
not directly connect with neuron group 3. B Average weights of intra‑
connections in each neuron group after self-organization. C Average
weights of interconnections among neuron groups after self-organi‑zation. D Phase coherence between neuron groups 1 and 3
Acknowledgements: This work was supported by JST CREST Grant
Number JPMJCR17A4 including the AIP challenge (conceptualization
and resources), and a project commissioned by the New Energy andIndustrial Technology Development Organization (NEDO; implementation and data curation).
References
1. Belmonte MK, et al. Autism and abnormal development of brain connectiv‑
ity. Journal of Neuroscience. 2004; 24: 9228–9231.
2. Nelson SB, Valakh V. Excitatory/inhibitory balance and circuit homeostasis in
autism spectrum disorders. Neuron. 2015; 87: 684–698.

﻿BMC Neurosci 2020, 21(Suppl 1):54

3. Izhikevich EM. Polychronization: computation with spikes. Neural Computa‑tion. 2006; 18: 245–282.

P5
Mathematical modeling of the circadian clock in spiders
Daniel ­Robb1, Lu ­Yang2, Nadia A­ youb2, Darrell M­ oore3, Thomas ­Jones3,
Natalia ­Toporikova2
1
Roanoke College, Department of Mathematics, Computer Scienceand Physics, Salem, Virginia, United States of America; 2Washington and Lee
University, Department of Biology, Lexington, Virginia, United Statesof America; 3East Tennessee State University, Department of Biological
Sciences, Johnson City, Tennessee, United States of America
Correspondence: Daniel Robb (robb@roanoke.edu)
BMC Neuroscience 2020, 21(Suppl 1):P5
The circadian clock in organisms produces oscillations in neural andphysiological functions with an intrinsic period on the order of the
24-hour circadian day. The circadian clock, controlled by the suprachiasmatic nucleus (SCN) in the brain, is entrained by light-dark cycles, sothat organisms synchronize these essential oscillations with external
conditions. The clock has been studied extensively in flies and yeast,
among other species, with intrinsic periods ranging from 22-26 hours[1], and a fairly limited range of entrainment to external light-darkcycles. Interestingly, our previous work has shown that spiders have asignificantly wider range of intrinsic periods, from 19-30 hours, and an
ability to entrain to a much wider range of applied external light-darkcycles. To identify a potential mechanism for the unusual circadianclock in spiders, we have developed a mathematical model for the
spider circadian clock which incorporates negative feedback. We haveused the model to investigate two possible mechanisms for the wide
range of entrainment in spiders. First, light could be a ‘strong stimulus’
which acts powerfully on a circadian clock of typical strength. Second,the circadian clock in spiders could be ‘weak’, i.e., not as robust to perturbations, relative to that of other species. To distinguish between
these two mechanisms, a bifurcation analysis of the model has been
performed, as shown in the figure (Fig. 1). Our model makes several
testable predictions. In the ‘strong stimulus’ scenario, we predict a
faster onset of locomotor activity for spiders with shorter intrinsic circadian periods with a change in the applied light cycle, and a slower
onset of locomotor activity for spiders with longer intrinsic periods.
In addition, the ‘strong stimulus’ scenario could often lead to a lack oflocomotor activity in constant light. In contrast, the ‘weak clock’ scenario predicts a strong dependence of entrainment on the light intensity, and that, once achieved, entrainment would lead to an increase in
the amplitude of model oscillations. The balance of our computationalmodeling and experimental results currently favors the weak clock
scenario.

Fig. 1 Bifurcation diagram of circadian clock model identifying weak,medium, and strong regimes
References
1. Johnson CH, Elliott J, Foster R, Honma K-I, Kronauer R. Fundamental
properties of circadian rhythms. In: Chronobiology: Biological Timekeeping.
Sinauer. 2004; 67-105.

Page 14 of 123

P6
Inference of functional connectivity in living neural networks
Sarah ­Marzen1, Martina L­ amberti2, Michael H­ ess3, Jacob ­Mehlman4,
Denise ­Hernandez4, Joost le ­Feber2
1
Pitzer, Scripps, and Claremont McKenna College, Physics, Claremont,
California, United States of America; 2University of Twente, Clinical
Neurophysiology, Twente, Netherlands; 3Claremont McKenna College,
Computational Neuroscience, Claremont, California, United Statesof America; 4Claremont McKenna College, Physics, Claremont, California,
United States of America
Correspondence: Sarah Marzen (smarzen@cmc.edu)
BMC Neuroscience 2020, 21(Suppl 1):P6
In experiments with stimuli, we often wish to assess changes in connectivity between neurons as the experiment progresses. There are anumber of methods for assessing connectivity with a variety of drawbacks, but it is not clear that these methods are connected to one
another. Furthermore, it is not clear that these functional connectivities are connected to real synaptic connectivities. We present someevidence that functional connectivities from two disparate methods(Conditional Firing Probability analysis and Maximum Entropy analysis) and synaptic connectivities in one dynamical model (that of leaky
integrate-and-fire neurons) are all related.

Fig. 1 The predicted Ji,j + Jj,I is compared to the true Ji,j + Jj,I fora recording of spontaneous activity from a neuronal culture. Only
excitatory connections are included. The analytic expression requiresthat Δt be sufficiently small, and here Δt is 3 ms—not small enough.
Even so, we see a strong relationship between predictions and meas‑
urements with R = 0.5
P7Functional dissection of prefrontal networks using optogenetics
and large‑scale multi‑electrode recordings
Eloise V ­Giraud1, Jean‑Philippe ­Thivierge1, Michael L­ ynn2, Jean‑Claude­Beique2
1
University of Ottawa, School of Psychology, Ottawa, Canada; 2Universityof Ottawa, Cellular and Molecular Medicine, Ottawa, Canada
Correspondence: Eloise V Giraud (egira024@uottawa.ca)
BMC Neuroscience 2020, 21(Suppl 1):P7
The prefrontal cortex (PFC) plays an important role in executive functions that guide reward-seeking, goal-directed and memory-guided
behaviours. However, the contribution of specific cell types to theactivity of broader cortical circuits remains largely unknown. This isimportant to inform accurate computational models of prefrontal
cortex function. Here, we used high-density multielectrode arrays

﻿BMC Neurosci 2020, 21(Suppl 1):54

Page 15 of 123

containing 4,096 closely spaced electrodes to monitor the spiking
activity of PFC neurons in acute slice preparations. We developed
spike sorting techniques that combined spline interpolation andprincipal component analysis to distinguish regular-spiking excitatory neurons from fast-spiking inhibitory interneurons. Our sorting
algorithm was validated using a targeted combination of viral andoptogenetic strategies. By cell-type-specific optogenetic stimulation,
we described how parvalbumin interneurons regulate the interplay
between excitation and inhibition. Specifically, we characterized theinfluence of parvalbumin interneurons on network-wide firing rates
and distance-dependent pairwise correlations within the PFC. These
results form a key target for computational models that aim to capture
the interactions between excitation and inhibition in cortical areas.
P8
Average beta burst duration profiles provide a signatureof dynamical changes between the ON and OFF medication states
in Parkinson’s disease
Benoit ­Duchet1,2, Filippo ­Ghezzi3, Gihan ­Weerasinghe1,2, Gerd ­Tinkhauser4,
Andrea A ­Kuhn5, Peter B­ rown1,2, Christian B­ ick6,7,8, Rafal ­Bogacz1,2
1
University of Oxford, Nuffield Department of Clinical Neuroscience,
Oxford, United Kingdom; 2University of Oxford, MRC Brain Network
Dynamics Unit, Oxford, United Kingdom; 3University of Oxford, Departmentof Physiology, Anatomy, and Genetics, Oxford, United Kingdom; 4Bern
University Hospital and University of Bern, Department of Neurology,
Bern, Switzerland; 5Charité – Universitätsmedizin Berlin, Departmentof Neurology, Movement Disorder and Neuromodulation Unit, Berlin,
Germany; 6University of Oxford, Oxford Centre for Industrial and Applied
Mathematics, Mathematical Institute, Oxford, United Kingdom; 7Universityof Exeter, Centre for Systems, Dynamics, and Control and Departmentof Mathematics, Exeter, United Kingdom; 8University of Exeter, EPSRC
Centre for Predictive Modelling in Healthcare, Exeter, United Kingdom
Correspondence: Benoit Duchet (benoit.duchet@ndcn.ox.ac.uk)
BMC Neuroscience 2020, 21(Suppl 1):P8
Parkinson’s disease motor symptoms are associated with an increasein subthalamic nucleus beta band oscillatory power. However, theseoscillations are phasic, and a growing body of evidence suggests thatbeta burst duration may be of critical importance to motor symptoms,
making insights into the dynamics of beta bursting generation valuable. In this study, we ask the question “Can average burst duration
reveal how dynamics change between the ON and OFF medication
states?”. Our analysis of local field potentials from the subthalamic
nucleus demonstrates using linear surrogates that the system generating beta oscillations acts in a more non-linear regime OFF medications and that the change in the degree of non-linearity is correlatedwith motor impairment. We further narrow-down dynamical changes
responsible for changes in temporal patterning of beta oscillations
between medication states by fitting to data biologically inspired
models, and simpler models of the beta envelope (Fig 1). Finally, weshow that the non-linearity can be directly extracted from averageburst duration profiles under the assumption of constant noise inenvelope models, revealing that average burst duration profiles provide a window into burst dynamics, which may underlie the successof burst duration as a biomarker. In summary, we have demonstrated
a relationship between average burst duration profiles, dynamics ofthe system generating beta oscillations, and motor impairment, whichputs us in a better position to understand the pathology and improvetherapies.

Fig. 1 A, B Average burst duration profiles are obtained by comput‑
ing beta envelope average burst duration for a range of thresholds.
Considering envelope models of the form dXt=- µ(Xt)dt + ζdWt,
where µ is the drift function, W a Wiener process, and ζ a constantnoise parameter, we illustrate with two examples the link between
envelope dynamics (C1, C2) and average burst duration profiles (E1,E2)

P9Introducing EBRAINS: European infrastructure for brain research
Wouter ­Klijn1, Sandra ­Diaz1, Sebastian ­Spreizer2, Thomas ­Lippert3, Spiros­Athanasiou4, Yannis ­Ioannidis5, Abigail M­ orrison6, Evdokia ­Mailli4, Katrin­Amunts7, Jan ­Bjaalie8
1
Forschungszentrum Jülich, Jülich Supercomputing Centre, Simulation
Lab Neuroscience,, Jülich, Germany; 2University of Freiburg, Bernstein
Center Freiburg, Freiburg, Germany; 3Forschungszentrum Jülich, Institutefor Advanced Simulation, Jülich Supercomputing Centre, Jülich, Germany;
4
Athena Research & Innovation Center, Marousi, Greece; 5National &Kapodistrian University of Athens, Informatics & Telecom, Athens, Greece;
6
Forschungszentrum Jülich, Institute of Neuroscience and Medicine(INM‑6), Jülich, Germany; 7Forschungszentrum Jülich, Instituteof Neuroscience and Medicine (INM 1), Jülich, Germany; 8Universityof Oslo, Institute of Basic Medical Sciences, Oslo, Norway
Correspondence: Wouter Klijn (w.klijn@fz‑juelich.de)
BMC Neuroscience 2020, 21(Suppl 1):P9

﻿BMC Neurosci 2020, 21(Suppl 1):54

The Human Brain Project (HBP), the ICT-based Flagship project of
the EU, is developing EBRAINS - a research infrastructure providing
tools and services which can be used to address challenges in brain
research and brain-inspired technology development. EBRAINS willallow the creation of the necessary synergy between different national
efforts to address one of the most challenging targets of research. This
presentation will illustrate the services of the EBRAINS infrastructurewith three use cases spanning the immensely diverse neurosciencefield.
The first case is about Viktoria, a researcher who received a grant toinvestigate the distribution of interneuron types in the cortex andtheir activity under specific conditions. She needs a place to store,
publish and share the data collected to add to the body of knowledge
on the human brain. She contacts the HBP service desk and her case isforwarded to the data curation team, a part of the EBRAINS High Level
Support Team. The data curators provide data management supportand help make her data FAIR by registering it in the Knowledge Graph[1] and the Brain Atlas [2]. Her data is stored for 10 years, given a DOI
to allow citations, and can be used by tools integrated in EBRAINS.
The second case is about Johanna, who has developed a software
package for the analysis of iEEG data and now wants this tool to be
used by as many researchers as possible. She contacts the HBP service
desk and is put in contact with the EBRAINS technical coordination
team. A co-design process is started together with the co-simulation
framework developers, and her software is integrated into the simulation and analysis framework. After integration, Johanna’s tool can
now be used with experimental data as well as to simulated iEEG data.
Her tool is integrated into the operations framework of EBRAINS and iseasily deployed on the HPC resources available through EBRAINS.
The third use case is about Jim, a neuroscience professor with a strong
focus on teaching. After learning about the HBP he explores the
EBRAINS website and discovers the wide range of educational tools
available. NEST Desktop (https://github.com/babsey/nest-desktop),
for instance, is a web accessible interface for spiking neuron networks. It allows the creation of a complete simulation with less than
10 mouse clicks, without the need to install any software. The outputof the simulation can be then ported to Jupyter notebooks hosted on
the EBRAINS’ systems to perform additional analysis. The functionality is accompanied with online MOOCs and detailed documentation to provide him with enough material to fill multiple courses onneuroscience.
With the EBRAINS infrastructure the HBP is delivering a set of tools andservices in support of all aspects of neuroscience research. Get more
information at: www.ebrains.eu or email the service desk at: support@
ebrains.eu
Acknowledgments: This research has received funding from theEuropean Union’s Horizon 2020 Framework Programme for Researchand Innovation under the Specific Grant Agreement No. 785907(Human Brain Project SGA2).
References
1. Amunts K, et al. The Human Brain Project—Synergy between neuroscience,computing, informatics, and brain-inspired technologies. PLoS biology.
2019; 17(7): e3000344.
2. Bjerke IE, et al. Data integration through brain atlasing: Human Brain Project
tools and strategies. European Psychiatry. 2018; 50: 70–76.

P10
Using adaptive exponential integrate‑and‑fire neurons to study
general principles of patho‑topology of cerebellar networks
Maurizio De ­Pitta1, Jose A O R­ odrigues2, Juan ­Sustacha3, Giulio ­Bonifazi1,
Alicia Nieto‑Reyes2, Sivan K­ anner4, Miri ­Goldin3, Ari B­ arzilai5, Paolo­Bonifazi3
1
Basque Center for Applied Mathematics, Bilbao, Spain; 2Universityof Cantabria, Department of Mathematics, Statistics and Computer
Science, Santander, Spain; 3BioCruces Health Research Institute, Barakaldo,
Spain; 4Friedrich Miescher Institute for Biomedical Research, Basel,
Switzerland; 5Tel Aviv University, Department of Life Sciences, Ramat Aviv,
IsraelCorrespondence: Maurizio De Pitta (maurizio.depitta@gmail.com)
BMC Neuroscience 2020, 21(Suppl 1):P10

Page 16 of 123(A-T) is an example of a systemic genetic disease impacting the cerebellar circuit’s structure and function. Kanner et al. [1] have shownhow the A-T phenotype in mice correlates with severe glial atrophy
and increased synaptic markers, resulting in altered cerebellar networks’ dynamics. In particular, experiments in cerebellar cultures
showed a disruption of networks’ synchronizations, which were recovered by replacements of A-T glial cells with healthy ones. Notably, theonly presence of healthy astrocyte was sufficient to restore the physiological synaptic puncta level between mutated neurons. In the intact
cerebellar circuits, glial morphological alterations and an increase ininhibitory synaptic connectivity markers were first reported and correlated (preliminary unpublished results) with an increase in the complex spiking of the Purkinje cells (PCs). In order to understand andmodel these structural-functional circuits’ alterations, we developed
a simplified model of the cerebellar circuit. To this aim, we adopt theadaptive Exponential Integrate-and-Fire (aEIF) neuron model in different parameter configurations, to capture essential functional featuresof four different cell types: granule cells and excitatory neurons of theinferior olive (IONs); Purkinje cells and inhibitory neurons of the Deep
Cerebellar Nuclei (DPNNs). Next, we explore different degrees of connectivity and synaptic weights, the dynamics of the simplified cerebellar circuitry. Our simulations suggest that the concomitant increased
number of inhibitory connections from PC to DPNNs, and from DPNNs
to IONs, ultimately results in a disinhibited IONs dynamics. As a consequence, IONs provide a higher rate of excitation to PCs within the cerebellar loop, which finally leads to higher complex spiking frequency
in PCs. These results provide new insights into the dysfunctional A-T
cerebellar dynamics and open a new perspective for targeted pharmacological treatments.
Acknowledgements: We thank the ‘Junior Leader’ Fellowship Program by ‘la Caixa’ Banking Foundation (Grant LCF/BQ/LI18/11630006).
Reference
1. Kanner S, et al. Astrocytes restore connectivity and synchronization in
dysfunctional cerebellar networks. Proceedings of the National Academy of
Sciences. 2018; 115(31): 8025-8030.

P11
Effect of interglomerular inhibitory networks on olfactory bulb odor
representations
Daniel ­Zavitz1, Isaac ­Youngstrom2, Matt ­Wachowiak2, Alla B­ orisyuk1
1
University of Utah, Department of Mathematics, Salt Lake City, UnitedStates of America; 2University of Utah, Department of Neurobiology
and Anatomy, Salt Lake City, United States of America
Correspondence: Daniel Zavitz (zavitz@math.utah.edu)
BMC Neuroscience 2020, 21(Suppl 1):P11
Lateral inhibition is a fundamental feature of circuits that process sensory information. In the mouse olfactory system, inhibitory interneurons called short axon cells initially mediate lateral inhibition between
glomeruli, the functional units of early olfactory coding and processing. However, their interglomerular connectivity and its impact onodor representations is not well understood. To explore this question,
we constructed a computational model of the interglomerular inhibitory network using detailed characterizations of short axon cell morphologies and simplified intraglomerular circuitry. We then examined
how this network transformed glomerular patterns of odorant-evokedsensory input (taken from previously-published datasets) at different
values of interglomerular inhibition selectivity. We examined threeconnectivity schemes: selective (each glomerulusconnects to few others with heterogeneous strength), nonselective (glomeruli connect tomost others with heterogeneous strength) and or global (glomeruli
connect to all others with equal strength). We found that both selective and nonselective interglomerular networks could mediate heterogeneous patterns of inhibition across glomeruli when driven byrealistic sensory input patterns, but that global inhibitory networks
were unable to produce input-output transformations that matchedexperimental data.
We further studied networks whose interglomerular connectivity was
tuned by sensory input profile. We found that this network construction improved contrast enhancement as measured by decorrelation of﻿BMC Neurosci 2020, 21(Suppl 1):54

odor representations. These results suggest that, despite their multiglomerular innervation patterns, short axon cells are capable of mediating odorant-specific patterns of inhibition between glomeruli that
could, theoretically, be tuned by experience or evolution to optimize
discrimination of particular odorants.
P12
A population level model of movement disorder oscillations
and deep brain stimulation
Nada ­Yousif1, Peter B­ ain2, Dipankar ­Nandi3, Roman ­Borisyuk4
1
University of Hertfordshire, Hatfield, United Kingdom; 2Imperial College
London, Division of Brain Sciences, London, United Kingdom; 3Imperial
College Healthcare NHS Trust, Neurosurgery, London, United Kingdom;
4
University of Exeter, College of Engineering, Mathematics and PhysicalSciences, Exeter, United Kingdom
Correspondence: Nada Yousif (n.yousif@herts.ac.uk)
BMC Neuroscience 2020, 21(Suppl 1):P12
The presence of neural oscillations is thought to be a hallmark of twoof the most common movement disorders, Parkinson’s disease (PD)and Essential tremor (ET). The symptoms of PD are tremor, slownessof movement and stiffness and is caused by the loss of dopaminergic
neurons in the substantia nigra. Although the pathological changes
in the basal ganglia network are not yet fully understood, it is widely
accepted that beta-band (15-30 Hz) oscillations play a role. Essential
tremor (ET) affects up to one percent of adults over 40 years of age
and is characterized by an uncontrollable shaking of the affected bodypart [1-3]. The neurophysiological basis of ET remains unknown, but
pathological neural oscillations in the thalamocortical-cerebellar network are also implicated in generating symptoms.
In our previous network study of PD, we studied how a multi-channel
model of Wilson-Cowan oscillators representing the STn-GPe behaved
in healthy and Parkinsonian conditions. We found that oscillations
exist for a much wider range of parameters in the Parkinsonian caseand demonstrated how an input representing DBS caused the oscillations to become chaotic and flattened the power spectrum. Looking at
ET, we again used a mean-field approach combined with intraoperative local field potential recordings from the Vim via DBS electrodes,
and simultaneous electromyographic activity from the contralateralaffected limb(s). We used the Wilson-Cowan approach to model thethalamocortical-cerebellar network implicated in ET. We found thatthe network exhibited oscillatory behaviour within the tremor frequency range of 4-5 Hz, as did our electrophysiological data. Applying
a DBS-like input to the modelled network had the effect of suppressing these oscillations. Our two previous studies therefore show thatthe dynamics of the cerebellar-basal ganglia thalamocortical network
support oscillations at frequency ranges relevant to movement disorders. The application of a DBS-like input into the modelled networks
disrupts such pathological activity. We believe that this is an important way to study the impact of DBS on the human brain and should
be used in conjunction with experimental recordings of neural activity
as well as with single neuron biophysical modelling work.
In this work we present new results from a combined model which
exhibits Parkinsonian oscillations in the beta band, oscillations in thetremor frequency range, as well as oscillations in the gamma band
which we term healthy [4,5]. We find critical boundaries in the parameter space of the model separating regions with different dynamics. We
go on to examine the transition from one oscillatory regime to anotherbehavior and the impact of DBS on these two types of pathologicalactivity. This approach will not only allow us to better understand themechanisms of DBS, but allow us to optimize the lengthy and difficult
clinical process of parameter setting via trial and error, upon which
the cited improvement in symptoms is reliant [6,7]. Furthermore, withthe advent of electrodes with more contacts this process is becoming
increasingly difficult. Hence, the need for a theoretical understandingof DBS is particularly important at present.

Page 17 of 123

References
1. Louis ED, Ottman R, Allen Hauser W. How common is the most common
adult movement disorder? Estimates of the prevalence of essential tremor
throughout the world. Movement Disorders. 1998; 13(1): 5-10.
2. Brin MF, Koller W. Epidemiology and genetics of essential tremor. Move‑ment Disorders. 1998; 13(S3): 55-63.
3. Deuschl G, Bain P, Brin M, Ad Hoc Scientific Committee. Consensus state‑ment of the movement disorder society on tremor. Movement Disorders.
1998; 13(S3): 2-23.
4. Beudel M, Brown P. Adaptive deep brain stimulation in Parkinson’s dis‑ease. Parkinsonism & related disorders. 2016; 22: S123-S126.
5. Fischer P, et al. Subthalamic nucleus gamma activity increases not only dur‑
ing movement but also during movement inhibition. Elife. 2017; 6: e23947.
6. Rizzone M, et al. Deep brain stimulation of the subthalamic nucleus in
Parkinson’s disease: effects of variation in stimulation parameters. Journal of
Neurology, Neurosurgery & Psychiatry. 2001; 71(2): 215-219.
7. Moro E, et al. The impact on Parkinson’s disease of electrical parameter set‑tings in STN stimulation. Neurology. 2002; 59(5): 706-713.

P13
Organization of connectivity between areas in the monkey
frontoparietal network
Bryan ­Conklin1, Steve ­Bressler2
1
Florida Atlantic University, Center for Complex Systems & Brain Science,West Palm Beach, Florida, United States of America; 2Florida Atlantic
University, West Palm Beach, Florida, United States of America
Correspondence: Bryan Conklin (bconkli4@fau.edu)
BMC Neuroscience 2020, 21(Suppl 1):P13
Anatomical projections between cortical areas are known to condition
the set of observable functional activity in a neural network. The largescale cortical monkey frontoparietal network (FPN) has been shownto support complex cognitive functions. However, the organizationof anatomical connectivity between areas in the FPN supporting such
behavior is unknown. To identify the connections in this network, over
40 tract-tracing studies were collated according to the Petrides & Pandya [1] parcellation scheme, which provides a higher resolution map
for the areas making up the FPN than other schemes. To understandhow this structural profile can give rise to cognitive functions, a graphtheoretic investigation was conducted in which the FPN’s degree distribution, structural motifs and small-worldness were analyzed. We
present a new connectivity matrix detailing the anatomical connections between all frontal and parietal areas of the parcellation scheme.
First, this matrix was found to have in and out-degree distributions
that did not follow a power-law. Instead they were each best approximated by a Gaussian distribution, signifying that the connectivity ofeach area in the FPN is relatively similar and that it does not rely on
hubs. Second, the dynamical relay motif, M9, was found to be overrepresented in the FPN. This 3-node motif is the optimal arrangement
for near-zero and non-zero phase synchrony to propagate throughthe network. Finally, the FPN was found to utilize a small-world architecture. This allows for simultaneous integration and specialization offunction. Important aspects of cognition such as attention and working memory have been shown to require both integration and specialization in order to function properly using near-zero and non-zerophase synchrony. Further, they benefit from the reliability affordedby the FPN’s homogenous connectivity profile which acts as a substrate resilient to targeted structural insult but vulnerable to a random
attack. This suggests the diseases that impair cognitive function supported by the FPN may owe their effectiveness to a random attack
strategy. These findings provide a candidate topological mechanism
for the synchrony observed during complex cognitive functions in theM9 dynamical relay motif. The results also serve as a benchmark to be
used in the network-level treatment of neurological disorders such as﻿BMC Neurosci 2020, 21(Suppl 1):54

Alzheimer’s or Parkinson’s disease where the types of cognition the
FPN supports are impaired. Finally, they can inform future neuromorphic circuit designs which aim to perform certain aspects of cognition.
References
1. Petrides M, Pandya DN. Efferent Association Pathways from the Rostral
Prefrontal Cortex in the Macaque Monkey. Journal of Neuroscience. 2007;
27: 11573–11586.

P14
Avalanches, criticality and correlations in self‑organised nanoscale
networks
Josh ­Mallinson1, Shota ­Shirai1, Susant A­ charya1, Saurabh B­ ose1, Matthew­Pike1, Edoardo ­Galli1, Matthew ­Arnold2, Simon ­Brown1
1
University of Canterbury, School of Physical and Chemical Sciences,
Christchurch, New Zealand; 2University of Technology Sydney, Schoolof Mathematical and Physical Sciences, Sydney, Australia
Correspondence: Josh Mallinson (josh.mallinson@pg.canterbury.ac.nz)
BMC Neuroscience 2020, 21(Suppl 1):P14
Neuronal avalanches are one of the key characteristic features of signal propagation in the brain [1]. These avalanches originate from thecomplexity of the network of neurons and synapses, which are widelybelieved to form a self-organised critical system. Criticality is hypothesised to be intimately linked to the brain’s computational power [2,3]but efforts to achieve neuromorphic computation have so far focusedon highly organised architectures, such as integrated circuits [4] andregular arrays of memristors [5]. To date, little attention has been
given to developing complex network architectures that exhibit criticality and thereby maximise [6] computational performance. We show
here, using methods developed by the neuroscience community [7],that electrical signals from self-organised percolating networks ofnanoparticles [8] exhibit brain-like correlations and criticality [9]. Specifically, the sizes and durations of avalanches of switching events are
power-law distributed, and the power-law exponents satisfy rigorous
criteria for criticality. Additionally we show that both the networks andtheir dynamics are scale-free. These networks provide a low-cost platform for computational approaches that rely on spatiotemporal correlations, such as reservoir computing, and are a significant step towards
creating neuromorphic device architectures.
References
1. Beggs JM, Plenz D. Neuronal avalanches in neocortical circuits. Journal of
Neuroscience Research. 2003; 23: 11167-11177.
2. Munoz MA. Colloquium: criticality and dynamical scaling in living systems.
Reviews of Modern Physics. 2018; 90(3): 031001.
3. Cocchi L, Gollo LL, Zalesky A, Breakspear M. Criticality in the brain: a synthe‑
sis of neurobiology, models and cognition. Progress in Neurobiology. 2017;
158: 132-152.
4. Merolla PA, et al. A million spiking-neuron integrated circuit with a scalable
communication network and interface. Science. 2014; 345(6197): 668-673.
5. Burr GW, et al. Neuromorphic computing using non-volatile memory.
Advances in Physics: X. 2017; 2(1), 89-124.
6. Srinivasa N, Stepp ND, Cruz-Albrecht J. Criticality as a set-point for adaptive
behavior in neuromorphic hardware. Frontiers in Neuroscience. 2015; 9:449.
7. Friedman N, et al. Universal critical dynamics in high resolution neuronalavalanche data. Physical Review Letters. 2012; 108(20): 208102.
8. Sattar A, Fostner S, Brown SA. Quantized conductance and switching in per‑colating nanoparticle films. Physical Review Letters. 2013; 111(13): 136808.
9. Mallinson JB, et al. Avalanches and criticality in self-organised nanoscale
networks. Science Advances. 2019; 5(11): eaaw8438.

P15
Learning sequences of correlated patterns in recurrent networks
Subhadra ­Mokashe1, Nicolas B­ runel21Duke University, Neurobiology, Durham, North Carolina, United Statesof America; 2Duke University, Neurobiology and Physics, Durham, North
Carolina, United States of America
Correspondence: Subhadra Mokashe (subhadramokashe@gmail.com)
BMC Neuroscience 2020, 21(Suppl 1):P15

Page 18 of 123

What determines the format of memory representations in cortical net‑
works is a subject of active research. During memory tasks, the retrievalof stored memories is characterized either by the persistent elevation in
the firing rate of a set of neurons (‘persistent activity’) [1] or by orderedtransient activation of different sets of neurons (‘sequential activity’) [2].
Multiple theoretical studies have shown that temporally symmetric Heb‑
bian learning rules give rise to fixed point attractor representation ofmemory (e.g., [3] and references therein), while temporally asymmetriclearning rules lead to a dynamic sequential representation of memories(e.g., [4] and references therein). These studies assume that inputs to the
network during learning have no temporal correlations.

The sensory information received by brain networks is likely to be temporally correlated. We study temporally asymmetric Hebbian learning
rules in a recurrent network of rate-based neurons in the presence oftemporal correlations in the inputs and characterize how the inputs
shape the network dynamics and memory representation using both
numerical simulations and mean-field analysis. We show that the
network dynamics depend on the temporal correlations in the input
stream the network receives. For inputs with short correlation timescale, the network exhibits sequential activity (Fig. 1 A left), while forlonger correlations within the stream of input, the network settles
into a fixed point attractor during retrieval (Fig. 1A right). At intermediate value of correlations, the network partially traverses the input
sequence before settling into an attractor state (Fig. 1 A middle). We
find that correlations increase the sequential memory capacity of the
network. Non-linear learning rules increase the range of timescale ofcorrelation for which the networks represent the memories as sequential activity in the network (Fig. 1 B). We also show that the network
maintains a sequential representation, both in the case of sequencesof discrete patterns and in the continuum limit (Fig. 1 C). Our work
thus suggests that the correlation time scales of inputs at the time oflearning have a strong influence on the nature of network dynamics
during retrieval.

Fig. 1 A The activity of neurons (top) and overlaps with stored pat‑terns from the simulations and mean-field theory (bottom), for short,intermediate, and long correlation timescale (L to R). Probability ofsequence retrieval with a linear learning and non-linear learning rule
as a function of correlation B and as a function of tau_OU, for differ‑
ent discretizations of a continuous OU process C
References
1. Funahashi S, Bruce CJ, Goldman-Rakic PS. Mnemonic coding of visual spacein the monkey’s dorsolateral prefrontal cortex. Journal of Neurophysiology.
1989; 61(2): 331–349.
2. Harvey CD, Coen P, Tank DW. Choice-specific sequences in parietal cortex
during a virtual-navigation decision task. Nature. 2012; 484(7392): 62-68.
3. Pereira U, Brunel N. Unsupervised learning of persistent and sequential
activity. Frontiers in Computational Neuroscience. 2020;13: 97.
4. Gillett M, Pereira U, Brunel N. Characteristics of sequential activity in net‑
works with temporally asymmetric Hebbian learning. BioRxiv. 2019; 818773.

﻿BMC Neurosci 2020, 21(Suppl 1):54

P16
A computational model for time cell‑based learning of interval
timingSorinel ­Oprisan1, Tristan ­Aft1, Michael ­Cox2, Mona B­ uhusi3, Catalin B­ uhusi3
1
College of Charleston, Department of Physics and Astronomy, Charleston,South Carolina, United States of America; 2Clemson University, MechanicalEngineering, Clemson, South Carolina, United States of America; 3Utah
State University, Psychology, Logan, Utah, United States of America
Correspondence: Sorinel Oprisan (oprisans@cofc.edu)
BMC Neuroscience 2020, 21(Suppl 1):P16
Lesion and pharmacological studies found that interval timing is the
emergent property of an extensive neural network that includes theprefrontal cortex (PFC), the basal ganglia (BG), and the hippocampus(HIP). We used our Striatal Beat Frequency (SBF) model with a large
number of PFC oscillators to produce beats from the coincidence
detection performed by BG [1,2]. The response of the PFC-BG neuralnetwork provides an output that (1) accurately identifies the criterion
time, i.e., the time at which the reinforcement was presented duringreinforced trails, and (2) is scalar, i.e., the prediction error is proportional to the criterion time. We found that, although the PFC-BG cancreate beats, the accuracy of the timing depends on the number of
PRC oscillators and the frequency range they cover [3,4].
The ability to discriminate between multiple durations requires a
metric space in which durations can be compared. We hypothesizedthat time cells, which were recently discovered in the hippocampus
and ramp-up their firing when the subject is at a specific temporalmarker in a behavioral test, can offer a time base for interval timing.
We expanded the SBF model by incorporating the HIP time cells that(1) provide a natural time base, and (2) could be the cellular root of
the scalar property of interval timing observed in all behavioral experiments (see [5]). Our model of interval timing learning assumes that
there are two stages of this process. First, during the reinforced trials,
the subject learns the boundaries of the temporal duration. This process is similar to the HIP space cell activity that first forms an accurate
spatial map of the edges of the environment. Subsequently, the time
cells are recruited to cover the entire to-be-timed duration uniformly.
Without any learning rule, i.e., without any feedback from the PFC-BG
network, the population of time cells simply produces a uniform average time field. In our computational model, the learning rule requires
the HIP time cell to adjust their activity to mirror the output of the
PFC-BG network. A plausible mechanism for the modulation of HIPtime cell activity could involve dopamine released during the reinforced trials. We tested numerically different learning rules and foundthat one of the most efficient in terms of the number of trails required
until convergence is the diffusion-like, or nearest-neighbor, algorithm.
References
1. Oprisan SA, Aft T, Buhusi M, Buhusi C. Scalar timing in memory: A temporal
map in the hippocampus. Journal of Theoretical Biology. 2018; 438: 133– 142.
2. Oprisan SA, Buhusi M, Buhusi CV. A population-based model of the tempo‑
ral memory in the hippocampus. Frontiers in Neuroscience. 2018; 12: 521.
3. Buhusi CV, Oprisan SA, Buhusi M. Clocks within Clocks: timing by Coinci‑
dence Detection. Current Opinion in Behavioral Sciences. 2016; 8: 207-213.
4. Buhusi CV, Reyes MB, Gathers CA, Oprisan SA, Buhusi M. Inactivation of theMedial-Prefrontal Cortex Impairs Interval Timing Precision, but Not Timing
Accuracy or Scalar Timing in a Peak-Interval Procedure in Rats. Frontiers in
Integrative Neuroscience. 2018; 12: 20.
5. Oprisan SA, Buhusi CV. What is all the noise about in interval timing?
Philosophical Transactions of the Royal Society B: Biological Sciences. 2014;
369(1637): 20120459.

Page 19 of 123

P17
The impacts of the connectome on coupled networksof wilson‑cowan models with homeostatic plasticity
Wilten ­Nicola1, Sue A ­Campbell2
1
University of Calgary, Calgary, Canada; 2University of Waterloo, Applied
Mathematics, Waterloo, Canada
Correspondence: Wilten Nicola (wilten.nicola@ucalgary.ca)
BMC Neuroscience 2020, 21(Suppl 1):P17
We study large networks of Wilson-Cowan neural field systems withhomeostatic plasticity. These networks have been known to display
rich dynamical states such consisting of a single recurrently coupled,or two cross-coupled nodes [1]. These dynamics include chaos, mixed
mode oscillations and chaos, and synchronized chaos, even underthese simple connectivity profiles in small networks. Here, we consider
these networks with connectomes that display so-called L1 normalization but are otherwise arbitrary under large network limits. We findthat for the majority of classical connectomes considered (Random,
Small World), the network displays a large-scale chaotic synchronization to the attractor states and bifurcation sequence of a single recurrently coupled node as in [1]. However, connectomes that display
sufficiently large pairs of eigenvalues can trigger multiple Hopf bifurcations which can potentially collide in Torus bifurcations that candestabilize the synchronized, single node attractor solutions. Our analysis demonstrates that for Wilson-Cowan systems with homeostatic
plasticity, the dominant determinant of network activity is not theconnectome directly, but rather the connectome’s ability to generatelarge eigenvalues that can induce multiple nearby Hopf bifurcations. If
the connectome cannot generate these large pairs of eigenvalues, thedynamics of the network considered become limited to the dynamics
of a single recurrently coupled node.
Reference
1. Nicola W, Hellyer PJ, Campbell SA, Clopath C. Chaos in homeostaticallyregulated neural systems. Chaos: An Interdisciplinary Journal of Nonlinear
Science. 2018; 28(8): 083104.

P18
Inhibitory gating in the dentate gyrus
Claudio ­Mirasso1, Cristian E­ starellas1, Santiago C­ anals2
1
Universitat de les Illes Balears, Instituto de Física Interdisciplinar y Sistemas
Complejos, Palma de Mallorca, Spain; 2Instituto de Neurociencias, San Juande Alicante, Spain
Correspondence: Claudio Mirasso (claudio@ifisc.uib‑csic.es)
BMC Neuroscience 2020, 21(Suppl 1):P18
Electrophysiological recordings have demonstrated a tight inhibitory control of hilar interneurons over Dentate Gyrus granule cells(DGgc) [1,2]. This excitation/inhibition balance is crucial for information transmission [3] and likely relies on inhibitory synaptic plasticity[4]. Our experiments show that LTP induction in the Perforant Pathway(PP) not only potentiates glutamatergic synapses, but unexpectedly
decreases feed-forward inhibition in the DG, facilitating activity propagation in the circuit and modifying the long-range connectivity in thebrain. To investigate this phenomenon, we propose to study a circuitof populations of point neurons described by the Izhikevich model.
The model contains entorhinal cortex (EC) neurons, DGgc, mossy
cells, basket cells and hilar interneurons. The proportion of neurons
per population and the connectivity of the neural network is basedon anatomical published data and is fitted to achieve experimental
electrophysiological in vivo recordings [2]. The study of the effect of
LTP in the local circuit of the DG is performed in the model adapting
synaptic weights in the EC projections. The results obtained from themodel, before and after LTP induction, support the counterintuitive
experimental observation of synaptic depression in the feed-forward
inhibitory connection induced by LTP. We show that LTP increases
the efficiency of the glutamatergic input to recruit the inhibitory
network, resulting in a reciprocal cancellation of the basket cell population activity. We validate the result of the model by electrophysiological experiments inducing LTP in the PP of anaesthetized micein vivo and recording excitatory and inhibitory currents in vitro in the﻿BMC Neurosci 2020, 21(Suppl 1):54

same animals. Overall, our findings suggest that LTP of the EC input
increases the excitation/inhibition balance, and facilitates activity
propagation to the next station in the circuit by recruiting an interneuron-interneuron network that inhibits the tight control of basket cells
over DGgc firing.
References
1. Bragin A, et al. Gamma (40-100 Hz) oscillation in the hippocampus of the
behaving rat. Journal of Neuroscience. 1995; 15(1): 47-60.
2. Pernía-Andrade AJ, Jonas P. Theta-gamma-modulated synaptic currents inhippocampal granule cells in vivo define a mechanism for network oscilla‑tions. Neuron. 2014; 81(1): 140–152.
3. Bartos M, Vida I, Frotscher M, Jörg G, Jonas P. Rapid Signaling at Inhibitory
Synapses in a Dentate Gyrus Interneuron Network. Journal of Neuroscience.
2001; 21(8): 2687-2698.
4. Vogels TP, Sprekeler H, Zenke F, Clopath C, Gerstner W. Inhibitory plasticity
balances excitation and inhibition in sensory pathways and memory net‑
works. Science. 2011; 334(6062): 1569-73.

P19
Neuroscience gateway enabling modeling and data processing
using high performance and high throughput computing resources
Amitava ­Majumdar1, Subhashini S­ ivagnanam1, Kenneth ­Yoshimoto1,
Dave ­Nadeau1, Martin ­Kandes1, Trever ­Petersen1, Ramon M­ artinez2, Dung
­Troung2, Arnaud D­ elorme2, Scott M­ akeig2, Ted ­Carnevale3
1
University of California, San Diego, San Diego Supercomputer Center,
La Jolla, California, United States of America; 2University of California, San
Diego, Institute for Neural Computation, La Jolla, California, United Statesof America; 3Yale University, Neurobiology, New Haven, Connecticut,
United States of America
Correspondence: Amitava Majumdar (majumdar@sdsc.edu)
BMC Neuroscience 2020, 21(Suppl 1):P19
The Neuroscience Gateway (NSG) has been serving the computational neuroscience community since early 2013. Its initial goal was toreduce technical and administrative barriers that neuroscientists facein accessing and using high performance computing (HPC) resources
needed for large scale neuronal modeling projects. For this purpose,
NSG provided tools and software that require and run efficiently on
HPC resources available as a part of the US XSEDE (Extreme Scienceand Engineering Discovery Environment) program that coordinates
usage of academic supercomputers. Since around 2017 experimentalists such as cognitive neuroscientists, psychologists and biomedical
researchers started to use NSG for their neuroscience data processing, analysis and machine learning work. Data processing workloads
are more suitable on high throughput computing (HTC) resources thatare suitable for single core jobs typically run to process individual data
sets of subjects. Machine learning (ML) workloads require use of GPUs
for well-known ML frameworks such as TensorFlow. NSG is adapting
to respond to the needs of experimental neuroscientists by providing HTC resources, in addition to already enabling successfully thecomputational neuroscience community for many years by providing
HPC resources. Data processing focused work of experimentalists also
require NSG to add various data functionalities, such as ability to transfer/store large data to/on NSG, validate the data, process same data by
multiple users, publish final data products, visualize the data, search
the data etc. These features are being add to NSG currently. Separately
there is a demand from the neuroscience community to make NSG anenvironment where neuroscience tool developers can test, benchmark, and scale their newly developed tools and eventually disseminate their tools via the NSG for neuroscience users.
The poster will describe NSG from its beginning and how it is evolving for the future needs of the neuroscience community such as:(i) NSG has been successfully serving primarily the computational
neuroscience community, as well as some data processing focusedneuroscience researchers, until now; (ii) new features are added tomake it a suitable and efficient dissemination environment for labdeveloped neuroscience tools. These will allow tool developers todisseminate their lab-developed tools on NSG taking advantage ofthe current functionalities that are being well served on NSG for thelast seven years such as a growing user base, an easy user interface,

Page 20 of 123

an open environment, the ability to access and run jobs on powerfulcompute resources, availability of free supercomputer time, a wellestablished training and outreach program, and a functioning user
support system. All of these well-functioning features of NSG will
make it an ideal environment for dissemination and use of lab-developed computational and data processing neuroscience tools; (iii) NSG
is being enhanced such that it can have more seamless access to HTC
resources provided by the Open Science Grid (OSG) and commercial
cloud. This will allow data processing and machine learning oriented
workloads to be able to take advantage of HTC and cloud resources
including GPUs; (iv) New data management features are being added
to NSG and these include the ability to transfer/upload large data, validate uploaded data, share and publish data etc.
P20
Effects of dopamine on networks of barrel cortex­ uang2, Prescilla U­ ijtewaal3, Bernhard ­Englitz1,
Fleur ­Zeldenrust1, Chao H
Tansu ­Celikel1
1
Donders Institute for Brain, Cognition and Behaviour, Radboud University,
Department of Neurophysiology, Nijmegen, Netherlands; 2Universityof Leipzig, Department of Biology, Leipzig, Germany; 3UMC Utrecht,
Imaging Division, Department of Radiotherapy, Utrecht, Netherlands
Correspondence: Fleur Zeldenrust (fleurzeldenrust@gmail.com)
BMC Neuroscience 2020, 21(Suppl 1):P20
The responses of excitatory pyramidal cells and inhibitory interneurons in cortical networks are shaped by each neuron’s place in the
network (connectivity of the network) and its biophysical properties(ion channel expression [1]), which are modulated by top-down neuromodulatory input, including dopamine. Using a recently developed
ex vivo method [2], we showed that the activation of the D1 receptor(D1R) increases the information transfer of fast spiking, but not regularspiking, cells, by decreasing their threshold [3]. Moreover, we showedthat these differences in neural responses are accompanied by faster
decision-making on a behavioural level. However, how the single-cellchanges in spike responses result in these behavioural changes is still
unclear. Here, we aim to bridge the gap between behavioural and single cell effects by considering the effects of D1R activation on a network level.
We took a 3-step approach and simulated the effects of dopamine bylowering the thresholds of inhibitory but not excitatory neurons:
1) Network construction. We created a balanced network of L2/3 andL4 of the barrel cortex, consisting of locally connected integrateand-fire neurons. We reconstructed the somatosensory cortex insoma resolution ([4], Fig. 1A), and adapted the number and ratioof excitatory and inhibitory neurons and the number of thalamic
inputs accordingly.
2) Activity of the balanced state. The adaptations in the neural populations and connectivity resulted in a heterogeneous asynchronous
regime [5] in L2/3, with highly variable single-neuron firing rates
and suggesting a functional role of stimulus separation, and a ‘classical’ asynchronous regime in L 4, with more constant firing rates
and suggestive of an information transmission role (Fig. 1B).
3) Functional effects. We used a spike-based FORCE learning [6,7]application, trained on either a gap-crossing task (data from [8]) oron a pole detection task (publicly available data from [9], Fig. 1C).
We compared the results against a benchmark test consisting of a
3-layer deep neural net with a recurrent layer.

﻿BMC Neurosci 2020, 21(Suppl 1):54

Page 21 of 123

systematic manner, stimuli in the form of a stochastic moving bar areused in experiments with retinas from bull frogs in a multi-electrodesystem. Trajectories of the bar are produced by Ornstein-Uhlenbeck(OU) processes with different time correlations (memories) induced by
a butter-worth low-pass filter with various cut-off frequencies.
We then investigated the predictive properties of single RGC by calculating the time shifted mutual information (MI(x,r;δt)) between spiking
output from RGCs and the bar trajectories. Intuitively, the peak position of MI(δt) is typically negative when considering the processing
delay of the retina. Our measured peak positions of MI(δt) for some
RGCs were characterized by both positive and negative peak position
under low-pass OU (LPOU) stimulus. This finding indicates that some
RGCs (P-RGCs) are predictive while the others are non-predictive (NPRGCs). For LPOU with various correlation times, the MI peaks from the
P-RGCs are positively correlated with the correlation times of the stimuli while those from the NP-RGCs are always around a fixed negative
number (-50ms).

Fig. 1 A Density of identified cellular populations across the six
cortical layers. B Effects of the relative number of inhibitory neurons
on the asynchronous state. Yellow indicates classical asynchronous(CA) dynamics, purple indicates a heterogeneous asynchronous (HA)
regime, white indicates no asynchronous irregular state. C Spiking
FORCE learning during a pole localization task
References
1. Azarfar A, Calcini N, Huang C, Zeldenrust F, Celikel T. Neural coding: A single
neuron’s perspective. Neuroscience & Biobehavioral Reviews. 2018; 94:
238-247.
2. Zeldenrust F, de Knecht S, Wadman WJ, Denève S, Gutkin B. Estimating theinformation extracted by a single spiking neuron from a continuous input
time series. Frontiers in Computational Neuroscience. 2017; 11: 49.
3. Calcini N, et al. Cell-type specific modulation of information transfer by
dopamine. Cosyne Abstract 2019, Lisbon
4. Huang C, Zeldenrust F, Celikel T. DepartmentofNeurophysiology/Corticalrepresentation-of-touch-in-silico. GitHub. 2019; https://github.com/
DepartmentofNeurophysiology/Cortical-representation-of-touch-in-silico(accessed March 2, 2020).
5. Ostojic S. Two types of asynchronous activity in networks of excitatory andinhibitory spiking neurons. Nature Neuroscience. 2014; 17: 594–600.
6. Sussillo D, Abbott LF. Generating Coherent Patterns of Activity from Chaotic
Neural Networks. Neuron. 2009; 63: 544–57.
7. Nicola W, Clopath C. Supervised learning in spiking neural networks with
FORCE training. Nature Communications. 2017; 8: 1–15.
8. Azarfar A, et al. An open-source high-speed infrared videography databaseto study the principles of active sensing in freely navigating rodents. GigaS‑
cience. 2018; 7(12); giy134.
9. Peron SP, Freeman J, Iyer V, Guo C, Svoboda K. A cellular resolution map of
barrel cortex activity during tactile behavior. Neuron. 2015; 86(3); 783-799.

P21
Synergy in the encoding of a moving bar by a retina­ hen2
Kuan H C­ hen1, Qi‑Rong ­Lin2, Chi K C
1
2Academia Sinica, Taiwan; Academia Sinica, Institute for Physics, Taiwan
Correspondence: Kuan H Chen (nghdavid123@gmail.com)
BMC Neuroscience 2020, 21(Suppl 1):P21
To produce timely responses, animals must conquer delays fromvisual processing pathway by predicting motion. Previous studies [1] revealed that predictive information of motion is encoded inspiking activities of retinal ganglion cells (RGCs) early in the visual
path. In order to study the predictive properties of a retina in a more

Furthermore, we apply principle component analysis [2] on the waveforms of stimuli preceding each of the neuron’s spike (spike triggered
stimuli) to separate spikes into two clusters according to whether
their projections to principle component are negative or positive. We
find that predictive information can be extracted from the apparent
non-predictive NP-RGCs when MI(δt) is obtained with spikes fromeach cluster. This last finding suggests that spikes from a single RGC
might have different origins. Since the responses (r) from RGCs can
carry information for both position (x) and velocity (v) of the moving
bar, we have also performed partial information decomposition [3] forthe mutual information between r and the combined state {x,v} which
can be written as I[(x,v):r] = S + Ur + Ux + R where Ur and Ux are theunique contribution from x and v respectively while similarly R and
S are the redundant and synergy contribution. We find that synergy
from x and v is needed to produce anticipation. A simple spikes generation model with synergy from x and v is constructed to understandour experimental data.

Fig. 1 Two kinds of responses under low-passed-OU stimulation
with different correlation time: Predicting cells (P, solid line) and Nonpredicting cells (N, dot line)
References
1. Palmer SE, Marre O, Berry MJ, Bialek W. Predictive information in a sensory
population. Proceedings of the National Academy of Sciences. 2015;
112(22): 6908-6913.
2. Geffen MN, de Vries SEJ, Meister M. Retinal Ganglion Cells Can Rapidly
Change Polarity from Off to On. PloS Biology. 2007; 5(3): e65.
3. Williams PL, Beer RD. Nonnegative Decomposition of Multivariate Informa‑tion. Arxiv. 2010; 1004.2515 [preprint].

﻿BMC Neurosci 2020, 21(Suppl 1):54

P22
Retina as a negative group delay filter
PoYu ­Chou1, Chi K ­Chen2
1
Academia Sinica, Taiwan; 2Academia Sinica, Institute for Physics, Taiwan
Correspondence: PoYu Chou (az6520555@gmail.com)
BMC Neuroscience 2020, 21(Suppl 1):P22
Anticipation is important for living organisms to survive. The anticipative dynamics is found in retina [1], which can compensate for thedelay of visual signals during transmission and processing. Responsesof retinas (r(t)) from bull frogs have been investigated for anticipative properties in a multi-electrode array system by using whole field
stochastic stimulations (S(t)) generated by an Ornstein–Uhlenbeck(OU) process. Time correlated S(t) can be then created by passing the
OU signal through a low pass filter with various cutoff frequencies.
Anticipative properties of the elicited spikes from the retinas are thencharacterized by the method of time lag mutual information (TLMI)
between r(t) and S(t) [1]. We find that only stimulations with longenough correlations can elicit anticipative responses from the retinas;
similar to the finding of [1] in which information in the stimulation wastime coded. However, information is being rate coded in the present
experiment. Recently, Voss [2] proposed that a negative group delay
filter can produce anticipative response to low-pass filtered random
signals. To test this idea, it is shown that an NGD filter with appropriate parameters can indeed be used to produce TLMI from S(t) similar
to those observed in experiments. Furthermore, experiments withdark and bright Gaussian light pulses further confirmed that retina
can be considered as an NGD filter; but only for the dark pulses. This
last finding and the NGD capability of the retina suggest that there isa delayed negative feedback in the off-pathway of the retina. In fact,a two neuron-model with delayed negative feedback can be shown
to produce properties of an NGD filter. Presumably, such mechanism
might also exist in a retina.
References
1. Chen KS, Chen CC, Chan CK. Characterization of predictive behavior of aretina by mutual information. Frontiers in Computational Neuroscience.
2017; 11: 66.
2. Voss HU. Signal prediction by anticipatory relaxation dynamics. PhysicalReview E. 2016; 93(3): 030201.

P23
Closed‑form approximations of single‑channel calcium
nanodomains in the presence of cooperative calcium buffers
Victor Matveev, Yinbo Chen
New Jersey Institute of Technology, Department of Mathematical Sciences,
Newark, United States of America
Correspondence: Victor Matveev (matveev@njit.edu)
BMC Neuroscience 2020, 21(Suppl 1):P23
Calcium ion (Ca2+) elevations produced in the vicinity of single open
Ca2+ channels are termed Ca2+ nanodomains, and play an importantrole in triggering secretory vesicle exocytosis, myocyte contraction,and other fundamental physiological processes. Ca2+ nanodomains
are shaped by the interplay between Ca2+ influx, Ca2+ diffusion andits binding to Ca2+ buffers, which absorb most of the Ca2+ entering the cell during a depolarization event. In qualitative studies of
local Ca2+ signaling, the dependence of Ca2+ concentration on the
distance from the Ca2+ channel source can be approximated with areasonable accuracy by analytic approximations of quasi-stationary
solutions of the corresponding reaction-diffusion equations. Suchclosed-form approximations help to reveal the qualitative dependence of nanodomain characteristics on Ca2+ buffering and diffusion
parameters, without resorting to computationally expensive numerical simulations. Although a range of nanodomain approximations
had been developed for the case of Ca2+ buffers with a single Ca2+
binding site, for example the Rapid Buffer Approximation, the Excess
Buffer Approximation, and the Linear approximation [1,2], most biological buffers have more complex Ca2+-binding stoichiometry.

Page 22 of 123

Further, several important Ca2+ buffers and sensors such as calretinin
and calmodulin consist of distinct EF-hand domains, each possessing two Ca2+ binding sites exhibiting significant cooperativity inbinding, whereby the affinity of the second Ca2+ binding reaction
is much higher compared to the first binding reaction. To date, only
the Rapid Buffer Approximation (RBA) has been generalized to Ca2+
buffers with two binding sties [3]. However, the performance of RBA
in the presence of cooperative Ca2+ buffers is limited by the complex interplay between the condition of slow diffusion implied by the
RBA, and the slow rate of the first Ca2+ binding reaction characterizing cooperative Ca2+ binding. To resolve this problem, we present
modified versions of several Ansatze recently introduced for the caseof simple buffers [4], extending them to the case of Ca2+ buffers with
2-to-1 stoichiometry. These new approximants interpolate between
the short-range and long-range distance-dependence of Ca2+ nanodomain concentration using a combination of rational and exponential functions. We examine in detail the parameter-dependence of theapproximation accuracy, and show that this method is superior to RBA
for a wide ranges of buffering parameter values. In particular, the new
approximants accurately estimate the distance-dependence of Ca2+
concentration in the case of calretinin or calmodulin.
Acknowledgements: Supported in part by NSF DMS-1517085 (V.M).
References
1. Naraghi M, Neher E. Linearized buffered Ca2+ diffusion in microdomains
and its implications for calculation of [Ca2+] at the mouth of a calcium
channel. Journal of Neuroscience. 1997; 17: 6961- 6973.
2. Smith GD, Dai L, Miura RM, Sherman A. Asymptotic Analysis of Buffered
Calcium Diffusion Near a Point Source. SIAM Journal on Applied Mathemat‑
ics. 2001; 6: 1816-1838.
3. Matveev V. Extension of Rapid Buffering Approximation to Ca2+ Bufferswith Two Binding Sites. Biophysical Journal. 2018; 114: 1204-1215.
4. Chen Y, Muratov C, Matveev V. Efficient approximations for stationary
single-channel Ca2+ nanodomains across length scales. bioRxiv 2020.

P24
A bump‑attractor spiking neural network for motor adaptation
and washout based on norepinephrine release in primary motor
cortexMariia ­Popova1, Melanie ­Tschiersch1, Nicolas ­Berberich2, Stefan K ­Ehrlich2,
David ­Franklin3, Gordon C­ heng2
1
Technical University of Munich, Department of Electrical and Computer
Engineering, Munich, Germany; 2Technical University of Munich, Institutefor Cognitive Systems, Munich, Germany; 3Technical University of Munich,
Neuromuscular Diagnostics, Munich, Germany
Correspondence: Mariia Popova (maria.popova@tum.de)
BMC Neuroscience 2020, 21(Suppl 1):P24
In order to examine the formation of predictive motor memories,
typical behavioural motor learning experiments perturb participants
reaching movements using an external force field, to which they rapidly adapt, and exhibit after effects when the force field is removed.
During the force field adaptation trials (Fig. 1), subjects move theirhand from the start position (red circle) to the target position (blackx) while a force field perturbs the movement (strength and direction
indicated by yellow arrows) causing the hand to move to the final
position (green circle). After the force field is removed, a washout
effect is observed. While previous computational models can recreate
the behavioral results, they do not account for the neural mechanisms
involved. A computational model including a synaptic mechanism
can help to explain the processes involved in motor learning. For
this reason, we developed a bump-attractor, spiking neuron modelof primary motor cortex (M1) proposing a synaptic mechanism using
reward-based neurotransmitter release to explain motor adaptation
and washout.

﻿BMC Neurosci 2020, 21(Suppl 1):54

Fig. 1 a Schematics of behavioural experiment. b Single trial simula‑tion: Bump of neuronal activity in the first perturbed trial. c Adapta‑
tion results: Results of the simulations (blue) of error development
over consecutive trials during adaptation and washout in comparison
to behavioural data (green) from [1]The developed model consists of directionally-tuned neurons, shown
to exist in M1 in biology, that encode the hand position through average neural firing. The force field is modeled through a simulated, external current perturbing the neural activity in the direction of the force
field. In biology, Norepinephrine is released from locus coreuleus toM1 when errors are detected in the visual pathway. Norepinephrine
affects M1 in a goal-directed manner, increasing the excitatory synaptic
responses in the so-called hotspot, which is determined by arousal. For
the model to remain close to biology, adaptation is modeled through anerror-dependent increase in excitatory to excitatory conductance in thetarget position within the M1 model, leading to a decrease of the perturbation on the stable bump of neural activity across trials. Washout isimplemented through a shift of the hotspot and the accumulated Norepinephrine through a motor-coordinate system shift during force fieldremoval. After the initial washout trial, the wrongful coordinate systemshift is detected and Norepinephrine in the shifted hotspot decays.
The simulations from the proposed computational model qualitatively
account for both: adaptation and washout as seen in comparison to
the behavioural data from [1] (Fig. 1). Thus, the model suggests for the
first time a biologically plausible synaptic mechanism in M1 that can
explain the main features of motor learning of external dynamics.
Reference
1. Nozaki D, Kurtzer I, Scott S. Limited transfer of learning between unimanualand bimanual skills within the same limb. Nature Neuroscience. 2006; 9:1364–1366.

P25
Increased glutamate metabolism in ACC after brief mindfulness
training: a pilot MR spectroscopy study
Yiyuan ­Tang1, Pegah ­Askari2, Changho C­ hoi2
1
Texas Tech University, Lubbock, Texas, United States of America;2
University of Texas Southwestern Medical Center, Advanced Imaging
Research Center, Dallas, United States of America
Correspondence: Yiyuan Tang (yiyuan.tang@ttu.edu)
BMC Neuroscience 2020, 21(Suppl 1):P25
Mindfulness training (MT) involves paying attention to the present andincreasing awareness of one’s thoughts and emotions without judgment and has become a promising intervention for promoting healthand well-being. Neuroimaging studies have shown its beneficial effects

Page 23 of 123

on brain functional activity, connectivity, and structures [1-3]. A seriesof RCTs indicated that one form of MT, integrative body-mind training (IBMT) induces brain functional and structural changes in region
related to self-control networks such as the anterior cingulate cortex(ACC) after 2-10 h of practice [1-3]. However, whether MT could change
brain metabolism in the ACC remains unexplored. Utilizing a non-invasive proton magnetic resonance spectroscopy (MRS), we conducted
the first pilot study investigating whether brief IBMT could change theexcitatory and inhibitory responses of neurotransmitters within the
ACC [1-3]. Nine healthy college students completed ten 1-hour IBMT
sessions within 2-week and brain metabolism were assessed before
and after using a 3T Siemens Prisma scanner. Following survey imaging
and T1-weighted structural imaging, single-voxel point-resolved spectroscopy (PRESS) was conducted for estimating the metabolite concentrations in 2 regions - rostral and dorsal ACC based on prior literature[1-4]. PRESS scan parameters included TR 2s, TE 90 ms, sweep width 2.5
kHz, 1024 sampling points, and 256 signal averages. Water suppression and B0 shimming up to second order were performed with thevendor-supplied tools. Reference water signal was acquired for eddy
current compensation, multi-channel combination, and metabolite
quantification. Spectral fitting was performed with LCModel software[5], using in-house basis spectra of metabolites which were calculated
incorporating the PRESS slice selective RF and gradient pulses. The
spectral fitting was performed between 0.5-4.0 ppm. After correcting the LCModel estimates of metabolite signals for the T2 relaxation
effects using published T2 values [6], the millimolar concentrations ofmetabolites were calculated with reference to water at 42 M. Paired
t-tests were performed to examine changes. Results indicated a significant increase in glutamate metabolism (t = 3.24, p = 0.012), as well as
Glx (glutamate + glutamine) (t = 2.44, p = 0.041) in the rostral ACC.
Results indicate that MT may not only increase ACC activity, but also
may induce neurochemical changes in regions of self-control networks,
suggesting a potential mechanism of MT’s effects on disorders such as
addiction and schizophrenia, which often involve the dysfunction of
glutamatergic system (i.e. lower glutamate metabolism).
References
1. Tang YY, Holzel BK, Posner MI. The neuroscience of mindfulness meditation.
Nature Reviews Neuroscience. 2015; 16: 213-22
2. Tang YY. The Neuroscience of Mindfulness Meditation: How the Body and
Mind Work Together to Change Our Behavior? Springer Nature. 2017.
3. Tang YY, Tang R, Gross JJ. Promoting emotional well-being through anevidence-based mindfulness training program. Frontiers in Human Neuro‑science. 2019;13: 237.
4. Yang S, et al. Lower glutamate levels in rostral anterior cingulate of chronic
cocaine users - A (1)H-MRS study using TE-averaged PRESS at 3T. Psychiatry
Research. 2009; 174: 171-6.
5. Provencher SW. Estimation of metabolite concentrations from localized
in vivo proton NMR spectra. Magnetic Resonance in Medicine. 1993; 30:
672–679.
6. Ganji SK, et al. T2 measurement of J‐coupled metabolites in the humanbrain at 3T. NMR in Biomedicine. 2012; 25(4): 523-529.

P26
Learning recurrent dynamic patterns in spiking neural networks
Christopher Kim, Carson Chow
National Institutes of Health, Laboratory of Biological Modeling, NIDDK,Maryland, United States of America
Correspondence: Christopher Kim (chrismkkim@gmail.com)
BMC Neuroscience 2020, 21(Suppl 1):P26
Understanding the recurrent dynamics of cortical circuits engaged incomplex tasks is one of the central questions in computational neuroscience. Most of recent studies train the output of recurrent models
to per- form cognitive or motor tasks and investigate if the recurrent
dynamics emerging from task-driven learning can explain neuronaldata. However, the possible range of recurrent dynamics that can berealized within a recurrent model after learning, particularly in a spiking neural network, is not well understood. In this study, we focus on
investigating spiking network’s capability to learn recurrent dynamics and characterize the learning capacity in terms of network size,

﻿BMC Neurosci 2020, 21(Suppl 1):54

intrinsic synaptic decay time and target decay time. We find that, bymodifying recurrent synaptic weights, spiking networks can generate
arbitrarily complex recurrent patterns if 1) the target patterns can beproduced self-consistently, 2) the synaptic dynamics are fast enoughto track the targets, and 3) the number of neurons in the network islarge enough for noisy postsynaptic currents to approximate the targets. We examine spiking network’s learning capacity analyticallyand corroborate the predictions by training spiking networks to learn
arbitrary patterns and in-vivo cortical activity. Furthermore, we showthat a trained network can operate in balanced state if the total excitatory and inhibitory synaptic weights to each neuron are constrained
to preserve the balanced network structure. Under such synaptic constraints, the trained network generates spikes at the desired rate withlarge trial-to-trial variability and exhibits paradoxical features of inhibition-stabilized network.
These results show that spiking neural networks with fast synapses
and a large number of neurons can generate arbitrarily complex
dynamics. When learning is not optimal, our findings can suggest
potential sources of learning errors. Moreover, networks can betrained in dynamic regime relevant to cortical circuits.
P27
Lessons from Artificial Neural Network for studying coding
principles of Biological Neural Network
Hyojin ­Bae1, Chang‑eop ­Kim2, Gehoon C­ hung3
1
Gachon university, Gyeonggi‑do, South Korea; 2Gachon university,
College of Korean Medicine, Gyeonggi‑do, South Korea; 3Seoul National
University, Oral Physiology, Seoul, South Korea
Correspondence: Hyojin Bae (qogywls1573@gmail.com)
BMC Neuroscience 2020, 21(Suppl 1):P27
An individual neuron or neuronal population is conventionally said tobe “selective” to a feature of stimulus if they differentially respond to
the feature. Also, they are considered to encode certain information if
decoding algorithms successfully predict a given stimulus or behavior
from the neuronal activity. However, an erroneous assumption about
the feature space could mislead the researcher about a neural coding
principle. In this study, by simulating several likely scenarios throughartificial neural networks (ANNs) and showing corresponding casesof biological neural networks (BNNs), we point out potential biases
evoked by unrecognized features i.e., confounding variable.
We modeled an ANN classifier with the open-source neural network
library Keras, running Tensorflow as backend. The model is composedof five hidden layers, dense connections and rectified linear activation.
We added a dropout layer and l2-regularizer on each layer to apply
penalties on layer activity during optimization. The model was trainedwith CIFAR-10 dataset and showed a saturated test set accuracy at
about 53% (the chance level accuracy = 10%). For a stochastic sampling of individual neuron’s activity from each deterministic unit, we
generated the Gaussian distribution through modeling within-population variability according to each assumption.
Using this model, we showed 4 possible misinterpretation cases
induced by a missing feature. (1) The researcher can choose the second-best feature which has similarity to ground truth feature. (2) An
irrelative feature which correlated with ground truth feature can bechosen. (3) Evaluating decoder in incomplete feature space could
result in the overestimation of the performance of the decoder. (4)Misconception about the receptive field of the unit could make a signal to be incorporated in noise.
In conclusion, we suggest that the comparative study of ANN and BNN
from the perspective of machine learning can be a great strategy for
deciphering the neural coding principle.
P28
Inhibitory gain allows transitions between integrated
and segregated states: a neuromodulatory analysis
from whole‑brain models
Carlos ­Coronel1, Patricio ­Orio2, Rodrigo Cofré3
1
Universidad de Valparaíso, Centro Interdisciplinario de Neurociencia
de Valparaíso, Valparaíso, Chile; 2Universidad de Valparaíso, Instituto dePage 24 of 123

Neurociencia, Valparaiso, Chile; 3Universidad de Valparaíso, Centro deInvestigación y Modelamiento de Fenómenos Aleatorios, Valparaíso, Chile
Correspondence: Carlos Coronel (carlos.coronel@postgrado.uv.cl)
BMC Neuroscience 2020, 21(Suppl 1):P28
In the brain, at the macroscale level, two organizational principles participate in the processing of information: segregation and integration.
While segregation allows the processing of information in specificbrain regions, integration coordinates the activity of these regions togenerate a behavioral response [1]. Recent studies suggest that thecholinergic system promotes segregated states and the noradrenergic
system promotes integrated states, both measured using graph theoretical tools over the functional connectivity (FC) matrices [2,3]. We
extended this neuromodulatory framework by including the noradrenergic system (filter gain), and the effect of the cholinergic systemin the excitatory and inhibitory circuits separately (excitatory andinhibitory gain). The neuromodulatory framework was tested using
the Jansen Rit neural mass model [4], built from real human structuralconnectivity matrices and heterogeneous transmission delays forlong-range connections (Fig. 1). The fMRI-BOLD signals were simulated using a generalized hemodynamic function model, and features
such as the global phase synchronization, oscillatory frequency and
SNR were measured. On the other hand, FC matrices were built using
pairwise Pearson’s correlation from the simulated BOLD signals. Thresholded FC matrices were analyzed with graph theoretical tools forcomputing segregation and integration. Our results suggest that functional integration is possible only with the suppression of the feedback
excitation, mediated by the inhibitory gain, and follows a sigmoid orinverted U-shaped function, depending of the noise intensity levels.
Also, the integration is accompanied by an increase in signal to noise
ratio and regularity of EEG signals. The results suggest a mechanistic
interpretation. We propose that the cholinergic system neuromodulation on the excitatory connections increases SNR locally, and the effectof that system on the inhibitory interneurons suppresses the local
cortico-cortical transmission, increasing the responsivity of pyramidal
neurons to stimulus from distant regions. Finally, the noradrenergic
system coordinates long-range neural activity promoting integration.
This framework constitutes a new set of tools and ideas to test how
neural gain mechanisms mediate the balance between integrationand segregation in the brain.

Fig. 1 Whole-brain neural mass model. A The model consists in a
population of pyramidal neurons, and two populations of excita‑
tory and inhibitory interneurons. B The N = 90 cortical columns
are connected by real human structural connectivity matrices, withheterogeneous time-delays. C The cholinergic system operates withthe parameters α and β, and the noradrenergic system with theparameter r0

﻿BMC Neurosci 2020, 21(Suppl 1):54

References
1. Rubinov M, Sporns O. Complex network measures of brain connectivity:
uses and interpretations. Neuroimage. 2010; 52(3): 1059-1069.
2. Shine JM, Aburn MJ, Breakspear M, Poldrack RA. The modulation of neuralgain facilitates a transition between functional segregation and integrationin the brain. Elife. 2018; 7: e31130.
3. Shine JM. Neuromodulatory influences on integration and segregation in
the brain. Trends in Cognitive Sciences. 2019.
4. Jansen BH, Rit VG. Electroencephalogram and visual evoked potential gen‑eration in a mathematical model of coupled cortical columns. Biological
Cybernetics. 1995; 73(4): 357-366.

P29
High‑order interdependencies and the functional balanceof segregation‑integration in the aging brain
Marilyn ­Gatica1, Rodrigo ­Cofre2, Fernando R­ osas3, Pedro M­ ediano4,
Patricio ­Orio5, Ibai ­Diez6, Stephan S­ winnen7, Jesus C­ ortes8
1
Universidad de Valparaíso and Biomedical Research Doctorate Program
UPV Spain, CINV, Faculty of Sciences, Valparaíso, Chile; 2Universidad
de Valparaiso, Institute of Mathematical Engineering, Valparaiso, Chile;
3
Imperial College London, Department of Medicine, London, United
Kingdom; 4Cambridge University, Department of Psychology, Cambridge,
United Kingdom; 5Universidad de Valparaíso, Instituto de Neurociencia,
Valparaiso, Chile; 6Gordon Center for Medical Imaging, Harvard Medical
School, Department of Radiology, Boston, United States of America; 7KU
Leuven, Research Center for Movement Control and Neuroplasticity,
Department of Movement Sciences, Leuven, Belgium; 8Computational
Neuroimaging Lab, IKERBASQUE: The Basque Foundation for Science,
Biocruces Health Research Institute, Bilbao, Spain
Correspondence: Rodrigo Cofre (rodrigocofre@gmail.com)
BMC Neuroscience 2020, 21(Suppl 1):P29
The interdependencies in the brain can be studied either from a structural/anatomical perspective (“structural connectivity”, SC) or by considering statistical interdependencies (“functional connectivity”, FC).
While the SC is essentially pairwise (white-matter fibers start in a certain region and arrive at another), the FC is not, i.e., there is no reason
to consider statistical interdependencies pairwise. A promising tool tostudy high-order interdependencies is the recently proposed O-Information [1]. This quantity captures the balance between redundancies
and synergies in arbitrary sets of variables, thus extending the properties of the interaction information of three variables to larger sets.
Redundancy is here understood as an extension of the conventional
notion of correlation to more than two variables In contrast, synergy
corresponds to an emergent statistical relationships that control thewhole but not the parts.
In this study, we follow the seminal ideas introduced by Tononi,
Sporns, and Edelman [2], which state that high brain functions might
depend on the co-existence of integration and segregation. Whilethe latter enables brain areas to perform specialized tasks independently of each other, the former serves to bind together brain areas
towards an integrated whole for the purpose of goal-directed task
performance. A key insight put forward in [2] is that segregation andintegration can coexist and that this coexistence is measurable byassessing the high-order interactions of neural elements. We used the
O-Information to investigate how high-order statistical interdependencies are affected by aging. For this, we analyzed fMRI data at rest
from 164 healthy participants, ranging from 10 to 80 years old. Our
results show an important increase in redundant interdependencies
in the older population (age ranging from 60 to 80 years). Moreover,this effect seems to be pervasive, taking place at all interaction orders,suggesting a change in the balance of differentiation and integration
towards more synchronized arrangements. Additionally, a redundantcore of brain modules was observed, which decreased in size withage. The framework presented here and in detail in [3], provide novel
insights into the aging brain revealing the role of redundancy in prefrontal and motor cortices in older participants, thus affecting basic
functions such as working memory, executive and motor functions.

Page 25 of 123

This methodology may help to provide a better understanding ofsome brain disorders from an informational perspective, providing“info-markers”, that may lead to fundamental insights into the humanbrain in health and disease. The code to compute the metrics is available at [4].

Fig. 1 Method overview and main result. A 164 subjects, 2514 fMRI
signals parcellated into 20 regions. B For each subject and interaction
order we computed for each n-plet their O-Information. C Averageover all 20 modules of redundancy and synergy for each of the agegroups I1 (younger), I2, I3, I4 (oldest). The right panel shows thatgroup differences in redundancy (represented by diamonds) using
FDR
References
1. Rosas F, Mediano Pedro AM, Gastpar M, and Jensen HJ. Quantifying Highorder Interdependencies via Multivariate Extensions of the Mutual Informa‑tion. Physical Review E. 2019; 100: 032305.
2. Tononi G, Sporns O, Edelman GM. A measure for brain complexity: relating
functional segregation and integration in the nervous system. Proceedingsof the National Academy of Sciences. 1994; 91(11):5033-5037.
3. Gatica M, et al. High-order interdependencies in the aging brain. bioRxiv.
2020.
4. https://github.com/brincolab/High-Order-interactions.

P30
Biophysically realistic model of mouse olfactory bulb gamma
fingerprint
Justas ­Birgiolas1, Richard G­ erkin2, Sharon C­ rook3
1
Arizona State University, Arizona, United States of America; 2ArizonaState University, School of Life Sciences, Tempe, Arizona, UnitedStates of America; 3Arizona State University, School of Mathematicaland Statistical Sciences, Tempe, Arizona, United States of America
Correspondence: Justas Birgiolas (jbirgio@gmail.com)
BMC Neuroscience 2020, 21(Suppl 1):P30
The mammalian olfactory bulb is an intensively investigated system that is important in understanding neurodegenerative diseases.
Insights gained from understanding the system also have important
agricultural and national security applications. In this work, we developed a large-scale, biophysically, and geometrically realistic model ofthe mouse olfactory bulb and the gamma frequency oscillations [1] it
exhibits. Model code, documentation, and tutorials are available atolfactorybulb.org.

﻿BMC Neurosci 2020, 21(Suppl 1):54

The model consists of realistic mitral, tufted (excitatory), and granule(inhibitory) cell models whose electrophysiology and reconstructedmorphology have been validated against experimental data using
a suite of NeuronUnit [2] validation tests. The cell models were realistically placed, oriented, and confined within anatomically correctmouse olfactory bulb layers obtained from the Allen Brain Atlas [3]using features of BlenderNEURON software [4]. Dendritic proximity
was used to form chemical and electrical synapses between principal
and inhibitory cell dendrites. Glomeruli were stimulated using simulated odors obtained from optical imaging experiments. The local fieldpotentials generated by the network were monitored and processedusing wavelet analysis to replicate a gamma frequency pattern (fingerprint) consisting of an early-high, and later-low frequency temporalcomponents. Simulations were performed using parallel-NEURON [5].
The network was subjected to computational manipulations, which
revealed the critical importance of gap junctions, granule cell inhibition, and input strength differences between mitral and tufted cells
in generating the gamma fingerprint. Specifically, at glomerular level,
gap junctions synchronize the firing of mitral cell and tufted cell populations. Synchronized tufted cells activate granule cells, which inhibit
mitral cells. Meanwhile, reduced afferent excitatory input results inmitral cell activation delay, which is amplified by tufted cell activatedgranule cell inhibition. The interaction between these three mechanisms results two clusters of activity seen in the gamma fingerprint(Fig. 1).

Fig. 1 The network model consists of biophysically realistic mitral,tufted, and granule cell models, which are positioned within recon‑
structed mouse olfactory bulb layers. The network is activated bysimulated glomerular inputs. Local field potentials generated by the
network are measured using an extracellular electrode. The network
produces a two-cluster gamma fingerprint (lower right)
The results of the computational experiments support mechanistic
hypotheses proposed in earlier experimental work and provide novel
insights into the mechanisms responsible for olfactory bulb gamma
fingerprint generation, which can be directly tested using common
experimental preparations.
Acknowledgments: This work was funded in part by the National
Institutes of Health through 1F31DC016811 to JB, R01MH1006674 to
SMC, and R01EB021711 to RCG.
References
1. Manabe H, Mori K. Sniff rhythm-paced fast and slow gamma-oscillations in
the olfactory bulb: Relation to tufted and mitral cells and behavioral states.
Journal of Neurophysiology. 2013; 110(7): 1593–1599.
2. Gerkin RC, Birgiolas J, Jarvis RJ, Omar C, Crook SM. NeuronUnit: A package
for data-driven validation of neuron models using SciUnit. bioRxiv. 2019;
665331.
3. Oh S, et al. A mesoscale connectome of the mouse brain. Nature. 2014;
508(7495): 207–214.
4. Birgiolas J. Towards Brains in the Cloud: A Biophysically Realistic Computa‑
tional Model of Olfactory Bulb [Doctoral dissertation, Arizona State University].

Page 26 of 123

5. Hines M, Carnevale N. The NEURON Simulation Environment. Neural Com‑
putation. 1997; 9(6): 1179–1209.

P31
Exploring fast and slow neural correlates of auditory perceptual
bistability with diffusion‑mapped delay coordinates
Pake Melland, Rodica Curtu
University of Iowa, Department of Mathematics, Iowa City, Iowa, UnitedStates of America
Correspondence: Rodica Curtu (rodica‑curtu@uiowa.edu)
BMC Neuroscience 2020, 21(Suppl 1):P31
Perceptual bistability is a phenomenon in which an observer is capable of perceiving identical stimuli with two or more interpretations.
The auditory streaming task has been shown to produce spontaneous switching between two perceptual states [1]. In this task a listener is presented a stream of tones, called triplets, with the pattern
ABA– where A and B are tones with different frequencies and ‘–’ is abrief period of silence. The listener can alternate between two perceptual states: 1-stream in which the stimulus is integrated into asingle stream, and 2-stream in which the stimulus is perceived as two
segregated streams. In order to study the localization and dynamic
properties of neural correlates of auditory streaming we collected
electrocorticography (ECoG) data from neurosurgical patients while
they listened to sequences of repeated triplets and self-reported
switching between the two perceptual states.
It is necessary to find meaningful ways to analyze ECoG recordings,
which are noisy and inherently high dimensional. Diffusion Maps is a
non-linear dimensionality reduction technique which embeds highdimensional data into low dimensional Euclidean space [2]. The Diffusion Map method leverages the creation of a Markov matrix froma similarity measure on the original data. Under reasonable assumptions, the eigenvalues of the Markov matrix are positive and bounded
above by 1. The largest eigenvalues along with their respective
eigenvectors provide coordinates for an embedding of the data intod-dimensional Euclidean space. In [3] Diffusion Maps were used for agroup level analysis of neural signatures during auditory streaming
based on subject reported perception. We extend this approach bytaking into account the time ordered property of the ECoG signals. For
data that has a natural time ordering, it is beneficial to structure thedata to emphasize its temporal dynamics; in [4] the authors develop
the Diffusion- Mapped Delayed Coordinates (DMDC) algorithm. In thisalgorithm, time-delayed data is first created from general time series
data; this initial step projects the data onto its most stable sub-system.
The stable sub-system may remain in a high dimensional space, sothey next apply Diffusion Maps to the time-delayed data which projects the (potentially high dimensional) stable sub-system onto a low
dimensional representation adapted to the dynamics of the system.
We apply the DMDC algorithm to ECoG recordings from Heschl’s
Gyrus in order to explore and reconstruct the underlying dynamics
present during the auditory steaming task. We find that the eigenvalues obtained through the DMDC algorithm provide a way to uncover
multiple time scales present in the underlying system. The corresponding eigenvectors form a Fourier-like basis that is adapted bothto the fast properties of ECoG signal encoding the physical propertiesof the stimulus as well as a slow mechanism that corresponds to perceptual switching reported by subjects.
Acknowledgments: National Science Foundation, CRCNS grant
1515678, and The Human Brain Research Lab, University of Iowa, Iowa
(Matthew A. Howard & Kirill Nourski).
References
1. Noorden LP. Temporal coherence in the perception of tone sequences[Doctoral dissertation, Eindhoven].
2. Coifman RR, Lafon S. Diffusion maps. Applied and Computational Harmonic
Analysis. 2006; 21(1): 5-30.
3. Curtu R, Wang X, Brunton BW, Nourski KV. Neural signatures of auditory
perceptual bistability revealed by large-scale human intracranial recordings.
Journal of Neuroscience. 2019; 39(33): 6482-97.

﻿BMC Neurosci 2020, 21(Suppl 1):54

4. Berry T, Cressman JR, Greguric-Ferencek Z, Sauer T. Time-scale separa‑tion from diffusion-mapped delay coordinates. SIAM Journal on Applied
Dynamical Systems. 2013; 12(2): 618-49.

P32
Contribution of the Na/K pump to rhythmic bursting.
Ronald ­Calabrese1, Ricardo J E ­Toscano2, Parker J ­Ellingson2, Gennady­Cymbalyuk2
1
Emory University, Department of Biology, Atlanta, Georgia, United Statesof America; 2Georgia State University, Neuroscience Institute, Atlanta,
Georgia, United States of America
Correspondence: Ronald Calabrese (biolrc@emory.edu)
BMC Neuroscience 2020, 21(Suppl 1):P32
The Na/K pump, often thought of as a background function in neuronal activity, contributes an outward current (IPump) that responds
to the internal concentration of Na+([Na+]i). In bursting neurons,
such as those found in central pattern generators (CPGs) that produce
rhythmic movements, one can expect the [Na+]i and thus IPump
to vary throughout the burst cycle [1-3]. This variation with electrical activity and the independence from membrane potential endow
IPump with dynamical properties not available in channel-based currents (e.g. voltage- or transmitter- gated, or leak channels). Moreover, in many neurons the pump’s activity is modulated by a variety ofmodulators further expanding the potential role of IPump in rhythmic
bursting activity [4]. Using a combination of experiment, modeling,and hybrid systems analyses, we have sought to determine how
IPump and its modulation influence rhythmic activity in a CPG.
References
1. Rybak IA, Molkov YI, Jasinski PE, Shevtsova NA, Smith JC. Rhythmic burstingin the pre-Bötzinger complex: mechanisms and models. Progress in Brain
Research. 2014; 209: 1-23.
2. Kueh D, Barnett WH, Cymbalyuk GS, Calabrese RL. Na(+)/K(+) pumpinteracts with the h-current to control bursting activity in central patterngenerator neurons of leeches. Elife. 2016; 5: e19322.
3. Picton LD, Nascimento F, Broadhead MJ, Sillar KT, Miles GB. Sodium Pumps
Mediate Activity-Dependent Changes in Mammalian Motor Networks.
Journal of Neuroscience. 2017; 37(4): 906-921.
4. Tobin AE, Calabrese RL. Myomodulin increases Ih and inhibits the NA/K
pump to modulate bursting in leech heart interneurons. Journal of Neuro‑physiology. 2005; 94(6): 3938-50.

P33
Temperature coding mechanisms in the cold‑sensitive Drosophila
neurons
Natalia ­Maksymchuk1, Akira ­Sakurai1, Jamin M L­ etcher1, Daniel N ­Cox1,
Gennady ­Cymbalyuk1
1
Georgia State University, Neuroscience Institute, Atlanta, Georgia, UnitedStates of America
Correspondence: Gennady Cymbalyuk (gcymbalyuk@gmail.com)
BMC Neuroscience 2020, 21(Suppl 1):P33
Sensation of noxious cold stimuli plays an essential role in the survivalof organisms. Adequate perception of certain characteristics of cold
stimuli is necessary for appropriate behavioral responses for body protection. There exist primary cold-sensing neurons that encode distinct
information about the cold thermal stimulus: the rate of temperature
decrease and absolute temperature coding. Here, we focus on theroles of different ion channels in the cold temperature coding of the
Drosophila larva. We investigate dynamics of the Class III (CIII) somatosensory neurons. They trigger a stereotypic cold-evoked behavior, afull-body contraction (CT). We combined computational neuroscience,
genetic, and electrophysiological methods to develop a biophysicalmodel of CIII neurons. Our computational model includes ionic currents implicated by transcriptomic data of ion channels expression in
CIII neurons. We implement these currents using the gating characteristics of Drosophila Na+ and K+ channels obtained from the experimental literature using patch-clamp data [1,2]. We consider threesubsystems (1) fast spike-generating subsystem, (2) moderately slow

Page 27 of 123

pattern-generating subsystem, and (3) slow thermotransduction subsystem. We investigated the role of these subsystems in temperaturecoding. Using the slow-fast decomposition approach, we isolated thefast spike-generating subsystem (a CIII reduced model). We systematically varied two parameters, the temperature (T) and Gleak, classified
observed regimes of activity and mapped them on the plane (T,Gleak).
The model exhibits a wide spectrum of regimes: spiking, bursting, andsilenced. Analysis of a model including a moderately slow patterngenerating subsystem unveiled a slow bursting activity pattern of the
CIII model. This regime requires the participation of Ca2+-activated
K+ currents at noxious cold temperatures and an increased level of
intracellular Ca2+. Similar burst and pause activity pattern were implicated in encoding noxious heat stimuli by the heat-sensitive Drosophila CIV neurons. We investigated a full model including the TRP
channels representing the slow thermotransduction subsystem. We
show that they play the key roles in coding of noxious cold temperature and encoding rate of temperature decrease. Our model qualitatively reproduces the temporal properties of the recordings from
Trpm, Pkd2, and TRPA1 knock-down animals. In agreement with theseexperimental results, the corresponding models of TRP mutant CIII
neurons [1] show decreased averaged cold-evoked firing rate and [2]diminished peak of firing rate in response to fast temperature change
relative to controls. Studying the role of ion channels subsystems
helped us classify possible regimes of activity and transitions between
them, understand the complex dynamics of the cold-sensing neuron
and putative mechanisms of encoding sensory information.
Acknowledgments: This research was supported by NIH grants
1R01NS115209 (DNC and GC) and 2R01NS086082 (DNC) and GSU B&B
Grant (DNC).
References
1. Wang L, Nomura Y, Du Y, Dong K. Differential effects of TipE and a TipEhomologous protein on modulation of gating properties of sodium chan‑nels from Drosophila melanogaster. PLoS One. 2013; 8(7): e67551.
2. Hardie RC. Voltage-sensitive potassium channels in Drosophila photorecep‑
tors. Journal of Neuroscience. 1991; 11(10): 3079-3095.

P34
Atlas‑mapped reconstruction of the cerebellar Lingula
Alice ­Geminiani1, Dimitri ­Rodarie2, Robin De ­Schepper3, Claudia­Casellato4, Egidio D’Angelo3
1
University of Pavia, Pavia, Italy; 2École polytechnique fédérale de Lausanne,Blue Brain Project, Geneva, Switzerland; 3University of Pavia, Department
of Brain and Behavioral Sciences, Pavia, Italy; 4University of Pavia,
Department of Brain and Behavioral Sciences, Unit of Neurophysiology,
Pavia, Italy
Correspondence: Alice Geminiani (alice.geminiani@unipv.it)
BMC Neuroscience 2020, 21(Suppl 1):P34
Large-scale neuronal networks are a powerful tool to investigate brain
area functionality, embedding realistic temporal dynamics of neurons,
synapses and microcircuits. However, spatial features differentiating
regions within the same brain area are crucial for proper functioningof interconnected networks. In case of cerebellum, input signals are
mapped and integrated in different regions with specific structuralproperties and local network dynamics [1].
Here we describe the reconstruction of the mouse Lingula (region of
cerebellar Vermis) mapped on data from the Allen Brain Atlas as in[2], including neuron densities and orientation vectors in cubic voxels(25um side).
Network reconstruction was based on strategies of the cerebellar scaffold [3], with new features for folded volumes. The main cerebellar
neurons were placed in the 3 layers of the cerebellar cortex, i.e. Granule and Golgi cells (GrC and GoC) in the Granular layer, Purkinje cells(PC) in the Purkinje layer, and Basket and Stellate cells in the Molecularlayer. A particle placement strategy was used for Granular and Molecular neurons, in subvolumes made up of 50 voxels each. The algorithm
included: placing the total number of neurons expected in each subvolume at random positions as repellent particles; iteratively re-positioning detected colliding particles within the subvolume; pruning﻿BMC Neurosci 2020, 21(Suppl 1):54

particles placed outside. An adhoc algorithm was developed for PCs:in each parasagittal section, they were placed in parallel arrays at aminimum distance of 130um to avoid dendritic tree overlap, using A*
search algorithm to find adjacent PC.
Orientation data were used for connectivity: each cell morphology
was rotated based on orientation of the voxel containing the soma;
connections were identified through an efficient algorithm searchingfor intersections of discretized rotated morphologies volumes. For
connections from GrCs, parallel fibers were also bended to follow theorientation field. The resulting network included about 105 neurons
and 107 connections (Fig. 1).

Page 28 of 123

References
1. Casali S, Marenzi E, Medini C, Casellato C, D’Angelo E. Reconstruction andsimulation of a scaffold model of the cerebellar network. Frontiers in Neuro‑
informatics. 2019; 13: 37.
2. Erö C, Gewaltig MO, Keller D, Markram H. A cell atlas for the mouse brain.
Frontiers in Neuroinformatics. 2018; 12: 84.
3. De Schepper, et al. A scaffold model for the cerebellum. [in prep -https://github.com/dbbs-lab/scaffold]
4. Apps R, et al. Cerebellar modules and their role as operational cerebellar
processing units. The Cerebellum. 2018; 17(5): 654-82.

P35
Through synapses to spatial memory maps: a topological model
Yuri Dabaghian
The University of Texas McGovern Medical School, Neurology, Houston,
Texas, United States of America
Correspondence: Yuri Dabaghian (yuri.a.dabaghian@uth.tmc.edu)
BMC Neuroscience 2020, 21(Suppl 1):P35

Fig. 1 Mapped reconstruction of the Lingula with the main cerebel‑
lar neurons. Rotated morphologies are reported for some PCs. The
inset panel shows more in detail the parallel arrays of PCs withrotated sample morphologies
This proposed pipeline will be generalized to other cerebellar regions
up to reconstruction of a full mouse cerebellum. The network, filledwith point or detailed neuron/synapse models, will be used to investigate spatial features of signal propagation in the cerebellum, andthe specialization and integration of sensory signals across different
regions [4]. This will allow reproducing spatially-mapped experimental data, e.g. Local Field Potentials, and embedding the cerebellum inwhole-brain frameworks built on atlases, in which multiple interconnected brain areas can be simulated even using different descriptions
for each region (e.g. hybrid models with spiking circuits and meanfield components).
The workflow, here developed for the cerebellum, could be complementary to other algorithms, and applied to other brain areas.
Acknowledgements: This work has received funding from European
Union’s Horizon 2020 Framework Programme for Research and Innovation under Grant Agreement N. 785907 (Human Brain Project SGA2)and SGA3.

Learning and memory are fundamentally collective phenomena,brought into existence by highly organized spiking activity of large
ensembles of cells. Yet, linking the characteristics of the individual
neurons and synapses to the properties of large-scale cognitive representations remains a challenge: we lack conceptual approaches
for connecting the neuronal inputs and outputs to the integrated
results at the ensemble level. For example, numerous experiments
point out that weakening of the synapses correlates with weakeningof memory and learning abilities—but how exactly does it happen? If,
for example, the synaptic strengths decrease on average by 5%, thenwill the time required to learn a particular navigation task increaseby 1%, by 5% or by 50%? How would the changes in learning capacity depend on the original cognitive state? Can an increase in learning
time, caused by a synaptic depletion, be compensated by increasing
the population of active neurons or by elevating their spiking rates?
Answering these questions requires a theoretical framework that connects the individual cell outputs and the large-scale cognitive phenomena that emerge at the ensemble level.
We propose a modeling approach that allows bridging the “semantic gap” between electrophysiological parameters of neuronal activity and the characteristics of spatial learning, using techniques fromalgebraic topology. Specifically, we study influence of synaptic transmission probability and the effects of synaptic plasticity on the hippocampal network’s ability to produce a topological cognitive mapof the ambient space. We simulate deterioration of spatial learning capacity as a function of synaptic depletion in the hippocampal
network to get a better insight into the spatial learning deficits (asobserved, e.g., in Alzheimer’s disease) and understanding why development of these deficits may correlate with changes in the numberof spiking neurons and/or of their firing rates, variations in the “brainwave” frequency spectra, etc. The results shed light on the principlesof spatial learning in plastic networks and may help our understanding of neurodegenerative conditions.
P36
Robust spatial memories encoded by transient neuronal networks
Yuri Dabaghian
The University of Texas McGovern Medical School at Houston, Neurology,
Houston, Texas, United States of America
Correspondence: Yuri Dabaghian (yuri.a.dabaghian@uth.tmc.edu)
BMC Neuroscience 2020, 21(Suppl 1):P36
The principal cells in mammalian hippocampus encode an internalized representation of the environment - the hippocampal cognitive
map, that underlies spatial memory and spatial awareness. However,the synaptic architecture of the hippocampal network is dynamic: it
contains a transient population of “cell assemblies”- functional unitsof the hippocampal computations - that emerge among the groupsof coactive neurons and may disband due to reduction or cessation of
spiking activity, then reappear, then disband again, etc. Electrophysiological studies in rats and mice suggest that the characteristic lifetimes

﻿BMC Neurosci 2020, 21(Suppl 1):54

of typical hippocampal cell assemblies range between minutes totens of milliseconds. In contrast, cognitive representations sustainedby the hippocampal network can last in rodents for months, whichraises a principal question: how can a stable large-scale representation of space emerge from a rapidly rewiring neuronal stratum? We
propose a computational approach to answering this question basedon Algebraic Topology techniques and ideas. By simulating the place
cell spiking activity during the rat’s exploratory movements throughdifferent environments and testing the stability of the resulting largescale neuronal maps, we find that the networks with “flickering” architectures can reliably capture the topology of the ambient spaces.
Moreover, the model suggests that the information is processed atthree principal timescales, which roughly correspond to the shortterm, intermediate term and the long-term memories. The rapid rewiring of the local network connections occurs at the fastest timescale.
The timescale at which the large-scale structures defining the shapeof the cognitive map may fluctuate is by about an order of magnitude
slower than the timescale of the information processing at the synaptic level. Lastly, an emerging stable topological base provides lasting,qualitative information about the environment, which remains robust
despite the ongoing transience of the local connections.
P37
A realistic spatial model of the complete synaptic vesicle cycle
Andrew Gallimore, Iain Hepburn, Erik De Schutter
Okinawa Institute of Science and Technology, Computational
Neuroscience Unit, Onna‑son, Japan
Correspondence: Andrew Gallimore (gallimore@cantab.net)
BMC Neuroscience 2020, 21(Suppl 1):P37
The release of neurotransmitters from synaptic vesicles is the fundamental mechanism of information transmission between neurons in
the brain. The entire synaptic vesicle cycle involves a highly complex
interplay of proteins that direct vesicle docking at the active zone, thedetection of intracellular calcium levels, fusion with the presynaptic
membrane, and the subsequent retrieval of the vesicle protein material for recycling [1]. Despite its central importance in many aspects ofneuronal function, and even though computational models of subcellular neuronal processes are becoming increasingly important inneuroscience research, realistic models of the synaptic vesicular cycle
are almost non-existent. This is largely because the modeling tools fordetailed spatial modeling of vesicles are not available.
Extending the STEPS simulator [2], we have pioneered spherical ‘vesicle’ objects that occupy a unique excluded volume and sweep a paththrough the tetrahedral mesh as they diffuse through the cytosol.
Our vesicles incorporate endo- and exocytosis, fusion with and budding from intracellular membranes, neurotransmitter packing, as wellas interactions between vesicular proteins and cytosolic and plasma
membrane proteins. This allows us to model all key aspects of thesynaptic vesicle cycle, including docking, priming, calcium detection
and vesicle fusion, as well as dynamin-mediated vesicle retrieval andrecycling.
Using quantitative measurements of protein copy numbers [3], membrane and cytosolic diffusion rates, protein-protein interactions, andan EM-derived spatial model of a hippocampal pyramidal neuron, weused this technology to construct the complete synaptic vesicle cycle
at the Schaffer Collateral–CA1 synapse at an unprecedented level ofspatially-realistic and biochemical detail (Fig. 1). We envisage that thisnew modeling technology will open up pioneering research into all
aspects of neural function in which synaptic transmission plays a role.

Page 29 of 123

Fig. 1 Model of a CA3-CA1 synaptic bouton with docked vesicles(blue), free vesicles (red), clustered vesicles (orange), and readilyretrievable pool (green)
References
1. Südhof TC. The molecular machinery of neurotransmitter release (Nobel lec‑ture). Angewandte Chemie International Edition. 2014; 53(47): 12696-717.
2. Hepburn I, Chen W, Wils S, De Schutter E. STEPS: efficient simulation of sto‑
chastic reaction-diffusion models in realistic morphologies. BMC Systems
Biology. 2012; 6: 36.
3. Wilhelm BG, et al. Composition of isolated synaptic boutons reveals theamounts of vesicle trafficking proteins. Science. 2014; 344(6187): 1023-8.

P38
Priors based on abstract rules modulate the encoding of pure tonesin the subcortical auditory pathway
Alejandro ­Tabas1, Glad M­ ihai1, Stefan ­Kiebel2, Robert ­Trampel3, Katharina
von ­Kriegstein4
1
Max Planck Institute for Human Cognitive and Brain Sciences, ResearchGroup in Neural Mechanisms of Human Communication, Leipzig,
Germany; 2Technische Univerität Dresden, Chair of Neuroimage, Dresden,
Germany; 3Max Planck Institute for Human Cognitive and Brain Sciences,
Department of Neurophysics, Leipzig, Germany; 4Technische Univerität
Dresden, Chair of Cognitive and Clinical Neuroscience, Dresden, Germany
Correspondence: Alejandro Tabas (alextabas@gmail.com)
BMC Neuroscience 2020, 21(Suppl 1):P38
Sensory pathways efficiently transmit information by adapting theneural responses to the local statistics of the sensory input. The predictive coding framework suggests that sensory neurons constantly
match the incoming stimuli against an internal prediction derived
from a generative model of the sensory input. Although predictive
coding is generally accepted to underlay cortical sensory processing, the role of predictability in subcortical sensory coding is still
unclear. Several studies have shown that single neurons and neuronalensembles of the subcortical sensory pathway nuclei exhibit stimulus
specific adaptation (SSA), a phenomenon where neurons adapt tofrequently occurring stimuli (standards) yet show restored responses
to a stimulus with deviating characteristics from the standard (deviant). Although neurons showing SSA are often interpreted as encoding prediction error, computational models to date have successfully
explained SSA in terms of local network effects based on synaptic
fatigue.
Here, we first introduce a novel experimental paradigm where abstract
rules are used to manipulate predictability. 19 human participants listened to sequences of pure tones consisting on seven standards andone deviant while we recorded mesoscopic responses in auditory
thalamus and auditory midbrain using 7-Tesla functional MRI. In eachsequence, the deviant was constrained to occur once and only once,
and always in locations 4, 5 or 6. Although the three locations were
equiprobable at the beginning of the trial, the conditional probabilityof hearing a deviant in location n after hearing n-1 standards is 1/3,
1/2, and 1, for deviant locations 4, 5, and 6, respectively.

﻿BMC Neurosci 2020, 21(Suppl 1):54

This paradigm yields different outcomes for habituation and predictive coding: if adaptation is driven by local habituation only, thethree deviants should elicit similar neuronal responses; however, if itis predictive coding that entails adaptation, the neuronal responses
to each deviant should depend on their abstract predictability. Ourdata showed that the responses to the deviants were strongly driven
by abstract expectations, indicating that predictive coding is the main
mechanism underlying mesoscopic SSA in the subcortical pathway.
These results are robust even at the single-subject level.
Next, we developed a new model of pitch encoding for pure tones following the main directives of predictive coding. The model comprises
two layers whose dynamics reflect two different levels of abstraction.
The lower layer receives its inputs from the auditory nerve and makes
use of the finite bandwidth of the peripheral filters to decode pitch
fast and robustly. The second layer holds a sparse representation that
integrates the activity in the first layer only once the pitch decision
has been made. Top-down afferents from the upper layer reinforce
the pitch decision and accept the inclusion of priors that facilitate thedecoding of predictable tones.
Without the inclusion of priors, the model explains the key elementsof SSA in animal recordings at the single-neuron level, as well as themain phenomenology of its mesoscopic representation. The inclusion of priors reflecting the abstract rules described in our paradigm
facilitates the decoding of tones according to their predictability,
effectively modulating the responses at the mesoscopic level. This
modulation affects the mesoscopic fields generated during pitch
encoding, fully explaining our experimental data.

Fig. 1 Top: z-scores of the BOLD responses to standards and deviantsin the left auditory thalamus. Similar responses were recorded in theright auditory thalamus and bilateral auditory midbrain. Bottom: aver‑age model predictions under the habituation only (without informed
priors) and predictive coding (with informed priors) scenarios

Page 30 of 123

P39
Simulating cell to cell interactions during the cerebellar
development
Mizuki Kato, Erik De Schutter
Okinawa Institute of Science and Technology, Computational
Neuroscience Unit, Onna‑son, Japan
Correspondence: Mizuki Kato (mizuki.kato@oist.jp)
BMC Neuroscience 2020, 21(Suppl 1):P39
The cerebellum is involved in both motor and non-motor functions in
the brain. Any deficit during its development has been suggested totrigger ataxia as well as various psychiatric disorders.
During the development of both human and mouse cerebella, precursors of one of the main excitatory neurons, granule cells, first accumulate in the external granule layer on the surface and subsequently
migrate down to the bottom of cerebellar cortex. In addition to themassive soma migration, these granule cell precursors also descendtheir axons through the migratory paths which further branch into
parallel fibers, making the environment even more crowded. Althoughpalisade-like Bergmann glia physically guide granule cells during the
migration, mechanisms about how these two cell types interact tomanage the migration through such a shambolic environment are still
unclear.
Rodent cerebella have been widely used as subjects in experimental
studies, and have provided great pictures of granule cells and Bergmann glia. However, technical limitations still hinder the observationof cerebellar development both as populations and in a continuous
manner. Building a computational model by a reverse-engineering
process which integrates available biological observations will beessential to point out differences in the developmental dynamics
between normal and abnormal cerebellum.
Most computational models for simulating neuronal development
have focused on intracellular factors of single cell types. Althoughmodels simulating limited environmental factors exist, models for cellcell interactions during neuronal development are rare. Alternatively,
we used new computational framework, NeuroDevSim, to simulate
populations of granule cells and Bergmann glia during cerebellar
development.
NeuroDevSim evolved from NeuroMaC [1] and so far is the only active
software that can simultaneously simulate developmental dynamics
of different types of neurons at population-scale.
The goal structure of simulation with NeuroDevSim comprises 3,000
granule cells and 200 Bergmann glia in a 1x106µm3 regular cube, calculated by assuming a cube of mice cerebellar cortex. 26 Purkinje cellsomas are also introduced as interfering spherical objects. Their dendritic development will be included in the future. At current stage ofthe simulation, reduced systems are used, aiming to direct the trafficof granule cell somas and to navigate their axonal growth.
The resulted model will enable visualization of massive migration
dynamics of cerebellar granule cells with growing parallel fibers and of
their phenomenological interactions with Bergmann glia. This model
will provide new insight to understand developmental dynamics of
cerebellar cortex.
Reference
1. Torben-Nielsen B, De Schutter E. Context-aware modeling of neuronalmorphologies. Frontiers in Neuroanatomy. 2014; 8: 92.

P40
Modeling multi‑state molecules with a pythonic STEPS interface
Jules Lallouette, Erik De Schutter
Okinawa Institute of Science and Technology, Computational
Neuroscience Unit, Onna‑son, Japan
Correspondence: Jules Lallouette (jules.lallouette@oist.jp)
BMC Neuroscience 2020, 21(Suppl 1):P40
Molecules involved in biological signaling pathways can, in somecases, exist in a very high number of different functional states. Wellstudied examples include the Ca2+/calmodulin dependent protein
kinase II (CaMKII), or receptors from the ErbB family. Through a combination of binding sites, phosphorylation sites, and polymerization,

﻿BMC Neurosci 2020, 21(Suppl 1):54

these molecules can form complexes that reach exponentially increasing numbers of distinct states. This phenomenon of combinatorial
explosion is a common obstacle when trying to establish detailed
models of signaling pathways.
Classical approaches to the stochastic simulation of chemical reactions require the explicit characterization of all reacting species and all
associated reactions. This approach is well suited to population based
methods in which there are a relatively low number of different molecules that can be present at relatively high concentrations. Since eachstate of multi-state complexes would however have to be modeled asa distinct specie, the combinatorial explosion that we mentioned earlier makes these approaches inapplicable.
Two separate problems need to be tackled: the “specification problem” which requires a higher level of abstraction in the definitionof complexes and reactions; and the “computation problem” that
requires efficient methods to simulate the time evolution of reactions
involving multi-state complexes. Rule based modeling (RBM) [1] tackles the former problem by allowing modelers to write “template” reactions that only contain the parts of the complexes actually involvedin the reaction. Network-free methods together with particle-based
methods [2] usually tackle the latter problem by only considering
the states and reactions that are accessible from the current complex
states and thus avoiding the computation of the full reaction network.
STEPS is a spatial stochastic reaction-diffusion simulation softwarethat implements population based methods to simulate reactiondiffusion processes on realistic tetrahedral meshes [3]. In an effort to
tackle the “specification problem” in STEPS, we present in this poster anovel, more pythonic, interface to STEPS that allows intuitive declaration of both classical and multi-state complexes reactions. Significant
emphasis was put on simplifying model declaration, data access, anddata saving during simulations.
To specifically tackle the “computation problem” in STEPS, we present a hybrid population/particle based method to simulate reactions
involving multi-state complexes. By preventing the formation of arbitrarily structured macromolecules, we lower the computational cost of
the pattern matching step necessary to identify potential reactants [4].
This diminished computational cost allows us to simulate larger spatial
systems. We discuss these performance improvements and present
examples of stochastic spatial simulations involving 12 subunits CamKII complexes which would have previously been intractable in STEPS.
References
1. Chylek LA, Stites EC, Posner RG, Hlavacek WS. Innovations of the rule-basedmodeling approach. Systems Biology. 2013; 273-300.
2. Tapia JJ, et al. Mcell-r: A particle-resolution network-free spatial modelingframework. Modeling Biomolecular Site Dynamics. 2019; 203-229.
3. Hepburn I, Chen W, Wils S, De Schutter E. STEPS: efficient simulation of sto‑
chastic reaction–diffusion models in realistic morphologies. BMC Systems
Biology. 2012; 6(1): 1-9.
4. Blinov ML, Yang J, Faeder JR, Hlavacek WS. Graph theory for rule-basedmodeling of biochemical networks. Transactions on Computational Sys‑
tems Biology VII. 2006; 89-106.

P41
Reaction‑diffusion simulations of astrocytic Ca2+ signals in realistic
geometries
Audrey ­Denizot1, Corrado Calì2, Weiliang ­Chen1, Iain H­ epburn1, Hugues­Berry3, Erik De ­Schutter1
1
Okinawa Institute of Science and Technology, Computational
Neuroscience Unit, Onna‑son, Japan; 2Neuroscience Institute Cavalieri
Ottolenghi, University of Turin, Department of Neuroscience, Turin, Italy;
3
INRIA, University of Lyon, LIRIS, UMR5205 CNRS, Villeurbanne, France
Correspondence: Audrey Denizot (audrey.denizot2@oist.jp)
BMC Neuroscience 2020, 21(Suppl 1):P41
Astrocytes, glial cells of the central nervous system, display a striking diversity of Ca2+ signals in response to neuronal activity. 80% of
those signals take place in cellular ramifications that are too fine tobe resolved by conventional light microscopy [1], often in apposition
to synapses (perisynaptic astrocytic processes, PAPs). Understanding Ca2+ signaling in PAPs, where astrocytes potentially regulate

Page 31 of 123

neuronal information processing [2], is crucial. At this spatial scale,
Ca2+ signals are not distributed uniformly, being preferentiallylocated in so-called Ca2+ hotspots [3], suggesting the existence ofsubcellular spatial domains. However, because of the spatial scale atstake, little is currently known about the mechanisms that regulate
Ca2+ signaling in fine processes. Here, we investigate the geometryof the endosplamic reticulum (ER), the predominant astrocytic Ca2+
store, using electron microscopy. Contrary to previous reports [4],we detect ER in PAPs, which can be as close as ~60nm to the closest
postsynaptic density. We use computational modeling to investigate the impact of the observed cellular and ER geometries on Ca2+
signaling. Simulations using the stochastic voxel-based model from
Denizot et al [5], both in simplified and in realistic 3D geometries,
reproduce spontaneous astrocytic microdomain Ca2+ transientsmeasured experimentally. In our simulations, the effect of the clustering of IP3R channels observed in 2 spatial dimensions [5] is still valid
in a simple cylinder geometry but no longer holds in complex realistic
geometries. We propose that those discrepancies might result fromthe geometry of the ER and that, in 3 spatial dimensions, the effects
of molecular distributions (such as IP3R clustering) are particularly
enhanced at ER-plasma membrane contact sites. Our results suggestthat the predictions from simulations in 1D, 2D or simplified 3D geometries should be cautiously interpreted. Overall, this work provides abetter understanding of IP3R-dependent Ca2+ signals in fine astrocytic processes and more generally in subcellular compartments, aprerequisite for understanding the dynamics of Ca2+ hotspots, which
are deemed essential for local intercellular communication.
References
1. Bindocci E, et al. Three-dimensional Ca2+ imaging advances understand‑ing of astrocyte biology. Science. 2017; 356(6339).
2. Savtchouk I, Volterra A. Gliotransmission: Beyond Black-and-White. Journalof Neuroscience. 2018; 38(1): 14–25.
3. Thillaiappan NB, Chavda AP, Tovey SC, Prole DL, Taylor CW. Ca 2+ signals
initiate at immobile IP 3 receptors adjacent to ER-plasma membrane junc‑
tions. Nature Communications. 2017; 8(1): 1-6.
4. Patrushev I, Gavrilov N, Turlapov V, Semyanov A. Subcellular location ofastrocytic calcium stores favors extrasynaptic neuron–astrocyte communi‑
cation. Cell calcium. 2013; 54(5): 343-9.
5. Denizot A, Arizono M, Nägerl UV, Soula H, Berry H. Simulation of calcium
signaling in fine astrocytic processes: Effect of spatial properties on sponta‑
neous activity. PLoS Computational Biology. 2019; 15(8): e1006795.

P42
A nanometer range reconstruction of the Purkinje cell dendritic tree
for computational models
Mykola Medvidov, Weiliang Chen, Christin Puthur, Erik De Schutter
Okinawa Institute of Science and Technology, Computational
Neuroscience Unit, Onna‑son, Japan
Correspondence: Mykola Medvidov (mykola.medvidov@gmail.com)
BMC Neuroscience 2020, 21(Suppl 1):P42
Purkinje neurons are used extensively in computational neuroscience[1]. However, despite extended knowledge about Purkinje cell morphology and ultrastructure, the complete dendritic tree of Purkinje
cell as well as the complete dendritic tree of other types of neurons
was never reconstructed at nanometer range resolution due to thecells size and complexity. At the same time, the use of real Purkinje
cell dendritic tree morphology may be very important for computational models. Considering the development of new instruments andimaging techniques that nowadays allow reconstruction of large volumes of the neuronal tissue, the main goal of our project is to reconstruct a dendritic tree of a Purkinje cell with all its dendritic spines andsynapses.
Serial Block Face Microscope (SBF) is widely used to examine large
volume of neuronal tissue with nanometer range resolution [2]. To
obtain volume data, perfused mouse brains were processed for SBF
imaging using OTO staining techniques and the best quality cerebellum slice was imaged on FEI Teneo VS Electron Microscope with pixel
resolution 8x8x60 nm. An imaged volume of approximately 2.2 Terapixel was processed and aligned with Image J and Adobe Photoshop.

﻿BMC Neurosci 2020, 21(Suppl 1):54

To reconstruct the Purkinje cell dendritic tree the imaged volume was
first analyzed to locate the most appropriate full cell inside the imaged
volume. Second, the volume containing the cell was segmented with
Ilastik [https://www.ilastik.org] and Tensor Flow deep learning network [https://github.com/tensorflow]. The super-pixels were fusedwith custom made software to generate a dendritic tree represented
by 3d voxels. Next, a 3d surface mesh was generated based on 3d
voxels array using the marching cubes algorithm [https://github.com/
ilastik/marching_cubes] and the resulting mesh was processed with
MeshLab to generate a final surface mesh. Finally, a tetrahedral volume mesh was generated with the TetWild software [https://github.
com/Yixin-Hu/TetWild]. The resulting tetrahedral mesh of Purkinje cell
full dendritic tree including cell body and initial axonal segment will
be used to run large scale stochastic models using the parallel STochastic Engine for Pathway Simulation [3] (STEPS) [http://steps.sourceforge.net].
References
1. Zang Y, Dieudonne S, De Schutter E. Voltage- and Branch-Specific Climbing
Fiber Responses in Purkinje Cells. Cell Reports. 2018; 24, 1536–1549.
2. Titze B, Genoud C. Volume scanning electron microscopy for imaging
biological ultrastructure. Biology of the Cell 2016;108, 307-323.
3. Chen W, De Schutter E. Parallel STEPS: Large Scale Stochastic Spatial
Reaction-Diffusion Simulation with High Performance Computers. Frontiersin Neuroinformatics 2017; 11, 1-15.

P43
Responses of a Purkinje cell model to inhibition and excitation
Gabriela C Rangel, Erik De Schutter
Okinawa Institute of Science and Technology, Computational
Neuroscience Unit, Onna‑son, Japan
Correspondence: Gabriela C Rangel (gabriela‑capo@oist.jp)
BMC Neuroscience 2020, 21(Suppl 1):P43
Although the effects of inhibition on Purkinje cells have first been
observed over five decades ago and have since then been intensively
studied, the manner in which the cerebellar output is regulated byboth inhibitory and excitatory cells has yet to be fully understood.
Purkinje cells represent the sole output of the cerebellar cortex andare known to fire simple spikes as a result of the integrated excitatory and inhibitory synaptic input originating from parallel fibers andthe interneurons in the molecular layer. When studied in vivo, bothPurkinje cells and interneurons exhibit a highly irregular pattern inthe firing of action potentials. The mechanisms underlying the complex interaction between the intrinsic properties of the membrane
and the pattern of synaptic inputs that generate the cerebellar output have not yet been completely understood. Recent literature has
underlined the importance of the inhibitory interneurons (stellate andbasket cells) in shaping the simple spikes of Purkinje cells. Moreover,when inhibitory interneurons are eliminated and only asynchronous
excitation is taken into account, numerous computational [1] andexperimental work have reported unrealistic behavior such as very
little variability between the spiking intervals, as well as very small
minimum firing frequencies. The modeling approach we propose here
focuses on analyzing the effects that combined inhibition and excitation have on the shape of action potential, on the firing frequency andon the time intervals in between the simple spikes. The starting pointof our work was a very detailed Purkinje cell model proposed by Zang
et al in [2]. Instead of varying somatic holding currents as in previous
work, in here, the dendritic voltage states are determined by the balance between the frequency of inhibitory cells and the frequency ofparallel fibers. Our preliminary results indicate that inhibition presents
both subtractive and divisive behavior, depending on stellate cells frequency. We discuss in detail the different shapes of firing we obtained.
In particular, our results capture not only simple spikes but also a trimodal firing pattern, previously observed experimentally in [3]. This
trimodal firing pattern is a characteristic of mature Purkinje cells andis given by a mixture of three different phases: tonic firing, burstingand silent mode. We mapped the regions in which simple spiking
occur and the regions in which simple spikes appear and we further

Page 32 of 123

investigate the role of the SK2 channels in eliminating or prolonging
the trimodal pattern.
References
1. De Schutter ER, Bower JM. An active membrane model of the cerebellar
Purkinje cell II. Simulation of synaptic responses. Journal of neurophysiol‑ogy. 1994; 71(1): 401-19.
2. Zang Y, Dieudonné S, De Schutter E. Voltage-and branch-specific climbing
fiber responses in Purkinje cells. Cell reports. 2018; 24(6): 1536-49.
3. Womack MD, Khodakhah K. Somatic and dendritic small-conductancecalcium-activated potassium channels regulate the output of cerebellar
Purkinje neurons. Journal of Neuroscience. 2003; 23(7): 2600-7.

P44
3D modeling of Purkinje cell activity
Alexey Martyushev, Erik De Schutter
Okinawa Institute of Science and Technology, Computational
Neuroscience Unit, Onna‑son, Japan
Correspondence: Alexey Martyushev (martyushev.alexey@gmail.com)
BMC Neuroscience 2020, 21(Suppl 1):P32
The NEURON software remains the main neural physiology modeling
tool for scientists. Its computational methods benefit from deterministic approximations of the cable equation solutions and 1-dimensionalradial calcium diffusion in cylindrical neuron morphologies [1]. However, in real neurons ions diffuse in 3-dimensional volumes [2] andmembrane channels get activated in a stochastic manner. Furthermore, NEURON does not suit to model nano-sized spine morphology.
In contrast, the Stochastic Engine for Pathway Simulation (STEPS) uses
fully stochastic 3-dimensional methods in tetrahedral morphologies
that can provide realistic modeling of neurons at the nanoscale [3, 4].
In this work, we compare the modeling results between those twoenvironments for the Purkinje cell model developed by Zang et al. [5].
This model considers a variety of calcium, potassium and sodium
channels, and the resulting calcium concentrations affecting themembrane potential of a Purkinje cell. The results demonstrate that: (i)the used cylinder light microscopy morphology can not be identicallytransformed into a 3D mesh; (ii) the effect of stochastic channel activation determines the timing of membrane potential spikes; (iii) the
kinetics of calcium activated potassium channels strongly depends on
the specified sub-membrane volumes in both environments.
A further step in developing the model will be integration of a digital microscopy reconstruction of spines to the existing 3D tetrahedral
mesh.
References
1. Carnevale NT, Hines ML. The NEURON book. Cambridge University Press;
2006 Jan 12.
2. Anwar H, Roome CJ, Nedelescu H, Chen W, Kuhn B, De Schutter E. Dendritic
diameters affect the spatial variability of intracellular calcium dynamics incomputer models. Frontiers in cellular neuroscience. 2014; 8: 168.
3. Hepburn I, Chen W, Wils S, De Schutter E. STEPS: efficient simulation of sto‑
chastic reaction–diffusion models in realistic morphologies. BMC systems
biology. 2012; 6(1): 1-9.
4. Chen W, De Schutter E. Time to bring single neuron modeling into 3D. 2017.
5. Zang Y, Dieudonné S, De Schutter E. Voltage-and branch-specific climbing
fiber responses in Purkinje cells. Cell reports. 2018; 24(6): 1536-49.

P45
Exploring relevant spatiotemporal scales for analyses of brain
dynamics
Xenia ­Kobeleva1, Ane López‑González2, Morten L K­ ringelbach3, Gustavo­Deco4
1
University of Bonn, Department of Neurology, Bonn, Germany; 2Universitat
Pompeu Fabra, Computational Neuroscience Group, Barcelona, Spain;
3
University of Oxford, Department of Psychiatry, Oxford, United Kingdom;
4
Universitat Pompeu Fabra, Barcelona, Spain
Correspondence: Xenia Kobeleva (xkobeleva@gmail.com)
BMC Neuroscience 2020, 21(Suppl 1):P45

﻿BMC Neurosci 2020, 21(Suppl 1):54

Introduction: The brain switches between cognitive states at a highspeed by rearranging interactions between distant brain regions.
Using analyses of brain dynamics neuroimaging researchers were able
to further describe this dynamical brain-behavior relationship. However, the diversity of methodological choices for the brain dynamics
analyses impedes comparisons between studies of brain dynamics,
reducing their reproducibility and generalizability. A key choice constitutes deciding on the spatiotemporal scale of the analysis, which
includes both the number of regions (spatial scale) as well as the sampling rate (temporal scale). Choosing a suboptimal scale might either
lead to loss of information or inefficient analyses with increase ofnoise. Therefore, the aim of this study was to assess the effect of different spatiotemporal scales on analyses of brain dynamics and to determine which spatiotemporal scale would retrieve the most relevant
information on dynamic spatiotemporal patterns of brain regions.
Methods: We compared the effect of different spatiotemporal scales
on the information content of the evolution of spatiotemporal patterns using empirical as well as simulated timeseries. Empirical timeseries were extracted from the Human Connectome Project [1]. We
then created a whole-brain mean-field model of neural activity [2]resembling the key properties of the empirical data by fitting the
global synchronization level and measures of dynamical functional
connectivity. This resulted in different spatiotemporal with spatial
scales from 100 to 900 regions and varying temporal scales from milliseconds to seconds. With a variation of an eigenvalue analysis [3], we
estimated the number of spatiotemporal patterns over time and thenextracted these patterns with an independent component analysis.
The evolution of these patterns was then compared between scales inregard to the richness of switching activity (corrected for the numberof patterns in total) using the measure of entropy. Given the probability of the occurrence of a pattern over time, we defined the entropy as
a function of the probability of patterns.
Results: Using the entropy measure, we were able to specify both
optimal and temporal scales for the evolution of spatiotemporal patterns. The entropy followed an inverted U-shaped function with thehighest value at an intermediate parcellation of n = 300. The entropy
was highest at a temporal scale of around 200ms.
Conclusions and discussion: We have investigated which spatiotemporal scale contained the highest information content for brain
dynamics analyses. By combining whole-brain computational modelling with an estimation of the number of resulting patterns, we wereable to analyze whole-brain dynamics in different spatial and temporal scales. From a probabilistic perspective, we explored the entropyof the probability of resulting brain patterns, which was highest at aparcellation of n = 300. Our results indicate that although more spatiotemporal patterns with increased heterogeneity are found withhigher parcellations, the most relevant information on brain dynamics is captured when using a spatial scale of n = 200 and a temporalscale of 200ms. Our results therefore provide guidance for researchers on choosing the optimal spatiotemporal scale in studies of brain
dynamics.
References
1. 1.Van Essen DC, Smith SM, Barch DM, Behrens TE, Yacoub E, Ugurbil K,
Wu-Minn HCP Consortium. The WU-Minn human connectome project: an
overview. Neuroimage. 2013; 80: 62-79.
2. Deco G, et al. Resting-state functional connectivity emerges from structur‑
ally and dynamically shaped slow linear fluctuations. Journal of Neurosci‑
ence. 2013; 33(27): 11239-52.
3. Deco G, Cruzat J, Kringelbach ML. Brain songs framework used for discover‑ing the relevant timescale of the human brain. Nature Communications.
2019; 10(1): 1-3.

P46
Faster gradient descent learning
Ho Ling L­ i1, Mark van R­ ossum2
1
University of Nottingham, School of Psychology, Nottingham, United
Kingdom; 2University of Nottingham, School of Psychology and Schoolof Mathematical Sciences, Nottingham, United Kingdom
Correspondence: Ho Ling Li (holing.li@nottingham.ac.uk)
BMC Neuroscience 2020, 21(Suppl 1):P46

Page 33 of 123

Back-propagation is a popular machine learning algorithm that uses
gradient descent in training neural networks for supervised learning. In stochastic gradient descent a cost function C is minimized byadjusting the weights wij as Δwij= -η(∂C/∂wij) at every training sample. However, learning with back-propagation can be very slow. A
number of algorithms have been developed to speed up convergenceand improve robustness of the learning. One way is to start with ahigh learning rate and anneal it to lower values at the end of learning. Other approaches combine past updates with the current weight
update, such as momentum [1] and Adam [2]. These algorithms arenow standard in most machine learning studies, but are complicatedto implement biologically.
Inspired by synaptic competition in biology, we have come up witha simple and local gradient descent optimization algorithm that can
reduce training time, with no demand on past information. Our algorithm works similarly to the traditional gradient descent used in backpropagation, except that instead of having a uniform learning rate
across all synapses, the learning rate depends on the current connection weights of individual synapses and the L2norm of the weights ofeach neuron.
Our algorithm encourages neurons to form strong connections
to a handful of neurons of their neighbouring layers by assigninghigher learning rateηijto synapses with bigger weights wij: Δwij=-η0(|wij|+α)/(||wj||+α)(∂C/∂wij), where i represents the indices of the
post-synaptic neurons and j represents the indices of the pre-synaptic
neurons. The parameter α is set at the range of values such that at the
beginning of training α > ||wj|| ≫ wij so that all synapses have learning rate close to η0. As learning progresses, the learning rate of large
synapses stays close to η0, while the learning rate of small synapses
decreases. Here, ||wj|| is summing over all the post-synaptic weights
of a pre-synaptic neuron, leading to each pre-synaptic neuron having
strong connections to a limited amount of post-synaptic neurons only.
However, our algorithm also works by replacing this term with ||wi||,which promotes every post-synaptic neuron to form strong connections to small number of pre-synaptic neurons instead. We note thatthe proposed modulation of learning can easily be imagined to occur
in biology, as it only requires post-synaptic factors and requires nomemory.
We have tested our algorithm with back-propagation networks with
one hidden layer consisting of 100 units to classify the MNIST handwritten digit dataset with 96% accuracy. Compared to networks
equipped with the best constant learning rate, networks train 24%
faster with our algorithm. The improvement is even greater withsmaller networks: with 50 units in the hidden layer, our algorithm
shortens the training time by 40% with respect to the best constantlearning rate. Preliminary results also show that our algorithm is comparable to Adam for the small networks that we have tested. Thus, ouralgorithm has shown the possibility of a local and biological gradientdescent optimization algorithm that only requires online information.
References
1. Plaut D, Nowlan S, Hinton G. Experiments on Learning by Back Propagation.
Technical Report CMU-CS-86-126. 1986.
2. Kingma DP, Ba J. Adam: A method for stochastic optimization. arXiv pre‑print: 1412.6980. 2014.

P47
Brain dynamics and structure‑function relationships via spectral
factorization and the transfer function
James ­Henderson1, Peter R­ obinson1, Mukesh ­Dhamala2
1
The University of Sydney, School of Physics, Sydney, Australia; 2Georgia
State University, Department of Physics and Astronomy, Atlanta, Georgia,
United States of America
Correspondence: James Henderson (james.henderson@sydney.edu.au)
BMC Neuroscience 2020, 21(Suppl 1):P47
The relationships between brain activity and structure are of central
importance to understanding how the brain carries out its functions
and to interrelating and predicting different kinds of experimental
measurements. The aim of this work is to first describe the transfer
function and its relationships to many existing forms of brain analysis,

﻿BMC Neurosci 2020, 21(Suppl 1):54

and then to describe methods for obtaining the transfer function, withemphasis on spectral factorization using the Wilson algorithm [1,2]applied to correlations of time series measurements.
The transfer function of a system contains complete information
about its linear properties, responses, and dynamics. This includes
relationships to impulse responses, spectra, and correlations. In thecase of brain dynamics, it has been shown that the transfer function
is closely related to brain connectivity, including time delays, and wenote that linear coupling is widely used to model the spatial interactions of locally nonlinear dynamics.
It is shown how the brain’s linear transfer function provides a meansof systematically analyzing brain connectivity and dynamics, providing a robust way of inferring connectivity, and activity measures suchas spectra, evoked responses, coherence and causality, all of which arewidely used in brain monitoring. Additionally, the eigenfunctions of
the transfer function are natural modes of the system dynamics andthus underlie spatial patterns of excitation in the cortex. Thus, thetransfer function is a suitable object for describing and analyzing thestructure-function relationship in brains.
The Wilson spectral factorization algorithm is outlined and used toefficiently obtain linear transfer functions from experimental twopoint correlation functions. Criteria for time series measurements
are described for the algorithm to accurately reconstruct the transfer
function, including comparing the algorithm’s theoretical computational complexity with empirical runtimes for systems of similar size tocurrent experiments. The algorithm is applied to a series of examples
of increasing complexity and similarity to real brain structure in orderto test and verify that it is free of numerical errors and instabilities (andmodifying the method where required to ensure this). The results ofapplying the algorithm to a 1D test case with asymmetry and timedelays is shown in (Fig. 1). The method is tested on increasingly realistic structures using neural field theory, introducing time delays, asymmetry, dimensionality, and complex network connectivity to verify thealgorithm’s suitability for use on experimental data.

Fig. 1 Reconstruction of the transfer function and propagator using
the Wilson algorithm from a correlation matrix for a 1D test case withasymmetry and time delays. a The original (black lines) and recon‑
structed (colored lines) transfer functions as a function of space, forfour time delays. b As for a, but for the system propagator. c The realpart of the original (black line) and re constructed (red line) transfer
function as a function of frequency. d As for c, but for the systempropagator. e The imaginary part of the original (black line) andreconstructed (red line) transfer function as a function of frequency. f
As for (e), but for the system propagator

Page 34 of 123

Acknowledgements: This work was supported by the Australian
Research Council under Center of Excellence grant CE140100007 andLaureate Fellowship grant FL140100025.
References
1. Dhamala M, Rangarajan G, Ding M. Estimating Granger causality from
Fourier and wavelet transforms of time series data. Physical Review Letters.
2008; 100(1): 018701.
2. Wilson, GT. The Factorization of Matrical Spectral Densities. SIAM Journal on
Applied Mathematics. 1972; 23: 420.

P48
Large‑scale spiking network models of primate cortex as researchplatforms
Sacha J van A­ lbada1,2, Aitor Morales‑Gregorio1,3, Alexander van­Meegen1,3, Jari P­ ronold1,3, Agnes Korcsak‑Gorzo1,3, HannahVollenbröker1,4, Rembrandt B­ akker1,5, Stine B ­Vennemo6, Håkon B Mørk6,
Jasper ­Albers1,3, Hans E P­ lesser1,6, Markus D­ iesmann1,7,8
1
Jülich Research Centre, Institute of Neuroscience and Medicine (INM‑6),
Institute for Advanced Simulation (IAS‑6), and JARA Institute Brain
Structure‑Function Relationships (INM‑10), Jülich, Germany; 2Instituteof Zoology, University of Cologne, Cologne, Germany; 3RWTH Aachen
University, Aachen, Germany; 4Heinrich Heine University Düsseldorf,
Düsseldorf, Germany; 5Radboud University, Donders Institute for Brain,
Cognition and Behavior, Nijmegen, Netherlands; 6Norwegian Universityof Life Sciences, Faculty of Science and Technology, Ås, Norway;
7
RWTH Aachen University, Department of Psychiatry, Psychotherapy
and Psychosomatics, Medical Faculty, Aachen, Germany; 8RWTH Aachen
University, Department of Physics, Faculty 1, Aachen, Germany
Correspondence: Sacha J van Albada (s.van.albada@fz‑juelich.de)
BMC Neuroscience 2020, 21(Suppl 1):P48
Despite the wide variety of available models of the cerebral cortex, aunified understanding of cortical structure, dynamics, and function at
different scales is still missing. Key to progress in this endeavor will be
to bring together the different accounts into unified models. We aim
to provide a stepping stone in this direction by developing large-scale
spiking neuronal network models of primate cortex that reproduce
a combination of microscopic and macroscopic findings on corticalstructure and dynamics. A first model describes resting-state activity inall vision-related areas in one hemisphere of macaque cortex [1,2], representing each of the 32 areas with a 1 m­ m2 microcircuit [3] with thefull density of neurons and synapses. Comprising about 4 million leaky
integrate-and-fire neurons and 24 billion synapses, it is simulated on
the Jülich supercomputers. The model has recently been ported to
NEST 3, greatly reducing the construction time. The inter-area connectivity is based on axonal tracing [4] and predictive connectomics[5]. Findings reproduced include the spectrum and rate distributionof V1 spiking activity [6], feedback propagation of activity across thevisual hierarchy [7], and a pattern of functional connectivity between
areas as measured with fMRI [8]. The model is available open-source[https://inm-6.github.io/multi-area-model/] and uses the tool Snakemake [9] for formalizing the workflow from the experimental data tosimulation, analysis, and visualization. It serves as a platform for further developments, including an extension with motor areas [10] forstudying visuo-motor interactions, incorporating function using alearning-to-learn framework [11], and creating an analogous model ofhuman cortex [12]. It is our hope that this work will contribute to anincreasingly unified understanding of cortical structure, dynamics, andfunction.
Acknowledgments: EU Grants 269921 (BrainScaleS), 604102 (HBP
SGA1), 785907 (HBP SGA2), HBP SGA ICEI 800858; VSR computation
time grant JINB33; DFG SPP 2041.
References
1. Schmidt M, Bakker R, Hilgetag CC, Diesmann M, van Albada SJ. Multi-scaleaccount of the network structure of macaque visual cortex. Brain Structure
and Function. 2018; 223(3): 1409-35.
2. Schmidt M, Bakker R, Shen K, Bezgin G, Diesmann M, van Albada SJ. A
multi-scale layer-resolved spiking network model of resting-state dynamics

﻿BMC Neurosci 2020, 21(Suppl 1):54

in macaque visual cortical areas. PLoS Computational Biology. 2018; 14(10):e1006359.
3. Potjans TC, Diesmann M. The cell-type specific cortical microcircuit: relating
structure and activity in a full-scale spiking network model. Cerebral cortex.
2014; 24(3): 785-806.
4. Bakker R, Wachtler T, Diesmann M. CoCoMac 2.0 and the future of tracttracing databases. Frontiers in Neuroinformatics. 2012; 6: 30.
5. Hilgetag CC, Beul SF, van Albada SJ, Goulas A. An architectonic type princi‑
ple integrates macroscopic cortico-cortical connections with intrinsic corti‑
cal circuits of the primate brain. Network Neuroscience. 2019; 3(4): 905-23.
6. Chu CC, Chien PF, Hung CP. Tuning dissimilarity explains short distancedecline of spontaneous spike correlation in macaque V1. Vision Research.
2014; 96: 113-32.
7. Nir Y, et al. Regional slow waves and spindles in human sleep. Neuron. 2011;
70(1): 153-69.
8. Babapoor-Farrokhran S, Hutchison RM, Gati JS, Menon RS, Everling S. Func‑
tional connectivity patterns of medial and lateral macaque frontal eye fields
reveal distinct visuomotor networks. Journal of Neurophysiology. 2013;
109(10): 2560-70.
9. Köster J, Rahmann S. Snakemake—a scalable bioinformatics workflow
engine. Bioinformatics. 2012; 28(19): 2520-2.
10. Morales-Gregorio A, et al. Bernstein Conference. 2019;https://abstracts.g-node.org/conference/BC19/abstracts#/
uuid/0e114351-af44-4cc8-b457-acb41f9064e6
11. Korcsak-Gorzo A, et al. Bernstein Conference. 2019; https://
abstracts.g-node.org/conference/BC19/abstracts#/uuid/
e28680aa-eb4c-448e-b2a8-2e0b32c2ff49
12. Pronold J, Bakker R, Morales-Gregorio A, van Albada S, van Meegen A.
Multi-area spiking network models of macaque and human cortices. In
NEST Conference 2019 (No. FZJ-2019-04472). Computational and Systems
Neuroscience.

P49
Electro‑diffusion of ions in dendritic signal transduction
Yinyun ­Li1, Alexander ­Dimitrov2
1
Beijing Normal University, Department of Management, Beijing, China;
2
Washington State University Vancouver, Vancouver, Washington, UnitedStates of America
Correspondence: Yinyun Li (leeyinyun@gmail.com)
BMC Neuroscience 2020, 21(Suppl 1):P49
Electrical dynamics of cellular membranes is central to our understanding of information processing in neurons. Until recently, thephysics of ionic dynamics was largely ignored [1, 2]. Indeed, ionicconcentrations are usually not significantly altered by the membrane
conductance. However, their effects may be sizeable when the intracellular volume is relatively small [3, 4]. More importantly, a suddenchange in concentration at one location may lead to gradients of ionicconcentrations within a neural process. In our work, we demonstratesome realistic neural processes in which this effect is significant. The
Nernst-Planck equation of electro-diffusion was applied to a dendrite,
and voltage-gated potassium and sodium channels are added into
the system. The difference of dynamics of ions and membrane voltage between the condition with electro-diffusion and without electrodiffusion were collected and compared. We found the voltage is themain driving force for the membranous ion fluxes, and the feed backloop from ion concentrations to the membrane voltage may dramatically change the dynamics of the membrane voltage. When voltagegated calcium influx and electro-diffusion was added into the system,
the dynamics of potassium becomes dramatically different from thecase without electro-diffusion. We conclude that the electro-diffusionof ions in a small volume may significantly change neural information
processing in a non-linear effect.
Acknowledgements: This work is supported by the China ScholarshipCouncil.

Page 35 of 123

References
1. Savtchenko LP, Poo MM, Rusakov DA. Electrodiffusion phenomena inneuroscience: a neglected companion. Nature Reviews Neuroscience. 2017;18(10): 598.
2. Solbrå A, et al. A Kirchhoff-Nernst-Planck framework for modeling large
scale extracellular electrodiffusion surrounding morphologically detailed
neurons. PLoS Computational Biology. 2018; 14(10): e1006510.
3. Qian N, Sejnowski TJ. An electro-diffusion model for computing membranepotentials and ionic concentrations in branching dendrites, spines andaxons. Biological Cybernetics. 1989; 62(1): 1-5.
4. Lopreore CL, et al. Computational modeling of three-dimensional
electrodiffusion in biological systems: application to the node of Ranvier.
Biophysical Journal. 2008; 95(6): 2624-35.

P50
Spatial denoising through topographic modularity in networksof spiking neurons
Barna ­Zajzon1, Abigail M­ orrison2, Renato ­Duarte3
1
Jülich Research Center, Institute of Neuroscience and Medicine (INM‑6),
Jülich, Germany; 2Forschungszentrum Jülich GmbH, Jülich, Germany;3
Jülich Research Center, Institute of Neuroscience and Medicine (INM‑6)
and Institute for Advanced Simulation (IAS‑6), Jülich, Germany
Correspondence: Barna Zajzon (b.zajzon@fz‑juelich.de)
BMC Neuroscience 2020, 21(Suppl 1):P50
Topographic maps are a pervasive structural feature of the mammalian brain, present throughout the cortical hierarchy and particularly
prevalent in the early sensory systems. These ordered projections
arrange and preserve the relative organization of cells between distinct populations and have been the object of many empirical studies.
From providing a structural scaffold for spatial information segregation to the organization of spatiotemporal feature maps, these ubiquitous anatomical features are known to have significant, albeit notentirely understood, functional consequences.
In this work, we systematically investigate the functional and dynamical impact of the characteristics of modular propagation pathways
in large networks of spiking neurons. Specifically, we manipulate
key structural parameters such as modularity, map size and degreeof overlap, and evaluate their impact on the network dynamics andcomputational performance during a continuous signal reconstruction task from noisy inputs. We show that transmission accuracy
increases as topographic projections become more structured, witheven moderate degrees of modularity improving overall discrimination capability.
Moreover, we identify a condition where the global population statistics converges towards a stable asynchronous irregular regime, allowing for a linear firing rate propagation along the topographic maps.
In such conditions, the networks exhibit spatial denoising properties
and the task performance improves vastly with hierarchical depth.
Importantly, the relative performance gain throughout the hierarchy
increases with the amount of noise in the input. This suggests that
topographic modularity is not only essential for accurate neural communication, but it can also provide the structural underpinnings tohandle noisy and corrupt information streams.
Using field-theoretic approximations, we demonstrate that this phenomenon can be attributed to a disruption in the E-I balance throughout the network. By changing the effective connectivity within thesystem, strongly modular projections facilitate the emergence of inhibition-dominated regimes where population responses along active
maps are amplified, whereas others are weakened or silenced.
In addition, we analytically derive constraints on the possible extent
of such maps, given that in cortical networks topographic specificity
is assumed to decrease with hierarchical depth. Our findings suggest
that, while task performance is relatively robust to variation in themap sizes, there is a fine balance between the spatial extent of the
topographic projections and their modularity. Maps may not become
arbitrarily small and must compensate for the size through denser
topographic connections, whereas this can actually have a detrimental effect in the case of larger maps if they overlap.

﻿BMC Neurosci 2020, 21(Suppl 1):54

Page 36 of 123

Taken together, these results highlight the functional benefits of structured connectivity in hierarchical neural networks, and shed light ona potential new role for modular topographic maps as a denoisingmechanism.
Acknowledgements: The authors gratefully acknowledge the computing time granted by the JARA Vergabegremium and provided on
the JARA Partition part of the supercomputer JURECA at Forschungszentrum Jülich.

into NetPyNE projects, from resources such as ModelDB, NeuroMorpho,
GitHub, etc.
In this poster, we present the latest improvements in NetPyNE anddiscuss recent data-driven multiscale models utilizing NetPyNE for
different brain regions, including: primary motor cortex, primary auditory cortex, and a canonical neocortex model underlying the Human
Neocortical Neurosolver, a software tool for interpreting the origin of
MEG/EEG data.

P51
Data‑driven multiscale modeling with NetPyNE: new featuresand example models
Joe ­Graham1, Matteo ­Cantarelli2, Filippo ­Ledda2, Dario Del ­Piano2,
Facundo ­Rodriguez2, Padraig G­ leeson3, Samuel A ­Neymotin4, Michael­Hines5, William W ­Lytton6, Salvador Dura‑Bernal6
1
SUNY Downstate Medical Center, Neurosim Lab, Brooklyn, New York,
United States of America; 2MetaCell, LLC, Cambridge, Massachusetts,
United States of America; 3University College London, Departmentof Neuroscience, Physiology & Pharmacology, London, United Kingdom;
4
Nathan Kline Institute for Psychiatric Research, Orangeburg, New York,
United States of America; 5Yale University, School of Medicine, New Haven,
Connecticut, United States of America; 6SUNY Downstate Medical Center,
Department of Physiology and Pharmacology, Brooklyn, New York, UnitedStates of America
Correspondence: Joe Graham (joe.w.graham@gmail.com)
BMC Neuroscience 2020, 21(Suppl 1):P51

Acknowledgments:
Supported
by
NIH
U24EB028998,
U01EB017695,
DOH01-C32250GG-3450000,
R01EB022903,
R01MH086638, R01DC012947, and ARO W911NF1910402.

Neuroscience experiments generate vast amounts of data that span
multiple scales: from interactions between individual molecules,
to behavior of cells, to circuit activity, to waves of activity across thebrain. Biophysically-realistic computational modeling provides atool to integrate and organize experimental data at multiple scales.
NEURON is a leading simulator for detailed neurons and neuronal
networks. However, building and simulating networks in NEURON is
technically challenging, requiring users to implement custom codefor many tasks. Also, lack of format standardization makes it difficult tounderstand, reproduce, and reuse many existing models.
NetPyNE is a Python interface to NEURON which addresses theseissues. It features a user-friendly, high-level declarative programming
language. At the network level for example, NetPyNE automaticallygenerates connectivity using a concise set of user-defined specifications rather than forcing the user to explicitly define millions of cell-tocell connections. NetPyNE enables users to generate NEURON models,
run them efficiently in automatically parallelized simulations, optimize
and explore network parameters through automated batch runs, anduse built-in functions for a wide variety of visualizations and analyses.
NetPyNE facilitates sharing by exporting and importing standardized
formats (NeuroML and SONATA), and is being widely used to investigate different brain phenomena. It is also being used to teach basic
neurobiology and neural modeling. NetPyNE has recently added support for CoreNEURON, the compute engine of NEURON optimized forthe latest supercomputer hardware architectures.
In order to make NetPyNE accessible to a wider range of researchersand students, including those with limited programming experience,and to encourage further collaboration between experimentalists andmodelers, all its functionality is accessible via a state-of-the-art graphical user interface (GUI). From a browser window, users can intuitively
define their network models, visualize and manipulate their cells andnetworks in 3D, run simulations, and visualize data and analyses. The
GUI includes an interactive Python console which synchronizes withthe underlying Python-based model.
The NetPyNE GUI is currently being improved in several ways. Flex
Layout is being introduced to ensure a responsive, customizable GUI
layout regardless of screen size or orientation. Redux is being added tothe stack to ensure the complete state of the app is known at all times,
minimizing bugs and improving performance. Bokeh is being used
to create interactive plots. Furthermore, by integrating NetPyNE with
Open Source Brain, users will be able to create online accounts to manage different workspaces and models (create, save, share, etc.). This
will allow interaction with online repositories to pull data and models

P52
Connectivity modulation by dual‑site transcranial alternating
current stimulation based on spike‑timing dependent plasticity
Bettina ­Schwab1, Peter König2, Andreas K ­Engel1
1
University Medical Center Hamburg‑Eppendorf, Departmentof Neurophysiology and Pathophysiology, Hamburg, Germany; 2Universityof Osnabrück, Institute of Cognitive Science, Osnabrück, Germany
Correspondence: Bettina Schwab (b.schwab@uke.de)
BMC Neuroscience 2020, 21(Suppl 1):P52
Transcranial alternating current stimulation (tACS) noninvasively
applies electric fields to the brain with the aim of entraining neuralactivity. As recordings of extracellular potentials are highly affectedby the stimulation artifact, M/EEG recordings before and after tACS
have become the standard to investigate neurophysiological effects oftACS in humans. In particular, we recently showed that dual-site tACS
can modulate functional connectivity between the targeted regions
outlasting the stimulation period, with in-phase stimulation (phaselag zero) increasing connectivity compared to anti-phase stimulation(phase lag π) [1]. Although the mechanism for such after-effects is notknown, spike-timing dependent plasticity (STDP) has been proposed
as a candidate [2,3]. We aim (1) to find a possible mechanism for our
experimentally observed connectivity changes, and (2) to estimate if
our dual-site tACS setting can successfully be extended to any stimulation frequency and target area.
We simulated two populations of each 1000 regularly spiking Izhikevich neurons [4] with realistic firing rate distributions. Excitatory
connections from each neuron to 100 random neurons of the otherpopulation with synaptic delay were subject to an experimentallyobserved STDP rule [5]. tACS was applied as sinusoidal input currents to both populations with varying phase lags. To validate ourmodel, we correlated experimentally found connectivity changes [1]between targeted sub-regions with the fiber length connecting those
sub-regions.
Synaptic weight changes depended on tACS frequency, phase lag,and synaptic delay. For 10 Hz tACS, synaptic weight changes between
in- and anti-phase tACS monotonically decreased within the range ofphysiological cortico-cortical conduction delays. Confirming this finding, our experimental data showed a negative correlation between
functional connectivity modulation (in-phase vs anti-phase tACS) andfiber length. Extending the simulations to other tACS frequencies, wefind that the expected direction of connectivity modulation can only
be expected for low tACS frequencies and small delays or delays thatare near multiples of the tACS cycle length.
In conclusion, our experimental findings [1] are in accordance with
STDP of synapses between the stimulated regions. Nevertheless, theapproach cannot be generalized to all tACS frequencies and delays.
Most robust effects are expected for low tACS frequencies and small
delays.
Acknowledgements: This work has been supported by DFG, SFB 936/
project A3.
References
1. Schwab BC, Misselhorn J, Engel AK. Modulation of large-scale cortical cou‑pling by transcranial alternating current stimulation. Brain stimulation. 2019;12(5): 1187-96.

﻿BMC Neurosci 2020, 21(Suppl 1):54

2. Wischnewski M, et al. NMDA receptor-mediated motor cortex plasticity
after 20 Hz transcranial alternating current stimulation. Cerebral Cortex.
2019; 29(7): 2924-31.
3. Vossen A, Gross J, Thut G. Alpha power increase after transcranial alternat‑
ing current stimulation at alpha frequency (α-tACS) reflects plastic changes
rather than entrainment. Brain stimulation. 2015; 8(3): 499-508.
4. Izhikevich EM. Simple model of spiking neurons. IEEE Transactions on neuralnetworks. 2003; 14(6): 1569-72.
5. Froemke RC, Dan Y. Spike-timing-dependent synaptic modification inducedby natural spike trains. Nature. 2002; 416(6879): 433-8.

P53
A simple, non‑stationary normalization model to explain
and successfully predict change detection in monkey area MT
Detlef ­Wegener1, Xiao ­Chen2, Lisa B­ ohnenkamp2, Fingal O G­ alashan1, Udo­Ernst2
1
University of Bremen, Brain Research Institute, Bremen, Germany;
2
University of Bremen, Computational Neurophysics Lab, Institutefor Theoretical Physics, Bremen, Germany
Correspondence: Detlef Wegener (wegener@brain.uni‑bremen.de)
BMC Neuroscience 2020, 21(Suppl 1):P53
Successful visually-guided behavior in natural environments criticallydepends on rapid detection of changes in visual input. A wildcat chasing a gazelle needs to quickly adapt its motions to sudden directionchanges of the prey, and a human driving a fast car on a highway
must instantaneously react to the onset of the red brake light of the
car in front. Visually responsive neurons represent such rapid featurechanges in comparably rapid, transient changes of their firing rate. In
the motion domain, for example, neurons in monkey area MT wereshown to represent the sign and magnitude of a rapid speed change
in the sign and amplitude of the evoked firing rate modulation following that change [1]. For positive speed changes, it was also shownthat the transient’s latency closely correlates with reaction time, and ismodulated by both spatial and non-spatial visual attention [2,3].
We here introduce a computational model based on a simple, canonical circuit in a cortical hypercolumn. We use the model to investigate
the computational mechanisms underlying transient neuronal firing
rate changes and their modulation by attention under a wide rangeof stimulus conditions. It is built of an excitatory and an inhibitory unit,
both of which are in response to an external input I(t). The excitatory
unit receives additional divisive input from the inhibitory unit. The
model’s dynamics is described by two differential equations quantifying how mean activity Ae of the excitatory unit and divisive input
current change with time t. By fitting the model parameters to experimental data, we show that it is capable to reproduce the time coursesof transient responses under passive viewing conditions. Mathematicalanalysis of the circuit explains hallmark effects of transient activations
and identifies the relevant parameters determining response latency,
peak response, and sustained activation. Visual attention is implemented by a simple multiplicative gain to the input of both units.
A key result of the analysis of the model’s dynamics is that steeper riseor decay times of the transient provide a consistent mechanisms ofattentional modulation, independent of both the overall activation of
the neuron prior to the speed change, and the sign of the change. Thisprediction is tested by new experiments requiring attention to both
positive and negative speed changes. The results of the experimentare in full accordance with the prediction of the model, providing evidence that even decreases in firing rate in response to the reductionof the speed of an attended stimulus occur with shorter latency. Thus,
the model provides a unique framework for a mechanistic understanding of MT response dynamics under very different sensory andbehavioral conditions.
Acknowledgments: Supported by BMBF grant 01GQ1106 and DFG
grant WE 5469/3-1.
References
1. Traschütz A, Kreiter AK, Wegener D. Transient activity in monkey area MT
represents speed changes and is correlated with human behavioral perfor‑
mance. Journal of Neurophysiology. 2015; 113: 890-903.

Page 37 of 123

2. Galashan FO, Saßen HC, Kreiter AK, Wegener D. Monkey area MT latencies tospeed changes depend on attention and correlate with behavioral reaction
times. Neuron. 2013; 78: 740-750.
3. Schledde B, Galashan FO, Przybyla M, Kreiter AK, Wegener D. Task-specific,
dimension-based attentional shaping of motion processing in monkey area
MT. Journal of Neurophysiology. 2017; 118: 1542-1555.

P54
Non‑invasive chronometric modeling of oculomotor processing
Devin Kehoe, Mazyar Fallah
York University, Department of Psychology, Toronto, Canada
Correspondence: Devin Kehoe (dhkehoe@gmail.com)
BMC Neuroscience 2020, 21(Suppl 1):P54
There is a striking correlation between overt oculomotor behavior andneurophysiological processing in critical oculomotor substrates likethe superior colliculus (SC). During oculomotor target selection, unresolved activation [1] or suppression [2] at a competing distractor locus
in the epoch ~30 ms prior to saccade initiation elicits saccades curved
towards or away from the distractor (respectively). We therefore developed a non-invasive technique to measure the time course of excitatory and inhibitory activity encoding a distractor in which humansaccade curvature is modeled as a function of saccade-distractor onset
asynchrony (SDOA): the time between the transient onset of a task
irrelevant distractor and the initiation of a saccade to a target [3]. The
distractor processing time course observed using this technique wasclosely aligned to the time course of visuomotor neural activity in SC
during target selection [4] and we observed time course differences
between luminance- and color-modulated distractors that were also
in alignment with SC visuomotor cell activity [5].
We expanded the SDOA technique to examine oculomotor processingof complex objects during a perceptual discrimination saccade-taskwith varied visual similarity between distractors and targets. We sawthat the latency of the initial excitatory response was ~60 ms longer
for these complicated, task-relevant objects than for the task irrelevant distractors with simple visual features [3]. We also saw differencesin the excitatory processing time course for the complex objects,
which has critical implications for theories of oculomotor target selection processing. We developed additional analytic techniques that
estimate the latency of excitatory processing independent of saccade
curvature modeling, which provided consistent temporal estimates.
The first analysis examined target selection accuracy as a function of
distractor processing time and showed that prior to the estimatedtime of distractor information being projected into the oculomotor
substrates, target selection was guided exclusively by the target representation. The second analysis examined the frequency of saccades
as a function of SDOA and demonstrated that immediately after theestimated time of excitatory activity encoding the distractor, there isa transient drop in the likelihood of making a saccade, mirroring theeffects of flash suppression. This work confirms the validity of our noninvasive chronometric technique and our original interpretation of it,
while also illustrating that SDOA saccade curvature modeling is applicable to more complicated oculomotor target selection contexts.
References
1. McPeek RM, Han JH, Keller EL. Competition between saccade goals in the
superior colliculus produces saccade curvature. Journal of Neurophysiology.
2003; 89: 2577–2590.
2. White BJ, Theeuwes J, Munoz DP. Interaction between visual- and goalrelated neuronal signals on the trajectories of saccadic eye movements.
Journal of Cognitive Neuroscience. 2012; 24: 707–717.
3. Kehoe DH, Fallah M. Rapid accumulation of inhibition accounts for sac‑cades curved away from distractors. Journal of Neurophysiology. 2017; 118:
832–844.
4. McPeek RM, Keller EL. Saccade target selection in the superior colliculus
during visual search task. Journal of Neurophysiology. 2002; 88: 2019–2034.
5. White BJ, Boehnke SE, Marino RA, Itti L, Munoz DP. Color-related signals
in the primate superior colliculus. Journal of Neuroscience. 2009; 29:12159–12166.

﻿BMC Neurosci 2020, 21(Suppl 1):54

P55
Identifying changes in whole‑brain functional connectivity
in complex longitudinal clinical trials
Sidhant ­Chopra1, Kristina ­Sabaroedin2, Shona F­ rancey3, BrianO’Donoghue3, Vanessa ­Cropley4, Barnaby ­Nelson3, Jessica G­ raham3, Lara
­Baldwin3, Steven ­Tahtalian4, Hok Pan ­Yuen3, Kelly A­ llott3, Mario A­ lvarez3,
Susy ­Harrigan3, Christos ­Pantelis4, Stephen ­Wood3, Patrick M­ cGorry3, Alex
­Fornito5
1
Monash University, Turner Institute for Brain and Mental Health,
Melbourne, Australia; 2Monash University, Melbourne, Australia; 3Orygen
Youth Health, Melbourne, Australia; 4Melbourne Neuropsychiatry Centre,
Melbourne, Australia; 5Monash University, The Turner Institute for Brainand Mental Health, School of Psychological Sciences and Monash Biomed,
Melbourne, Australia
Correspondence: Sidhant Chopra (sid.chopra@monash.edu)
BMC Neuroscience 2020, 21(Suppl 1):P55
Resting-state Functional Magnetic Resonance Imaging (rs-fMRI) is
increasingly being used as a secondary measure in complex clinical
trials [1]. The inclusion of rs-fMRI allows researchers to investigate theimpact interventions, such as medication, can have on regional andnetwork-level brain hemodynamics. Such trials are expensive, difficult
to conduct, often have small samples in rare clinical populations andhigh attrition rates. Standard neuroimaging analysis software are notusually suited to these sub-optimal design parameters. Accessible statistical tools that are robust to these conditions are much needed.
We propose an analysis workflow, which combines 1) ordinary leastsquares marginal model with a robust covariance estimator to accountfor within-subject correlation, 2) nonparametric p-value inference
using a novel bootstrapping method [2] and, 3) edge- and component-level family-wise error (FWE) control using the Network Based
Statistic [3]. This workflow has several advantages, including being
robust to unbalanced longitudinal samples, small-sample correction
using heteroskedasticity-consistent standard errors and simplified
nonparametric inference. Additionally, this method is computationally less demanding than traditional mixed-linear models and does notbias the analysis by pre-selecting regions of interest.
We apply this novel workflow to a world-first triple-blind longitudinal
placebo-controlled trial where 62 antipsychotic-naïve people aged
between 15 to 24 with first-episode psychosis received either an atypical antipsychotic or a placebo pill over a treatment period of 6 months.
Both patient groups received intensive psychosocial therapy. A third
healthy control group with no psychiatric diagnosis (n=27) was also
recruited. rs-fMRI scans were acquired at baseline, 3-months and
12-months. We show that our analysis method is sufficiently sensitive
to detect FWE-corrected significant components in this complex threegroups [healthy control, placebo, medication] by three time points[baseline, 12-weeks, 52-weeks] design.
Here, we introduce an analysis workflow which is capable of detectingchanges in resting-state functional networks in complex clinical trials
with multiple timepoints and unbalanced groups. This analysis workflow is freely available as an R function::netSandwich.
References
1. O’Donoghue B, et al. Staged treatment and acceptability guidelines inearly psychosis study (STAGES): A randomized placebo controlled trial ofintensive psychosocial treatment plus or minus antipsychotic medication
for first‐episode psychosis with low‐risk of self‐harm or aggression. Study
protocol and baseline characteristics of participants. Early Intervention in
Psychiatry. 2019; 13(4): 953-960.
2. Guillaume B, et al. Improving mass-univariate analysis of neuroimaging databy modelling important unknown covariates: Application to EpigenomeWide Association Studies. NeuroImage. 2018; 173: 57-71.
3. Zalesky A, Fornito A, Bullmore ET. Network-based statistic: identifying differ‑ences in brain networks. Neuroimage. 2010; 53(4): 1197-1207.

Page 38 of 123

P56
Impact of simulated asymmetric interregional cortical connectivity
on the local field potential
David Boothe, Alfred Yu, Kelvin Oie, Piotr Franaszczuk
U.S. Army Research Laboratory, Human Research Engineering Directorate,
Aberdeen Proving Ground, Maryland, United States of America
Correspondence: David Boothe (david.l.boothe7.civ@mail.mil)
BMC Neuroscience 2020, 21(Suppl 1):P56
Spontaneous neuronal activity as observed using electroencephalogram is characterized by a non-stationary 1/f power spectrum
interspersed with periods of rhythmic activity [1]. Underlying cortical neuronal activity is, by contrast, hypothesized to be sparse andarrhythmic [2]. Properties of cortical neuronal connectivity such assparsity, small world organization, and conduction delays have all
been proposed to play a critical role in the generation of spontaneous brain activity. However, the relationship between the structurereflected in measures of global brain activity, the underlying neuronal
activity, and neuronal connectivity is, at present, poorly characterized.
In order to explore the role of cortical connectivity in the generation of
spontaneous brain activity, we present a simulation of cerebral cortex
based on the Traub model [3] implemented in the GENESIS neuronalsimulation environment.
We made extensive changes to the original Traub model in order tomore faithfully reproduce the spontaneous cortical activity described
here. We re-tuned the original Traub parameters to eliminate both
intrinsic neuronal activity and removed the gap junctions. Tuning
out intrinsic neuronal activity in the model allowed changes to theunderlying connectivity to be the central factor in modifying overallmodel activity. The model we present consists of 16 simulated cortical regions each containing 976 neurons (15,616 neurons total).
Previously we connected simulated regions in a nearest neighborfashion via short range association fibers. These association fibers
originated from pyramidal cells in cortical layer 2/3 (P23s). We foundthat the introduction of symmetric bidirectional inter-regional connectivity was sufficient to induce both a 1/f power spectrum as wellas oscillatory behavior in the local field potential of the underlying
cortical regions in the 2 to 40 Hz range. However we also found that
sub-region activity was fairly uniform, even if these sub-region oscillations were not strongly correlated with one another. We hypothesizethat introducing asymmetric inter-regional connectivity in this model
may produce underlying simulated neuronal activity that is more variable in its output and more similar to the output observed in the biological system..
Connectivity between cortical regions in the biological brain are often
asymmetric with outputs of layer 2/3 pyramidal cells terminating indifferent layers and in different proportions on receiving regions ofcortex [4].
Here we explore how these asymmetrical connectivity schema alter
microscopic (spikes) and macroscopic (local field potential) featuresof our cortical simulations. We re-organized our 16 simulated cortical regions in a hierarchical fashion using feedforward and feedback
connectivity patterns observed between regions of the visual system[4]. We then compare the behavior of this network to our previous
simulations using nearest neighbor and small world like inter-regional
connectivity. We hypothesize that networks with asymmetric connectivity between regions will give richer and more heterogenous model
outputs.
References
1. Le Van Quyen M. Disentangling the dynamic core: a research program for aneurodynamics at the large-scale. Biological Research. 2003; 36(1): 67-88.
2. Buzsáki G. Rhythms of the Brain: Oxford University Press; 2006.
3. Traub RD, et al. Single-column thalamocortical network model exhibiting
gamma oscillations, sleep spindles, and epileptogenic bursts. Journal of
Neurophysiology. 2005; 93(4): 2194-232.
4. Salin PA, Bullier J. Corticocortical connections in the visual system: structureand function. Physiological Reviews. 1995; 75(1): 107-54.

﻿BMC Neurosci 2020, 21(Suppl 1):54

P57
Motor cortex encodes a value function consistent
with reinforcement learning
Venkata ­Tarigoppula1, John C­ hoi2, John H­ essburg2, David M­ cNiel2, Brandi­Marsh2, Joseph F­ rancis2
1
University of Melbourne, Biomedical Engineering, Melbourne, Australia;
2State University of New York Downstate Medical Center, Physiology
and Pharmacology, New York, United States of America
Correspondence: Venkata Tarigoppula (adi.tarigoppula@unimelb.edu.au)
BMC Neuroscience 2020, 21(Suppl 1):P57
Reinforcement learning (RL) theory provides a simple model that canhelp explain many animal behaviors. RL models have been very successful in describing the neural activity in multiple brain regions andat several spatiotemporal scales ranging from single units up to hemodynamics during the learning process in animals including humans.
A key component of RL is the value function, which captures the
expected, temporally discounted reward, from a given state. A reward
prediction error occurs when there is a discrepancy between the valuefunction and actual reward, and this error is used to drive learning. The
value function can also be modified by the animal’s knowledge andcertainty of its environment. Here we show that the bilateral primary
motor cortical (M1) neural activity in non-human primates (Rhesus
and Bonnet macaques either sex) encodes a value function in linewith temporal difference RL. M1 responds to the delivery of unpredictable reward (unconditional stimulus (US)), and shifts its value related
response earlier in a trial, becoming predictive of expected reward,
when reward is predictable due to the presence of an explicit cue(conditional stimulus (CS)). This is observed in tasks performed manually or observed passively and in tasks without an explicit CS, but witha predictable temporal reward environment. M1 also encodes the
expected reward value in a multiple reward level CS-US task. Here
we extend the Microstimulus temporal difference RL model (MSTD),
reported to accurately capture RL related dopaminergic activity, toaccount for both phasic and tonic M1 reward-related neural activity ina multitude of tasks, during manual trials, as well as observational trials. This information has implications towards autonomously updating
brain-machine interfaces.
P58
Systematic testing and validation of models of hippocampal
neurons against electrophysiological data
Sára Sáray1, Andrew ­Davison2, Szabolcs K­ ali3, Christian Rössert4, Shailesh­Appukuttan5, Eilif ­Muller4, Tamás ­Freund3
1
Pázmány Péter Catholic University, Faculty of Information Technology
and Bioinics, Budapest, Hungary; 2Centre National de la Recherche
Scientifique, Unité de Neuroscience, Information et Complexité, Gif sur
Yvette, France; 3Institute of Experimental Medicine, Hungarian Academyof Sciences, Budapest, Hungary; 4École Polytechnique Fédérale deLausanne, Blue Brain Project, Geneva, Switzerland; 5Centre National dela Recherche Scientifique/ Université Paris‑Saclay, Paris‑Saclay Instituteof Neuroscience, Gif‑sur‑Yvette, France
Correspondence: Sára Sáray (saraysari@gmail.com)
BMC Neuroscience 2020, 21(Suppl 1):P58
Anatomically and biophysically detailed neuronal models, that are built
in a data-driven manner, are useful tools in understanding and predicting the behavior and function of the different cell types of the brain.
Due to the growing number of computational and software tools andthe increasing body of experimental data from electrophysiologicalmeasurements, that enable more accurate neuronal modeling, there isa constantly increasing number of different models of many cell types
available in the literature. These are usually developed using different methods and for different purposes, most often to reproduce theresults of a few selected experiments, and it is often unknown how
they would behave in other situation or whether they are able to generalize outside their original scope. This might be the reason why it isuncommon in the modelling community to re-use and further develop
already existing models, which prevents the construction of consensus“community models” that could capture an increasing proportion ofPage 39 of 123

the electrophysiological properties of the given cell type. In addition,even when models are re-used they may lose their ability to capture
their originally adjusted behavior while their parameters are retuned tomake them fit another subset of experimental data.
The collaborative approach of model development requires extensive
validation test suites which enables modelers to evaluate their models
against experimental observations according to standardized criteria
and to explore the changes in model behavior at the different stagesof its development. Applying automated tests also facilitates optimalmodel re-use and co-operative model development by making it possible to learn more about models published by other groups (beyond
the results included in the papers) with relatively little effort.
Initially we addressed this issue by developing an open-source Python
test suite, called HippoUnit (https://github.com/KaliLab/hippounit) for
the automated and systematic validation and quantitative comparison of the behavior of models of the hippocampal CA1 pyramidal cells(which is one of the most studied cell type) against electrophysiological data. We applied HippoUnit to test and compare the behavior ofseveral different hippocampal CA1 pyramidal cell models available on
ModelDB (results are available at: https://github.com/KaliLab/HippoUnit_demo). We also employed the test suite to aid the development ofmodels within the Human Brain Project (HBP) and integrated the tests
into the validation framework developed in the HBP.
Currently we are extending this test suite by adding new tests for thevalidation of other important hippocampal cell types. New validation
tests cover somatic behavior and signal propagation in dendrites ofbasket cells and CA3 pyramidal cells, and the propagation of action
potential in the axon of basket cells.
By presenting these results we hope to encourage the modeling community to use more systematic testing during model development,in order to create neural models that generalize better, and make theprocess of model building more reproducible and transparent.
Acknowledgments: Supported by the ÚNKP-19-3-III New National
Excellence Program of the Ministry for Innovation and Technology;
European Social Fund (EFOP-3.6.3-VEKOP-16-2017-00002); the European Union’s Horizon 2020 Framework Programme for Research andInnovation (Specific Grant Agreements No. 720270, 785907 - Human
Brain Project SGA1, SGA2).
P59
Reverse engineering neural networks to identify their cost functions
and implicit generative models
Takuya ­Isomura1, Karl F­ riston2
1
RIKEN Center for Brain Science, Saitama, Japan; 2University College
London, London, United Kingdom
Correspondence: Takuya Isomura (takuya.isomura@riken.jp)
BMC Neuroscience 2020, 21(Suppl 1):P59
It is widely recognised that maximising a variational bound on model
evidence – or equivalently, minimising variational free energy – provides a unified, normative formulation of inference and learning [1].
According to the complete class theorem [2], any dynamics that minimises a cost function can be viewed as performing Bayesian inference;
implying that any neural network whose activity and plasticity follow
the same cost function is implicitly performing Bayesian inference.
However, the implicit Bayesian model that corresponds to any given
cost function is a more delicate problem. Here, we identify a class of biologically plausible cost functions for canonical neural networks of rate
coding neurons, where the same cost function is minimised by bothneural activity and plasticity [3]. We then demonstrate that such costfunctions can be cast as variational free energy under an implicit generative model in the well-known form of partially observed Markov decision processes. This equivalence means that the activity and plasticity ina canonical neural network can be understood as approximate Bayesian inference and learning, respectively. Mathematical analysis showsthat the firing thresholds – that characterise the neural network costfunction – correspond to prior beliefs about hidden states in the generative model. This means that the Bayes optimal encoding of hidden
states is attained when the network’s implicit priors match the process

﻿BMC Neurosci 2020, 21(Suppl 1):54

Page 40 of 123

generating its sensory inputs. The theoretical formulation was validated
using in vitro neural networks comprising rat cortical cells cultured ona microelectrode array dish [4,5]. We observed that in vitro neural networks – that receive input stimuli generated from hidden sources – perform causal inference or source separation through activity-dependent
plasticity. The learning process was consistent with Bayesian belief
updating and the minimisation of variational free energy. Furthermore,constraints that characterise the firing thresholds were estimated fromthe empirical data to quantify the in vitro network’s prior beliefs about
hidden states. These results highlight the potential utility of reverse
engineering generative models to characterise the neuronal mechanisms underlying Bayesian inference and learning.

combining either presynaptic nAChR with presynaptic mAChR or presynaptic nAChR with postsynaptic nAChR is capable of producing changes to
A1 receptive fields observed during rapid task-related plasticity.
This model tested four mechanisms by which cholinergic modulation
may induce rapid task-related plasticity in A1. Cholinergic modulation
could reproduce experimentally observed changes to A1 receptive fields
when it was implemented using a combination of mechanisms. Two different combinations of cholinergic modulation were found to produce
the expected changes in A1 receptive fields. Since the model predicts thatthese two different combinations of cholinergic modulation would havedifferential effects on the rate of neuronal firing, it will be possible to run
experimental tests to distinguish between the two theoretic possibilities.

References
1. Friston K. The free-energy principle: a unified brain theory?. Nature Reviews
Neuroscience. 2010; 11(2): 127-38.
2. Wald A. An essentially complete class of admissible decision functions. The
Annals of Mathematical Statistics. 1947; 549-55.
3. Isomura T, Friston K. Reverse engineering neural networks to characterise
their cost functions. bioRxiv. 2019; 654467.
4. Isomura T, Kotani K, Jimbo Y. Cultured cortical neurons can perform blindsource separation according to the free-energy principle. PLoS Computa‑
tional Biology. 2015; 11(12): e1004643.
5. Isomura T, Friston K. In vitro neural networks minimise variational freeenergy. Scientific Reports. 2018; 8(1): 1-4.

References
1. Chambers JD, et al. Computational Neural Modeling of Auditory Cortical
Receptive Fields. Frontiers in Computational Neuroscience. 2019; 13: 28.
2. Fritz J, Shamma S, Elhilali M, Klein D. Rapid task-related plasticity of spec‑
trotemporal receptive fields in primary auditory cortex. Nature neurosci‑
ence. 2003; 6(11): 1216-23.

P60
Cholinergic modulation can produce rapid task‑related plasticity
in the auditory cortex
Jordan ­Chambers1, Shihab S­ hamma2, Anthony B­ urkitt1, David G­ rayden1,
Diego ­Elgueda3, Jonathan F­ ritz4
1
University of Melbourne, Department of Biomedical Engineering,
Melbourne, Australia; 2University of Maryland, Institute for Systems Research,
College Park, Maryland, United States of America; 3Universidad de Chile,
Facultad de Ciencias Veterinarias y Pecuarias, Santiago, Chile; 4New York
University, Center for Neural Science, New York, United States of America
Correspondence: Jordan Chambers (jordanc@unimelb.edu.au)
BMC Neuroscience 2020, 21(Suppl 1):P60
Neurons in the primary auditory cortex (A1) display rapid task-related
plasticity, which is believed to enhance the ability to selectively attend
to one stream of sound in complex acoustic scenes. Previous studies
have suggested that cholinergic projections from Nucleus Basalis to
A1 modulate auditory cortical responses and may be a key componentof rapid task related plasticity. However, the underlying molecular, cellular and network mechanisms of cholinergic modulation of corticalprocessing remain unclear.
A previously published model of A1 receptive fields [1] that can reproduce
task-related plasticity was used to investigate mechanisms of cholinergic
modulation in A1. The previous model comprised a cochlea model andintegrate-and-fire model neurons to represent networks in A1. Action
potentials from individual model neurons were used to calculate the receptive field using reverse correlation, which allowed direct comparison toexperimental data. To allow an investigation into different mechanismsof cholinergic modulation at A1, this previous model was extended by: (1)adding integrate-and-fire neurons to represent neurons projecting from
Nucleus Basalis to A1; (2) adding inhibitory interneurons in A1; (3) includinginternal calcium dynamics in the integrate-and-fire models; and (4) including calcium-dependent potassium conductance in the integrate-and-firemodels. Since cholinergic modulation has several potential sites of action in
A1, the current model was used to investigate acetylcholine acting throughboth muscarinic and nicotinic acetylcholine receptors (mAChR and nAChR,
respectively) located presynaptically or postsynaptically.
Four possible mechanisms of cholinergic modulation on A1 receptive
fields were investigated. Previous research indicates cholinergic modulation should be able to suppress an inhibitory region and enhance an excitatory region in the receptive fields [2]. Our model indicates it is unlikelythat any one of these four mechanisms could produce these oppositechanges to both excitatory and inhibitory regions. However, multiple
mechanisms occurring simultaneously could produce the expectedchanges to the receptive fields in this model. We demonstrate thatP61
Self‑supervision mechanism of multiple dendritic compartments
for temporal feature learning
Milena M C­ arvalho1, Tomoki ­Fukai2
1
University of Tokyo, Department of Complexity Science and Engineering,Tokyo, Japan; 2Okinawa Institute of Science and Technology Graduate
University, Neural Coding and Brain Computing Unit, Okinawa, Japan
Correspondence: Milena M Carvalho (glowingsea@gmail.com)
BMC Neuroscience 2020, 21(Suppl 1):P61
In recent years, there has been a surge of interest in how dendrites andtheir complex geometry allow for integration of spatiotemporal patternsof input and transformation into neuronal response [1,2]. Although sizeable amounts of synaptic input arrive at distinct branches, the dendritic
tree must convert this composite signal into meaningful information,
which will be then transferred to the soma and potentially evoke spiking activity [3]. While there has been considerable development in dendritic integration modeling in the last two decades, single-compartmentneurons are still a hallmark of computational neuroscience and machinelearning [2]; this level of abstraction, however, ignores components ofdendritic integration that may be essential to properly represent neuronal
dynamics, hence limiting the performance of the studied systems.
Some of these shortcomings were recently overcome by a two-compartment model that introduced a self-supervision rule within a single neuron to minimize information loss between dendritic synaptic input andsomatic output spiking activity [4]. Networks composed of this neuronmodel could perform a variety of unsupervised temporal feature learning tasks such as chunking and blind source separation, usually performed by specialized networks with different learning rules. We wish to
generalize this learning principle and develop a new framework in which
dendritic trees have two or more compartments with hierarchical, linearnonlinear integrations [3]. Here we investigate how can self-supervision
be defined in this system and examine its accuracy when presented tothe previously introduced temporal feature learning tasks. We expect
that, by distributing the synaptic input into different compartments ofthe same neuron, our model can use delayed integration to differentiate
similar temporal patterns that were previously indistinguishable.
Acknowledgments: This work was partly supported by KAKENHI(nos. 18H05213 and 19H04994). We would like to thank the Ministryof Education, Culture, Sports, Science and Technology (MEXT) of Japanand Okinawa Institute of Science and Technology Graduate University(OIST) for supporting M. M. C.
References
1. Stuart GJ, Spruston N. Dendritic integration: 60 years of progress. Natureneuroscience. 2015; 18(12): 1713-21.
2. Häusser M, Mel B. Dendrites: bug or feature? Current Opinion in Neurobiol‑
ogy. 2003; 13(3): 372-383.
3. Ujfalussy BB, Makara JK, Lengyel M, Branco T. Global and Multiplexed Dendritic
Computations under In Vivo-like Conditions. Neuron. 2018; 100(3): 579 - 592.e5

﻿BMC Neurosci 2020, 21(Suppl 1):54

4. Asabuki T, Fukai T. Somatodendritic consistency check for temporal feature
segmentation. Nature Communications. 2020; 11: 1554.

P62
Large scale discrimination between neural models
and experimental data
Russell ­Jarvis1, Sharon ­Crook2, Richard G­ erkin3
1
Arizona State University, Neuroscience, Tempe, Arizona, UnitedStates of America; 2Arizona State University, School of Mathematicaland Statistical Sciences, Tempe, Arizona, United States of America;
3
Arizona State University, School of Life Sciences, Tempe, Arizona, UnitedStates of America
Correspondence: Russell Jarvis (rjjarvis@asu.edu)
BMC Neuroscience 2020, 21(Suppl 1):P62
Scientific insight is well-served by the discovery and optimization of
abstract models that can reproduce experimental findings. NeuroML(NeuroML.org), a model description language for neuroscience, facilitates reproducibility and exchange of such models by providing animplementation-agnostic model description in a modular format.
NeuronUnit (neuronunit.scidash.org) evaluates model accuracy bysubjecting models to experimental data-driven validation tests, a formalization of the scientific method.
A neuron model that perfectly imitated real neuronal electricalbehavior in response to any stimulus would not be distinguishable
from experiments by any conventional physiological measurement.
In order to assess whether existing neuron models approached thisstandard, we took 972 existing neuron models from NeuroML-DB.org
and subjected them to a standard series of electrophysiological stimuli(somatic current injection waveforms). We then extracted analogous
448 stimulus-evoked recordings of real cortical neurons from the Allen
Cell Types database. We applied multiple feature extraction algorithms on the physiological responses of both model simulations andexperimental recordings in order to characterize physiological behavior with a very high degree of detail spanning hundreds of features.
After applying dimensionality reduction to this very high dimensionalfeature space, we show that the real (biological neurons) and simulated(model neurons) recordings are easily and fully discriminated by eye or
any reasonable classifier. Consequently, not a single model neuron produced physiological responses that could be confused with a biological neuron. Was this a defect of the model design (e.g. key mechanisms
unaccounted for) or of model parameterization? We found that if we
introduced models that were revised via optimization the revised models overlapped with the distribution of biological neurons, and were
mostly classified as such. The remaining post-optimization disagreement between models and biological neurons may reflect limitations ofmodel design and can be investigated by probing the key features usedby classifiers to distinguish these two populations.

Page 41 of 123

these compartments as an excitable network [5, 6], we could apply asimple discrete model of dendritic spike propagation to investigate
how the morphology affects the firing behavior of a neuron.
Our approach allows for a detailed analysis of the neuron’s dendritic activity pattern. For example, we can generate spatial heatmaps of firing rate,
revealing a significant spatial dependence of dynamics. By comparing thecompartmental activity for different strengths of external stimulus, we
can investigate the dynamic range – over what range of input strength acompartment’s firing rate is varied the most. We find that dendritic bifurcations boost the local dynamic range. Thus, a soma located in densely
bifurcated regions tends to have large dynamic ranges.
Identifying how effectively a neuron utilizes its dendritic tree toamplify stimuli can be achieved by comparing the average dendritic
compartment activity against how often the soma fires. Since it takes
energy to control the ion channels responsible for dendritic spikes,
we call this ratio the relative energy consumption of the neuron. If it is<1, the neuron is energy efficient. Conversely, if it is >1, the neuron isinefficient. We identified two morphological features – the number ofsomatic branches, and the centrality of the soma – that can be used tocategorize the energy behavior of neurons (Fig. 1).

P63
Morphological determinants of neuronal dynamics and function
Christoph ­Kirch1, Leonardo G­ ollo2
1
QIMR Berghofer, Brisbane, Australia; 2Monash University, Melbourne,
Australia
Correspondence: Christoph Kirch (ckirc4@gmail.com)
BMC Neuroscience 2020, 21(Suppl 1):P63
Neurons use their complex dendritic tree to integrate complex spatiotemporal patterns of incoming signals. The nonlinear interactions of
spikes along the bifurcating branches allows the neuron to perform
dendritic computations [1]. While models often aim to realisticallysimulate the complicated chemical properties of these signals, theyoften do this at the cost of simplifying the spatial structure to one(e.g., Hodgkin Huxley, integrate-and-fire) [2] or few compartments [3].
These simplified structures do not accurately represent the morphology, and thus it is not possible to infer how the dendritic structure canshape a neuron’s output.
Here, we used detailed neuron reconstructions from the online database NeuroMorpho.Org [4]. Each neuron is made up of one somaticcompartment and up to 10,000 dendritic compartments. By treating

Fig. 1 Neurons can be classified into functional groups based on
two morphological features; the number of branches connected tothe soma, and its centrality. Type 1 corresponds to energy efficient
behaviour (energy < 1) across the parameter space of h and P. Type 2
exhibits mixed behaviour. Type 3 neurons are inefficient (energy > 1).
Type T represents a transitional category with mixed behaviour

﻿BMC Neurosci 2020, 21(Suppl 1):54

The classification scheme we have proposed provides an importanttestable basis in explaining structural differences across neurons. For
example, depending on the required computational function, certain
features of the dendritic tree would be more favorable. Our model canbe applied to any of the 100,000+ reconstructions available at NeuroMorpho.Org, and can be extended to investigate the effect of changes
in dendritic structure.
References
1. London M, Häusser M. Dendritic computation. Annual Review of Neurosci‑
ence. 2005; 28: 503-32.
2. Izhikevich EM. Simple model of spiking neurons. IEEE Transactions on Neu‑ral Networks. 2003; 14(6): 1569-72.
3. Izhikevich EM. Dynamical systems in neuroscience. MIT press; 2007.
4. Kirch C, Gollo LL. Spatially resolved dendritic integration: Towards a func‑
tional classification of neurons. bioRxiv. 2020; 657403.
5. Gollo LL, Kinouchi O, Copelli M. Active dendrites enhance neuronal
dynamic range. PLoS Computational Biology. 2009; 5(6): e1000402.
6. Gollo LL, Kinouchi O, Copelli M. Statistical physics approach to dendritic
computation: The excitable-wave mean-field approximation. PhysicalReview E. 2012; 85(1): 011911.

P64
Gamma oscillations organized as localized burst patterns
with anomalous propagation dynamics in primate cerebral cortex
Xian ­Long1, Yuxi ­Liu1, Paul R ­Martin2, Samuel G ­Solomon3, Pulin ­Gong1
1
University of Sydney, School of physics, Sydney, Australia; 2University
of Sydney, Save Sight Institute, Sydney, Australia; 3University College
London, Department of Experimental Psychology, London, United
Kingdom
Correspondence: Xian Long (longxian319@hotmail.com)
BMC Neuroscience 2020, 21(Suppl 1):P64
Gamma oscillations (30-80 Hz) occur in transient bursts with varying
frequencies and durations. These non-stationary gamma bursts havebeen widely observed in many brain areas but have rarely been quantitatively characterized, and the mechanisms that produce them are notunderstood. In this study we investigate the spatiotemporal propertiesof gamma bursts through combined empirical and modeling investigation. Our array recordings of local field potentials in visual corticalarea MT of the marmoset monkey reveal that gamma bursts form localized patterns with complex propagation dynamics. We also show thatthe propagations of these patterns are characterized by anomalous
dynamics that are fundamentally different from regular or Brownian
motions conventionally assumed. We show that all aspects of theseanomalous dynamics can be quantitatively captured by a spatiallyextended, biophysically realistic circuit model. Circuit dissection of themodel shows further that the anomalous dynamics rely on the intrinsic
meta-stability near the critical transition between different circuit states(i.e. between synchronous and the regular propagating wave states).
Our results thus reveal novel spatiotemporal organization properties ofgamma bursts, and explain them in terms of underlying circuit mechanisms, providing new computational functions for gamma oscillations.
P65
Dynamical circuit mechanisms of attentional sampling
Guozhang Chen, Pulin Gong
The University of Sydney, School of Physics, Sydney, Australia
Correspondence: Guozhang Chen (gche4213@uni.sydney.edu.au)
BMC Neuroscience 2020, 21(Suppl 1):P65
Selective attention can sift out particular objects or features fromthe plethora of stimuli. Such preferential processing of attention isoften compared to a spotlight pausing to illuminate relevant targets
in visual fields in a stimulus-driven way (bottom-up attention) and/or
task-driven way (top-down attention). Recent studies have revealed
that bottom-up distributed attention involving multiple objects is
not a sustained spotlight, but samples the visual environment in afundamentally dynamical manner with theta-rhythmic cycles, witheach sampling cycle being implemented through gamma oscillations.

Page 42 of 123

However, the fundamental questions regarding the dynamical natureand the circuit mechanism underlying such dynamical attentional
sampling remain largely unknown. To address these questions, in thisstudy we investigate a biophysically plausible cortical circuit modelof spiking neurons and find that in the working regime of the model(i.e. the regime near the critical transition between the asynchronousand propagating wave states), the localized activity pattern emerging
from the circuit exhibits rich spatiotemporal dynamics. We illustratethat the nonequilibrium nature of the localized pattern enables thecircuit to dynamically shift to different salient external inputs, without introducing additional neural mechanisms such as inhibition of
return as in the conventional winner-take-all models of attention. We
elucidate that the dynamical shifting process of the activity patternprovides a mechanistic account of key neurophysiological and behavioral findings on attention, including theta oscillations, theta-gamma
phase-amplitude coupling, and vigorous-faint spiking fluctuations.
Furthermore, by using the saliency maps of natural stimuli, we demonstrate that the nonequilibrium activity pattern dynamics can better
explain the psychophysical findings regarding attention maps andattention sampling paths than the conventional models, providing a
profound computational advantage for efficiently sampling external
environments. Our work thus establishes a novel circuit mechanism bywhich non-equilibrium, fluctuating pattern dynamics near the criticaltransition of circuit states can be exploited for implementing efficientattentional sampling.
P66
Brain‑computer interfaces using stereotactic
electroencephalography: identification of discriminative recordingsites for decoding imagined speech
Kevin ­Meng1, David ­Grayden1, Mark C­ ook2, Farhad G­ oodarzy3
1
University of Melbourne, Department of Biomedical Engineering,
Melbourne, Australia; 2University of Melbourne, Department of Medicine,
Melbourne, Australia; 3University of Melbourne, Department of Medicine,
Dental and Health Sciences, Melbourne, Australia
Correspondence: Kevin Meng (ksmeng@student.unimelb.edu.au)
BMC Neuroscience 2020, 21(Suppl 1):P66
As part of the monitoring of medication-resistant epilepsy before resective surgeries, patients are implanted with electrocorticography (ECoG)
electrode arrays placed on the surface of the cortex or stereotactic electroencephalography (SEEG) depth electrodes penetrating the cortex.
Both recording modalities measure local field potentials (LFPs) from theirrespective target locations. The patients are occasionally recruited to voluntarily participate in brain-computer interface (BCI) research. In recent
years, ECoG-based BCIs have demonstrated long-term reliable decodingof various cortical processes involved in mental imagery tasks. Despite
similarities in terms of clinical application and decoding strategies, SEEGbased BCIs have been the focus of only a limited number of studies.
While the sparsity of their cortical coverage represents a disadvantage,
SEEG depth electrodes have the potential to target bilateral combinations of deeper brain structures that are inaccessible with ECoG [1].
Here, we propose a framework for SEEG-based BCIs to identify discriminative recording sites for decoding imagined speech. Four patientswith epilepsy were implanted with 7 to 11 SEEG depth electrodes, eachconsisting of 8 to 15 recording sites. Electrode placement and durationof monitoring were solely based on the requirements of clinical evaluation. Signals were amplified and recorded at a sampling rate of 5 kHz.
The task consisted of listening to utterances and producing overt andcovert (imagined) utterances of a selection of 20 monosyllabic English
words made up of all combinations of five consonant patterns (/b_t/,/m_n/, /r_d/, /s_t/, /t_n/) and four vowels (/æ/, /ε/, /i:/, /u:/).
We determined the relative importance of recording sites based onclassification accuracies obtained from features extracted at the corresponding electrode locations. Each trial was associated with a label(consonant pattern or vowel) and a set of features consisting of normalized log-transformed power spectral densities at different time
points and selected frequency bands: delta (1-4 Hz), theta (4-8 Hz),alpha (8-12 Hz), beta (12-30 Hz), gamma 1 (30-45 Hz), gamma 2 (5595 Hz), gamma 3 (105-145 Hz), and gamma 4 (155-195 Hz). A pair-wise﻿BMC Neurosci 2020, 21(Suppl 1):54

classification model using logistic regression was used to predict thelabels. Parameters were trained for different combinations of recording sites, as well as each condition (listening, overt, covert), patient,and pair of labels separately. The mean classification rate across all
pairs of labels was calculated to quantify the discriminative power ofindividual and combined recording sites.
Our results consistently show across all patients that relevant depth
electrodes for decoding imagined speech are found in both left andright superior temporal gyri. Anatomical analyses of these electrodelocations revealed that recording sites in the grey matter were themost discriminative. This is in line with previous studies of speech
BCIs [2]. In addition to providing a better understanding of the neuralprocesses underlying imagined speech, our practical framework may
be applied to reduce feature dimensionality and computational cost
while improving accuracy in real-time SEEG-based BCI applications.
References
1. Herff C, Krusienski DJ, Kubben P. The Potential of Stereotactic-EEG for BrainComputer Interfaces: Current Progress and Future Directions. Frontiers in
Neuroscience. 2020; 14: 123.
2. Cooney C, Folli R, Coyle D. Neurolinguistics research advancing develop‑
ment of a direct-speech brain-computer interface. IScience. 2018; 8: 103-25.

P67
A computational model to inform presurgical evaluation of epilepsy
from scalp EEG
Marinho ­Lopes1, Leandro J­ unges2, Luke ­Tait3, John ­Terry2, Eugenio A­ bela4,
Mark ­Richardson4, Marc ­Goodfellow5
1
Cardiff University, Cardiff, United Kingdom; 2University of Birmingham,
Centre for Systems Modelling and Quantitative Biomedicine, Birmingham,
United Kingdom; 3Cardiff University, Cardiff University Brain Research
Imaging Centre, Cardiff, United Kingdom; 4King’s College London, Instituteof Psychiatry, Psychology and Neuroscience, London, United Kingdom;
5
University of Exeter, Living Systems Institute, Exeter, United Kingdom
Correspondence: Marinho Lopes (m.lopes@exeter.ac.uk)
BMC Neuroscience 2020, 21(Suppl 1):P67
Epilepsy affects an estimated fifty million people worldwide. Approximately one third do not respond to anti-epileptic medication and are
therefore potential candidates for alternative treatments such as epilepsy surgery. Surgery aims to remove the epileptogenic zone (EZ),the brain area responsible for the generation of seizures. Epilepsy
surgery is thus preceded by an evaluation to determine the locationof the EZ. A number of brain imaging modalities may be used in thisevaluation, namely scalp electroencephalography (EEG) and magnetic
resonance imaging (MRI), possibly followed by invasive intracranial
EEG. The effectiveness of intracranial EEG to inform epilepsy surgery
depends on where electrodes are implanted. This decision is informed
by noninvasive recording modalities such as scalp EEG. The decision
is frequently not trivial because scalp EEG may provide inconclusive
or even contradictory predictions of the EZ location. A poor hypothesis based on noninvasive data may lead to an incorrect placement of
intracranial electrodes, which in turn may make surgery ill-advised andpotentially unsuccessful if performed [1].
Here we propose a framework to interrogate scalp EEG and determine
epilepsy lateralization to aid in electrode implantation [2]. We used
eLORETA to map source activities from seizure epochs recorded from
scalp EEG and obtained functional networks using the phase-locking
value (PLV). The networks were then studied using a mathematicalmodel of epilepsy (a modified theta model to represent a network ofinteracting neural masses [2,3]). By removing different regions of interest from the network and simulating their impact on the network’s
ability to generate seizures in silico, the framework provides predictions of epilepsy lateralization. We considered 15 individuals from the
EPILEPSIAE database and studied a total of 62 seizures. Results wereassessed by taking into account actual intracranial implantations andpostsurgical outcome. The framework proved useful in assessing
epilepsy lateralization in 12 out of 15 individuals considered. These
results show promise for the use of this framework to better interrogate scalp EEG and aid clinicians in presurgical assessment of people
with epilepsy.

Page 43 of 123

References
1. Jayakar P, et al. Diagnostic utility of invasive EEG for epilepsy surgery: indica‑
tions, modalities, and techniques. Epilepsia. 2016; 57(11): 1735-47.
2. Lopes MA, et al. Computational modelling in source space from scalp EEG
to inform presurgical evaluation of epilepsy. Clinical Neurophysiology. 2020;
131(1): 225-34.
3. Lopes MA, et al. An optimal strategy for epilepsy surgery: Disruption of therich-club?. PLoS Computational Biology. 2017; 13(8) :e1005637.

P68
Large‑scale calcium imaging of spontaneous activity in larvalzebrafish reveals signatures of criticality
Michael ­McCullough1, Robert ­Wong1, Zac ­Pujic1, Biao S­ un1, Geoffrey J­Goodhill1,2
1
University of Queensland, Queensland Brain Institute, St Lucia,
Queensland, Australia; 2University of Queensland, School of Mathematics
and Physics, St Lucia, Queensland, Australia
Correspondence: Geoffrey J Goodhill (g.goodhill@uq.edu.au)
BMC Neuroscience 2020, 21(Suppl 1):P68
Neural networks in the brain may self-organise such that they operate near criticality, that is, poised on the boundary between phasesof order and disorder [1]. Models of neural networks tuned close tocriticality are optimal in terms of dynamic range, information transmission, information storage and computational adaptability [2]. Most
experimental evidence for criticality in the brain has come from studies
of high resolution neural spiking data recorded from tissue cultures oranaesthetised animals using microelectrode arrays, or from studies ofmesoscopic-scale neural activity using magnetic resonance imaging orelectroencephalograms. These approaches are inherently limited either
by under-sampling of the neural population or by coarse spatial resolution. This can be problematic for empirical studies of criticality because
the characteristic dynamics of interest are theoretically scale-free.
Recently, Ponce-Alvarez et al. [3] investigated the larval zebrafish as a
new model for neural criticality by utilising the unique properties ofthe organism that enable whole-brain imaging of neural activity in vivo
and without anaesthetic. They identified hallmarks of neural criticality
in larval zebrafish using 1-photon calcium imaging and voxel-based
analysis of neuronal avalanches. Here we addressed two key limitationsof their study by instead using 2-photon calcium imaging to observe
truly spontaneous activity, and by extracting neural activity time series
at single-cell resolution via state-of-the-art image segmentation [4].
Our data comprise fluorescence time series for large populations ofneurons from 3-dimensional volumetric recordings of spontaneous
activity in the optic tectum and cerebellum of larval zebrafish with panneuronal expression of GCaMP6s (n=5; approx. 10000 neurons per fish)(Fig. 1A). Neuronal avalanche statistics revealed power-law relationships and scale-invariant avalanche shape collapse which are consistent with crackling noise dynamics from a 3-dimensional random field
Ising model [5] (Fig. 1B-C). Observed power laws were validated using
shuffled surrogate data and log-likelihood ratio tests. This result provides the first evidence of criticality in the brain from large-scale in vivo
neural activity at single cell resolution. Our findings demonstrate thepotential of larval zebrafish as a model for the investigation of criticalphenomena in the context of neurodevelopmental disorders that may
perturb the brain away from criticality.

﻿BMC Neurosci 2020, 21(Suppl 1):54

Fig. 1 A z-projection of 2-photon data from a larval zebrafish. B
The spatio-temporal profile of a neuronal avalanche (clusters ofneural activations that propagate through the brain, as shown by
shaded regions). C Avalanche statistics reveal characteristic powerlaw relationships including the distribution of avalanche size (shown)consistent with a random field Ising model (dashed line)
References
1. Cocchi L, Gollo LL, Zalesky A, Breakspear M. Criticality in the brain: A synthe‑
sis of neurobiology, models and cognition. Progress in Neurobiology. 2017;
158: 132–152.
2. Shew WL, Plenz D. The functional benefits of criticality in the cortex. Neuro‑scientist. 2013; 19(1): 88–100.
3. Ponce-Alvarez A, et al. Whole-brain neuronal activity displays cracklingnoise dynamics. Neuron. 2018; 100(6): 1446–1459.
4. Giovannucci A, et al. CaImAn an open source tool for scalable calcium
imaging data analysis. Elife. 2019; 8: e38173.
5. Sethna JP, Dahmen KA, Myers CR. Crackling noise. Nature. 2001; 410(6825):242-250.

P69
Population model of oscillatory dynamics in hippocampal CA1
and CA3
regions
Ashraya S Shiva, Bruce Graham
University of Stirling, Department of Computing Science and Mathematics,
Stirling, United Kingdom
Correspondence: Ashraya S Shiva (asv@cs.stir.ac.uk)
BMC Neuroscience 2020, 21(Suppl 1):P69
Theta oscillations may act as carrier waves for synchronizing activities across neuronal regions. Several gamma cycles, each containing
a specific pattern of activity correlated with an event e.g. animal location forming place fields, are encompassed in a single theta cycle. We
have extended the septo-hippocampal population firing rate model
proposed by Denham and Borisyuk [1] to study the influence of inhibitory interneurons, specifically PV-containing basket cells (BCs) andbistratified cells (BSCs) on theta and theta-coupled gamma oscillations
in both CA1 and CA3 hippocampal networks. Our CA1 microcircuit
model is a combination of that of Denham and Borisyuk and Cutsuridis
et. al [2]. The CA3 model is adapted from the CA1 model on the basisof CA3-specific experimental data.
The theta-phase relationships of the neurons in these models arelargely determined by the afferent and efferent connections of BCs
in both CA1 and CA3. In CA1, BCs and BSCs are active on opposite
phases of theta in a pure-theta tuning (no gamma), as per data fromPage 44 of 123

Klausberger et. al [3,4] due to extra external drive to basket cells from
CA3, enabling them to be active on the opposite cycle to CA1 pyramidal cells (PCs). As excitatory drive to BCs from PCs is increased there isa bifurcation in which BC activity switches to being in-phase with PCs,
and BSC activity remains in-phase with PCs. A further increase in drive
to BCs from PCs leads to gamma oscillations in both PC and BC activity, and forces BSC activity to shift to the opposite theta phase due tostrong inhibition from BCs.
Varying strengths of external inputs also affects the strength of oscillations and phase relationships. Both in CA1 and CA3, PCs, BCs and
BSCs reach steady state activity for very low or very high external
inputs. BCs in CA1 rely on CA3 and EC input for their activity in thetaonly tuning, so their activity reduces if these inputs are reduced. BSCs
activity in CA1 actually reduces for increased CA3 and EC input, due toincreased inhibition from BCs. In CA3, recurrent connections between
PCs are far more likely (and hence stronger on a population scale) thanin CA1. BSCs in CA3 are driven only by CA3 PCs and have no externalsources of excitation. BCs in CA3 get dentate gyrus input in addition
to PC input. Other interneurons (modeled as a single population) get
excitation from CA1 and CA3 PCs, plus EC. In CA1, only CA3 and EC are
external sources of input, apart from septum. The resultant main difference in activity in CA3, compared with CA1, is that BSCs are always
in sync with BCs and PCs during theta. A minimum strength of septum input is required to generate theta, then increasing septum input
raises the frequency of theta oscillations. Whereas, increasing thevalue of dentate gyrus input transforms the oscillatory activity into
stable, non-oscillatory activity, and also decreases the septum activity.
Strong EC and CA1 input to CA3 may silence CA3 PCs, BCs and BSCs
through exciting other inhibitory interneurons.
References
1. Denham MJ, Borisyuk RM. A model of theta rhythm production in the
septal‐hippocampal system and its modulation by ascending brain stem
pathways. Hippocampus. 2000; 10(6): 698-716.
2. Cutsuridis V, Cobb S, Graham BP. Encoding and retrieval in a model of the
hippocampal CA1 microcircuit. Hippocampus. 2010; 20(3): 423-46.
3. Klausberger T, et al. Brain-state-and cell-type-specific firing of hippocampal
interneurons in vivo. Nature. 2003; 421(6925): 844-8.
4. Klausberger T, et al. Spike timing of dendrite-targeting bistratified cells dur‑ing hippocampal network oscillations in vivo. Nature Neuroscience. 2004;
7(1): 41-7.

P70
Influence of anatomical connectivity and intrinsic dynamics
in a connectome based neural mass model of TMS‑evoked
potentials
Neda ­Kaboodvand1, John ­Griffiths2
1
Karolinska Institutet, Department of Clinical Neuroscience, Stockholm,
Sweden; 2Centre for Addiction and Mental Health, Krembil Centre
for Neuroinformatics, Toronto, Canada
Correspondence: John Griffiths (j.davidgriffiths@gmail.com)
BMC Neuroscience 2020, 21(Suppl 1):P70
Perturbation via electromagnetic stimulation is a powerful way ofprobing neural systems to better understand their functional organization. One of the most widely used neurostimulation techniques inhuman neuroscience is transcranial magnetic stimulation (TMS) withconcurrently recorded electroencephalography (EEG). The immediate
EEG responses to single-pulse TMS stimulation, termed TMS-evoked
potentials (TEPs), are spatiotemporal waveforms in EEG sensor- or
source-space [1]. TEPs display several characteristic features, including i) rapid wave-like propagation away from the primary stimulation
site, and ii) multiple volleys of recurrent activity, that continue for several hundred milliseconds following the stimulation pulse. These TEP
patterns reflect reverberant activity in large-scale cortico-cortical andcortico-subcortical brain networks, and have been used to study neural excitability in a wide variety of research contexts, including sleep,anaesthesia, and coma [2]. There has been relatively little work done,
however, on computational modelling of TEP waveform morphologies, and how these spatiotemporal patterns emerge from a combination of global brain network structure and local physiological﻿BMC Neurosci 2020, 21(Suppl 1):54

characteristics. Here we present a novel connectome-based neuralmass model of TEPs that accurately reproduces recordings across multiple subjects and stimulation sites. We employ a biophysical electricfield model (using the simnibs [3] library) to identify the electrical field(‘E-field’) distribution over the cortical surface resulting from stimulation at a given TMS coil location and orientation, that is based on
T1-weighted MRI-derived cortical geometry, and personalized to individual subjects. These TMS-induced E-field maps are then summed toyield a current injection pattern over regions in a canonical freesurferbased brain parcellation. Whole-brain neural activity is modelled witha network of oscillatory (Fitzhugh-Nagumo) units [4,5], coupled byanatomical connectivity weights derived from diffusion-weighted MRI
tractography [6], and perturbed by a brief square-wave current injection weighted regionally by the cortical E-field map magnitudes. Using
this model we are able to accurately reproduce the typical radially
propagating TEP patterns under a wide range of parameter values. For
the later (150ms+) TEP components however, we find that it is necessary to modify the weight of cortico-thalamic and thalamo-corticalprojections in the tractography-defined anatomical connectivity (seealso [7]), which has the effect of promoting recurrent activity patterns.
These results contribute important insights to our long-term objectiveof developing an accurate model of TEPs that can be used to guidethe design and administration of TMS-EEG for excitability mapping inclinical contexts.
References
1. Ilmoniemi RJ, Kičić D. Methodology for combined TMS and EEG. Brain
topography. 2010; 22(4): 233.
2. Massimini M, et al. Breakdown of cortical effective connectivity during
sleep. Science. 2005; 309(5744): 2228-32.
3. Saturnino GB, et al. Simnibs 2.1: A comprehensive pipeline for individual‑
ized electric field modelling for transcranial brain stimulation. In Brain and
Human Body Modeling 2019; 3-25. Springer, Cham.
4. Izhikevich EM, FitzHugh R. Fitzhugh-nagumo model. Scholarpedia. 2006;
1(9): 1349.
5. Spiegler A, Hansen EC, Bernard C, McIntosh AR, Jirsa VK. Selective activationof resting-state networks following focal stimulation in a connectomebased network model of the human brain. Eneuro. 2016; 3(5).
6. Schirner M, Rothmeier S, Jirsa VK, McIntosh AR, Ritter P. An automatedpipeline for constructing personalized virtual brains from multimodal
neuroimaging data. NeuroImage. 2015; 117: 343-57.
7. Bensaid S, Modolo J, Merlet I, Wendling F, Benquet P. COALIA: a computa‑
tional model of human EEG for consciousness research. Frontiers in systems
neuroscience. 2019; 13: 59.

P71
Local homeostatic regulation of the spectral radius of echo‑state
networks
Fabian ­Schubert1, Claudius G­ ros2
1
Goethe University Frankfurt, Institute for Theoretical Physics, Frankfurt am
Main, Germany; 2Goethe University Frankfurt, Frankfurt am Main, Germany
Correspondence: Fabian Schubert (fschubert@itp.uni‑frankfurt.de)
BMC Neuroscience 2020, 21(Suppl 1):P71
Recurrent cortical network dynamics plays a crucial role for sequential
information processing in the brain. While the theoretical frameworkof reservoir computing provides a conceptual basis for the understanding of recurrent neural computation, it often requires manualadjustments of global network parameters, in particular of the spectral
radius of the recurrent synaptic weight matrix. Being a mathematical and relatively complex quantity, the spectral radius is not readily accessible to biological neural networks, which are based on theprinciple that information about the network state should either be
encoded in local intrinsic dynamical quantities (e.g. membrane potentials), or transmitted via synaptic connectivity. We present an intrinsic adaptation rule, termed flow control, for echo state networks thatsolely relies on locally accessible variables, while still being capable oftuning a global quantity, the spectral radius of the network, towards a

Page 45 of 123

desired value. The adaptation rule works online, in the presence of acontinuous stream of input signals. It is based on a local comparison
between the mean squared recurrent membrane potential and themean squared activity of the neuron itself. It is derived from a global
scaling condition on the dynamic flow of neural activities, and requires
the separability between external and recurrent input currents. The
effectiveness of the presented mechanism is tested numerically using
different external input protocols. Furthermore, the network performance after applying the adaptation is evaluated by training the network to perform a time delayed XOR operation on binary sequences.
P72
Comparison of surrogate techniques for evaluationof spatio‑temporal patterns in massively parallel spike trains­ ouss2, Günther P­ alm3, Sonja G­ ruen4
Alessandra ­Stella1, Peter B
1
Forschungszentrum Jülich, Jülich, Germany; 2Forschungszentrum Jülich,
Institute of Neuroscience and Medicine (INM‑6, INM‑10, IAS‑6), Jülich,
Germany; 3University of Ulm, Institute of Neural Information Processing,Ulm, Germany; 4Jülich Research Centre, Institute of Neuroscienceand Medicine (INM‑6 and INM‑10), Jülich, Germany
Correspondence: Alessandra Stella (a.stella@fz‑juelich.de)
BMC Neuroscience 2020, 21(Suppl 1):P72
To identify active cell assemblies we developed a method to detectsignificant spatio-temporal spike patterns (STPs). The method, called
SPADE [1-3], identifies repeating ms-precise spike patterns across neurons. SPADE first discretizes the spike trains in exclusive bins (defining
the pattern precision, e.g. 5ms) and clips the bin content to 1 if morethan 1 spike is therein. Second, STPs are mined by Frequent Itemset
Mining [4], and their counts are evaluated for significance throughcomparison to surrogate data. The distribution of the pattern counts
in the surrogate data provides p-values for determining the significance of grouped patterns. The surrogate data implement the nullhypothesis of independence, and a classical choice is to apply uniform
dithering (UD) [5], i.e. independent, uniformly distributed displacement of each spike (e.g. in a range of +/- 5 times the bin width [1]).
This approach does not maintain the absolute refractory period anda potentially existing ISI regularity. The binarization leads in the surrogates to a higher probability of more than 1 spike per bin, and thus by
the consecutive clipping to a reduction of the spike count (up to 12%,in particular for high firing rates) as compared to the original data.
This may cause false positives (cmp. [6]). Therefore, we explored further methods for surrogate generation. To not have different spikecounts in the original and the surrogate data, bin-shuffling shuffles
the bins after binning the original data. To keep the refractory period(RP) uniform dithering with refractory period (UD-RP) does not allow
dithered spikes within a short time interval after each spike. Dithering
according to the ISI distribution (ISI-D) [e.g. 5] or the Joint-ISI distribution (J-ISI-D) [7] conserves the ISI and ISI/J-ISI distributions, respectively. Spike-train shifting (ST-Shift) [8,5] moves the whole spike train,
trial by trial, by a random amount, thereby only affecting the relationof spike trains to each other. Thus all of these implement different
null-hypotheses, as summarized in the table below. It shows the non-/preservation (no/yes) of features in the various surrogates comparedto the original data.
We applied all surrogate methods (within SPADE) and compared theirresults using artificial, and experimental spike data simultaneously
recorded in pre-/motor cortex of a macaque monkey performing areach-to-grasp task [9]. We find that all methods besides UD lead tovery similar results in terms of number of patterns and their composition. UD results in a much larger number of patterns, in particular if
neurons have very high firing rates and exhibit regular spike trains. We
conclude that the reduction in the spike count using UD increases thefalse positive rate for spike trains with CV<1 and/or high firing rates,
the other methods are much less affected, the least spike train shifting.

﻿BMC Neurosci 2020, 21(Suppl 1):54

Page 46 of 123

Table summarizing statistical properties of the spike trains being
non-/preserved (no/yes) by the various surrogate techniques
taken into consideration
Method/Fea‑ture

UD ISI-D

J-ISI-D UD-RP Bin-Shuff ST-shift
approx. approx. yes

Spike count

no

yes

ISI

no

approx. approx. no

no

yes

J-ISI

no

no

no

yes

approx. no

yes

References
1. Torre E, et al. Synchronous spike patterns in macaque motor cortex during
an instructed-delay reach-to-grasp task. Journal of Neuroscience. 2016;
36(32): 8329-40.
2. Quaglio P, Yegenoglu A, Torre E, Endres DM, Grün S. Detection and evalua‑tion of spatio-temporal spike patterns in massively parallel spike train datawith spade. Frontiers in Computational Neuroscience. 2017; 11: 41.
3. Stella A, Quaglio P, Torre E, Grün S. 3d-SPADE: Significance evaluation of
spatio-temporal patterns of various temporal extents. Biosystems. 2019;
185: 104022.
4. Picado-Muiño D, Borgelt C, Berger D, Gerstein G, Grün S. Finding neuralassemblies with frequent item set mining. Frontiers in Neuroinformatics.
2013; 7(9).
5. Louis S, Borgelt C, Grün S. Complexity distribution as a measure for assem‑
bly size and temporal precision. Neural Networks. 2010; 23(6): 705-12.
6. Pipa G, Grün S, Van Vreeswijk C. Impact of spike train autostructure on prob‑
ability distribution of joint spike events. Neural Computation. 2013; 25(5):1123-63.
7. Gerstein GL. Searching for significance in spatio-temporal firing patterns.
Acta Neurobiologiae Experimentalis. 2004; 64(2): 203-8.
8. Pipa G, Wheeler DW, Singer W, Nikolić D. NeuroXidence: reliable andefficient analysis of an excess or deficiency of joint-spike events. Journal ofcomputational neuroscience. 2008; 25(1): 64-88.
9. Brochier T, et al. Massively parallel recordings in macaque motor cortex
during an instructed delayed reach-to-grasp task. Scientific data. 2018; 5(1):
1-23.

P73
Effects of transneuronal spreading in the logistic diffusion modelof Aβ protein with longitudinal PET scans
Byeong Chang ­Jeong1, Myungwon ­Choi2, Daegyeom ­Kim2, Xue ­Chen3,
Marcus ­Kaiser4, Cheol E ­Han2
1
Korea University, Sejong, South Korea; 2Korea University, Department
of Electronics and Information Engineering, Sejong, South Korea; 3China
University of Petroleum, College of Control Science and Engineering,
Qingdao, China; 4Newcastle University, School of Computing, Newcastle,
United Kingdom
Correspondence: Byeong Chang Jeong (ni3_jbc@naver.com)
BMC Neuroscience 2020, 21(Suppl 1):P73
Alzheimer’s disease (AD) is a neuro-degenerative disease which causes
severe loss of cognitive functions and deteriorates the quality ofdaily life of elders. One hypothesis of AD development and progression is deposition of amyloid-beta protein which is known to cause
neuronal cell death in the gray matter and to degrade the cognitive
function of the affected regions. Recently, it is reported that the toxic
protein can move through connectivity between neurons, which iscalled transneuronal projection, besides the local diffusion throughthe non-neuronal tissues. In this study, we investigated the effectsof the transneuronal projection on the amyloid deposition patternover the brain through simulation of a mathematical model basedon the actual neuroimage data. The model consists of two components: transneuronal spreading, and local spreading. The transneuronal spreading captures propagation of the toxic protein in thewhite matter while the local spreading captures its local diffusion
through the gray matter. Each component has its own parameter tobalance between components. We estimated all parameters in themodel through the Bayesian inference method that best describe

the longitudinal data from Alzheimer’s disease neuroimaging initiative (ADNI) dataset, by comparing the results of simulation datawith the actual dataset. We modelled our brain as a high-resolution
graph whose nodes are a small volume of the cerebral cortices, andwhose edges are the structure of adjacency between them, delineating spreading pathways. We transformed the cerebral cortices into a
triangular lattice of prism-like volumes from structural magnetic resonance (MR) images. From the topology of the lattice we extracted local
adjacency between the nodes. On the contrary, for the long-range
connection through the neuronal fibers, we obtained the connectivity between the nodes from the diffusion-weighted MR imaging (DWI).
Each node has the level of the amyloid deposition, obtained throughthe18F-Florbetapir positron emission tomography (PET) images withpartial volume effect correction. This high-resolution graph model canenable to simulate more granularly and accurately. To investigate theeffect of transneuronal spreading, we compared results of two conditions: simulation 1) with only the local spreading, and 2) with both thelocal and transneuronal spreading. Both models showed the spreadof the amyloid from the regions where the initial protein accumulation is high, also known as epicenters. The result of the former condition may explain gradual spread from the epicenters to their nearby
regions while it could not explain remote spread from the epicentersto their distant regions. On the other hand, the latter results illustrated
not only local spread, but also remote spread. Thus, the model withboth local and transneuronal spreading components is more feasible
to explain the deposition of amyloid-beta in AD. The purpose of thisstudy is to investigate the effect of transneuronal spread of the amyloid beta over AD’s progression using the actual neuroimage data.
Our results support the previous research of transmission of amyloid
through the neuronal pathways.
Acknowledgements: This work was supported by the Korea HealthTechnology R&D Project through the Korea Health Industry Development Institute (KHIDI) that was funded by the Ministry of Health & Welfare, Republic of Korea (HI19C0645), and Medical Research Council, UK(MR/T004347/1).
P74
Implications of reduced somatostatin interneuron inhibition
in depression on human cortical microcircuit activity
Heng Kang ­Yao1, Alexandre Guet‑McCreight2, Etay ­Hay3
1
University of Toronto, Physiology, Toronto, Canada; 2Centre for Addiction
and Mental Health, Krembil Centre for Neuroinformatics, Toronto, Canada;
3
Centre for Addiction and Mental Health/ University of Toronto, Krembil
Centre for Neuroinformatics/ Psychiatry, Toronto, Canada
Correspondence: Heng Kang Yao (kant.yao@mail.utoronto.ca)
BMC Neuroscience 2020, 21(Suppl 1):P74
There is increasing evidence of reduced cortical inhibition in a varietyof psychiatric disorders such as major depressive disorder (MDD) andschizophrenia. Cortical inhibition is mediated by GABAergic interneurons and plays a key role in modulating information processing in
cortical pyramidal neurons. In particular, interneurons expressing
somatostatin (SST) inhibit the distal dendrites of pyramidal neurons
and mediate lateral inhibition. Recent postmortem studies showed
reduced SST expression in these interneurons in MDD patients,suggesting weaker levels of inhibition. The reduced inhibition isthought to result in a lower signal-to-noise ratio of cortical microcircuit activity, due to abnormally increased intrinsic activity compared
to stimulus-evoked activity. We test this hypothesis and characterize
the implications of reduced SST inhibition in depression using novel
computational models of human cortical microcircuits. We generated detailed models of the major neuronal types in human corticallayer 2/3, by integrating unique human electrophysiology data andapplying machine-learning optimization algorithms. We connected
the model neurons in a microcircuit according to connectivity statistics and synaptic parameters derived from the literature. We showthat intrinsic activity significantly increases in models of MDD microcircuits, in which SST interneuron inhibition was reduced, comparedto healthy microcircuits. We then then compared the signal-to-noise
ratio of intrinsic and evoked activity in healthy and MDD microcircuits.

﻿BMC Neurosci 2020, 21(Suppl 1):54

Page 47 of 123

Our results thus elucidate the role that inhibition plays in normal andpathological information processing by human cortical microcircuits.
P75
The covariance perceptron: theory and application
Matthieu ­Gilson1, David ­Dahmen2, Ruben Moreno‑Bote3, Andrea­Insabato4, Moritz ­Helias5
1
Universitat Pompeu Fabra, Center for Brain and Cognition, Barcelona,
Spain; 2Jülich Research Centre, Institute of Neuroscience and Medicine(INM‑6), Jülich, Germany; 3Universitat Pompeu Fabra, Center for Brain
and Cognition & DTIC, Barcelona, Spain; 4Universty Pompeu Fabra,
Barcelona, Spain; 5Jülich Research Centre, Institute of Neuroscienceand Medicine (INM‑6, INM‑10) and Institute for Advanced Simulation(IAS‑6), Jülich, Germany
Correspondence: Matthieu Gilson (matthieu.gilson@upf.edu)
BMC Neuroscience 2020, 21(Suppl 1):P75
Introduction: Many efforts in the study of the brain have focused on
representations of stimuli by neurons and learning thereof. Our work
[1] demonstrates the potential of a novel learning paradigm for neuronal activity with high variability, where distributed information isembedded in the correlation patterns.
Learning theory: We derive a learning rule to train a network to perform an arbitrary operation on spatio-temporal covariances for timeseries. To illustrate our scheme, we use the example of classification
where the network is trained to perform an input-output mappingfrom given sets of input patterns to representative output patterns,
one output per input group. This setup is the same as learning activity patterns for the classical perceptron [2], a central concept that hasbrought many fruitful theories in the fields of neural coding and learning in networks. For that reason, we refer to our classifier as “covariance perceptron”. Compared to the classical perceptron, a conceptual
difference is that we base information on the the co-fluctuations of
the input time series that result in second-order statistics. In this way,
robust information can be conveyed despite a high apparent variability in the activity. This approach is a radical change of perspective
compared to classical approaches that typically transform time series
into a succession of static patterns where fluctuations are noise. On
the technical ground, our theory relies on the multivariate autoregressive (MAR) dynamics, for which we derive the weight update (a gradient descent) such that input covariance patterns are mapped to given
objective output covariance patterns.
Application to MNIST database: To further explore its robustness,
we apply the covariance perceptron to the recognition of objects thatmove in the visual field by a network of sensory (input) and downstream (output) neurons. We use the MNIST database of handwritten
digits 0 to 4. As illustrated in Figure 1, the traces “viewed” by an input
neuron exhibit large variability across presentations. Because we wantto identify both the digit identity and its moving direction, covariancesof the input time series are necessary. We show that the proposed
learning rule can successfully train the network to perform the classification task and robustly generalize to unseen data. In our work [1], we
also show that the covariance perceptron favorably compares to theclassical nonlinear perceptron in extracting second-order statistics.

Fig. 1 A Moving digit in the visual field with two columns of 9 input
neurons each feeding 10 output neurons (one per category, the larg‑
est output variance indicates the predicted category). B Responses of
the input to the digit 0 in panel A moving to the right. C Mean activ‑
ity traces for an input neuron for digits 0 and 2, moving left or right as
indicated above. The colored areas correspond to the standard devia‑
tion of the time-varying activity over all patterns. D The information
relative to both digit and motion is reflected in the input covariances.
E Confusion matrices of the covariance perceptron for the train andtest sets for digits 0 to 4 moving left and right
Towards distributed spike-based information processing: We
envisage future steps that transpose this work to information conveyed by high-orders in the spike trains, to obtain the supervised
equivalent of spike-timing-dependent plasticity (STDP).
References
1. Gilson M, Dahmen D, Moreno-Bote R, Insabato A, Helias M. The covariance
perceptron: A new framework for classification and processing of timeseries in recurrent neural networks. bioRxiv. 2019; 562546.
2. Bishop CM. Pattern recognition and machine learning. springer; 2006.

P76
Inferring a simple mechanism for alpha‑blocking by fitting a neural
population model to EEG spectra
Agus ­Hartoyo1, Peter C­ adusch2, David ­Liley3, Damien H­ icks4
1
Swinburne University of Technology, Optical Sciences Centre,
Melbourne, Australia; 2Swinburne University of Technology, Departmentof Physics and Astronomy, Melbourne, Australia; 3Swinburne Universityof Technology, Centre for Human Psychopharmacology, Melbourne,
Australia; 4Centre for Human Psychopharmacology, Optical Sciences
Centre, Melbourne, Australia
Correspondence: Agus Hartoyo (ahartoyo@swin.edu.au)
BMC Neuroscience 2020, 21(Suppl 1):P76

﻿BMC Neurosci 2020, 21(Suppl 1):54

Alpha blocking, a phenomenon where the alpha rhythm is reduced
by attention to a visual, auditory, tactile or cognitive stimulus, is oneof the most prominent features of human electroencephalography(EEG) signals. Here we identify a simple physiological mechanism bywhich opening of the eyes causes attenuation of the alpha rhythm. We
fit a neural population model to EEG spectra from 82 subjects, eachshowing different degrees of alpha blocking upon opening of theireyes. Although it is notoriously difficult to estimate parameters fromfitting such models, we show that, by regularizing the differences inparameter estimates between eyes-closed and eyes-open states, we
can reduce the uncertainties in these differences without significantly
compromising fit quality. From this emerges a parsimonious explanation for the spectral changes between states: just a single parameter, pei, corresponding to the strength of a tonic, excitatory input
to the inhibitory population, is sufficient to explain the reduction inalpha rhythm upon opening of the eyes. When comparing parameter estimates across different subjects we find that the inferred differential change in pei for each subject increases monotonically withthe degree of alpha blocking observed. In contrast, other parameters
show weak or negligible differential changes that do not scale withthe degree of alpha attenuation in each subject. Thus, most of thevariation in alpha blocking across subjects can be attributed to thestrength of a tonic afferent signal to the inhibitory cortical population.
P77
Tonic GABAergic inhibition enhances activity‑dependent dendritic
calcium signaling
Thomas M M­ orse1, Chiayu Q ­Chiu2, Francesca N­ ani3, Frederic ­Knoflach3,
Maria‑Clemencia ­Hernandez3, Monika ­Jadi4, Michael J H­ igley11
Yale University, Department of Neuroscience, Kavli Instituteof Neuroscience, New Haven, Connecticut, United States of America;2
Universidad de Valparaiso, Centro Interdisciplinario de Neurociencia
de Valparaiso, Valparaiso, Chile; 3Roche Innovation Center, Roche
Pharmaceutical Research and Early Development, Neuroscience and Rare
Diseases, Basel, Switzerland; 4Yale University, Department of Psychiatry,
New Haven, Connecticut, United States of America
Correspondence: Thomas M Morse (tom.morse@yale.edu)
BMC Neuroscience 2020, 21(Suppl 1):P77
Brain activity is highly regulated by GABAergic activity, which acts via
GABA type A receptors (GABAARs) to suppress somatic spike generation as well as dendritic synaptic integration and calcium signaling.
Tonic GABAergic conductances mediated by distinct receptor subtypes can also inhibit neuronal excitability and spike output, though
the consequences for dendritic calcium signaling are unclear. Here, we
use 2-photon calcium imaging in cortical pyramidal neurons and computational modeling to show that low affinity GABAARs containing
an alpha5 subunit mediate a tonic hyperpolarization of the dendritic
membrane potential, resulting in deinactivation of voltage-gated calcium channels and a paradoxical boosting of action potential-evoked
calcium influx. We also find that GABAergic enhancement of calcium
signaling modulates short-term synaptic plasticity, augmenting
depolarization-induced suppression of inhibition. These results demonstrate a novel role for GABA in the control of dendritic activity andsuggest a mechanism for differential modulation of electrical and biochemical signaling.
Acknowledgements: The authors wish to thank members of the Higley laboratory and Dr. Jessica A. Cardin for helpful comments, the Yale
Center for Research Computing for support with the Yale Farnam highperformance cluster, Henner Knust for compound synthesis, Chiristian Miscenic and Marcello Foggetta for cell transfections and membrane preparations, Judith Lengyel, Gregoire Friz, and Maria Karg forcell line generation and radioligand binding assays, and Marie Claire
Pflimlin for support with electrophysiological characterization of Compound A selectivity. This work was supported by funding from the
NIH/NIMH (R01 MH099045 and MD113852 to MJH, K01 MH097961 to
CQC), funding agencies in Chile (FONDECYT No. 1171840 and MILENIOPROYECTO P09-022-F, CINV to CQC), and Roche Pharmaceutical.

Page 48 of 123

P78
Phasic response of epileptic models
Alberto P Cervera, Jaroslav Hlinka
Institute of Computer Science ‑ Czech Academy of Sciences, Prague,
Czechia
Correspondence: Alberto P Cervera (perez@cs.cas.cz)
BMC Neuroscience 2020, 21(Suppl 1):P78
Although epilepsy is the most chronic neurological disorder, themechanisms underlying the initiation of epileptic seizure remain
unknown. Epileptic seizures are generated by intense activity emerging from a highly synchronized neuronal population. These phenomena are usually preceded and followed by intervals of reduced activity,
known as interictal periods. Importantly, the transient neuronal activity during these interictal periods -known as interictal epileptiformdischarges (IEDs) - is considered a key mechanism governing the transition to seizure. However, whether IEDs prevent or facilitate that transition is still a matter of debate.
In this work, based on previous findings in [1], we show how thesedual effects for IEDs can be interpreted in terms of the phasic responseof a slow-fast system. Indeed, since the phase response of a given system follows from its isochrons distribution, we perform a theoreticaland computational study of the isochrons and phase response curvesof different planar slow-fast epileptic models. Our results unfold thestrong influence of the slow vector field in the phasic response of thesystem to IEDs and suggest theoretical strategies whose effects range
from the short delay to the full suppression of seizures.
Reference
1. Chang WC, et al. Loss of neuronal network resilience precedes seizures anddetermines the ictogenic nature of interictal synaptic perturbations. Nature
Neuroscience. 2018; 21(12): 1742-52.

P79
Frequency‑dependent synaptic gain in a computational modelof mouse thoracic sympathetic postganglionic neurons
Astrid ­Prinz1, Michael M­ cKinnon1, Kun ­Tian1, Shawn H­ ochman2
1
Emory University, Department of Biology, Atlanta, Georgia, United Statesof America; 2Emory University, Department of Physiology, Atlanta, Georgia,
United States of America
Correspondence: Astrid Prinz (astrid.prinz@emory.edu)
BMC Neuroscience 2020, 21(Suppl 1):P79
Postganglionic neurons in the thoracic sympathetic chain represent
the final common output of the sympathetic nervous system. These
neurons receive synaptic inputs exclusively from preganglionic neurons located in the spinal cord. Synaptic inputs come in two varieties:
primary inputs, which are invariably suprathreshold, and secondary
inputs, which exhibit a range of typically subthreshold amplitudes.
Postganglionic neurons typically receive a single primary input and avariable number of secondary inputs in what has been described as an“n+1” connectivity pattern. Secondary inputs have often been viewed
as inconsequential to cell recruitment due to the short duration of
measured synaptic inputs and the relatively low tonic firing rate ofpreganglionic neurons in vivo. However, recent whole-cell patch
clamp recordings reveal that thoracic postganglionic neurons have agreater capacity for synaptic integration than previous microelectroderecordings would suggest. This supports a greater role for secondarysynapses in cell recruitment.
We previously created a conductance-based computational model ofmouse thoracic postganglionic neurons. In the present study, we have
expanded the single-cell model into a network model with synaptic
inputs based on whole-cell recordings. We systematically varied theaverage firing rate of a network of stochastically firing preganglionic
neurons and measured the resultant firing rate in simulated postganglionic neurons. Synaptic gain was defined as the ratio of postganglionic to preganglionic firing rate.
We found that for a network configuration that mimics the typicalarrangement in mouse, low presynaptic firing rates (<0.1Hz) resulted
a synaptic gain close to 1, while firing rates closer to 1Hz resulted ina synaptic gain of 2.5.Synaptic gain diminished for firing rates higher

﻿BMC Neurosci 2020, 21(Suppl 1):54

than ~3Hz (Fig. 1). We also determined that synaptic gain linearly
increases with the number of secondary synaptic inputs (n) within therange of physiologically realistic presynaptic firing rate. Amplitudeof secondary inputs also determines frequency-dependent synaptic
gain, with a bifurcation where secondary synaptic amplitude equals
recruitment threshold. We further demonstrate that the synaptic gain
phenomenon depends on the preservation of passive membrane
properties as determined by whole-cell recordings.

Fig. 1 Effect of secondary inputs on synaptic gain. A Firing rate of
postganglionic neurons as a function of presynaptic firing rate. Eachsimulation includes a single primary input, and n secondary synaptic
inputs. Dashed line is the line of unity. B Synaptic gain as a functionof presynaptic firing rate for postganglionic neurons with different
numbers of secondary inputs
One major biological role of the sympathetic nervous system is theregulation of vascular tone in both skeletal muscle and cutaneous
structures. The firing rate of muscle vasoconstrictor preganglionic
neurons is modulated by the cardiac cycle, while cutaneous vasoconstrictor neurons fire independently of the cardiac cycle. We modulated
preganglionic firing rate according to the typical mouse heart rate
to determine if cardiac rhythmicity changes the overall firing rate of
postganglionic neurons. Cardiac rhythmicity does not appear to havea significant impact on synaptic gain within the physiological range ofpreganglionic input.
Under normal physiological conditions, the unity gain of sympathetic neurons would lead to faithful transmission of central signals
to peripheral targets. However, during episodes of high sympathetic
activation, the postganglionic network can amplify central signals ina frequency-dependent manner. These results suggest that postganglionic neurons play a more active role in shaping sympathetic activity
than previously thought.
P80
Comp‑NeuroFedora, a Free/Open Source operating systemfor computational neuroscience: download, install, research
Ankur ­Sinha1, Aniket ­Pradhan2, Qianqian F­ ang2, Danny L­ ee2, Danishka­Navin2, Alberto R S­ anchez2, Luis ­Bazan2, Luis M S­ egundo2, Alessio C­ iregia2,
Zbigniew J˛edrzejewski‑Szmek2, Sergio ­Pascual2, Antonio ­Trande2, Victor
M T ­Yau2, Morgan ­Hough2
1
University of Hertfordshire, Biocomputation Research Group, Hatfield,
United Kingdom; 2Fedora ProjectCorrespondence: Ankur Sinha (a.sinha2@herts.ac.uk)
BMC Neuroscience 2020, 21(Suppl 1):P80
The promotion and establishment of Open Neuroscience [1] is heavily dependent on the availability of Free/Open Source Software (FOSS)
tools that support the modern scientific process. While more and more
tools are now being developed using FOSS driven methods to ensure
free (as in freedom, and thus also free of cost) access to all, the complexity of these domain specific tools tends to hamper their uptake
by the target audience – scientists hailing from multiple, sometimes
non-computing, disciplines. The NeuroFedora initiative aims to shrink
the chasm between the development of neuroscience tools and theirusage [2].

Page 49 of 123

Using the resources of the FOSS Fedora community [3] to implement
current best practices in software development, NeuroFedora volunteers identify, package, test, document, and disseminate neuroscience
software for easy usage on the general purpose Fedora Linux Operating System (OS). The result is the reduction of the installation/deployment process for this software to a simple two-step process: install any
flavour of the Fedora OS; install the required tools using the in-built
package manager.
To make common computational neuroscience tools even moreaccessible, NeuroFedora now provides an OS image that is ready todownload and use. In addition to a plethora of computational neuroscience software -Auryn [4], NEST [5], Brian [6], NEURON [7], GENESIS [8], Moose [9], Neurord [10], and others - the image also includes
various utilities that are commonly used along with modelling tools,
such as the complete Python science stack. Further, since this image isderived from the popular Fedora Workstation OS, it includes the modern GNOME integrated application suite and retains access to thousands of scientific, development, utility, and other daily use tools fromthe Fedora repositories.
A complete list of available software can be found at the NeuroFedora
documentation at neuro.fedoraproject.org. We invite students, trainees, teachers, researchers, and hobbyists to use Comp-NeuroFedora intheir work and provide feedback. As a purely volunteer driven initiative, in the spirit of the Open Science and FOSS, we welcome everyone
to participate, engage, learn, and contribute in whatever capacity theywish.
References
1. Gleeson P, Davison AP, Silver RA, Ascoli GA. A commitment to open sourcein neuroscience. Neuron. 2017; 96(5): 964-5.
2. Sinha A, et al. NeuroFedora: a ready to use Free/Open Source platform
for Neuroscientists. BMC Neuroscience 20; 1471-2202 at: https://neuro.
fedoraproject.org.
3. Hat R. Fedora Project.
4. Zenke F, Gerstner W. Limits to high-speed simulations of spiking neuralnetworks using general-purpose computers. Frontiers in Neuroinformatics.
2014; 8.
5. Linssen C, et al. NEST 2.16. 0. Jülich Supercomputing Center; 2018.
6. Goodman DF, Brette R. The brian simulator. Frontiers in Neuroscience. 2009;
3: 26.
7. Hines ML, Carnevale NT. The NEURON simulation environment. Neuralcomputation. 1997; 9(6): 1179-209.
8. Bower JM, Beeman D, Hucka M. The GENESIS simulation system. 2003.
9. Dudani N, Ray S, George S, Bhalla US. Multiscale modeling and interoper‑
ability in MOOSE. BMC Neuroscience. 2009; 10(1): 1-2.
10.
Jȩdrzejewski-Szmek Z, Blackwell KT. Asynchronous τ-leaping. The
Journal of Chemical Physics. 2016; 144(12): 125104.

P81
A computational neural model of pattern motion selectivity of MT
neurons
Parvin Zarei E­ skikand1, David ­Grayden2, Tatiana ­Kameneva3, Anthony­Burkitt2, Michael I­bbotson1
1
University of Melbourne, Melbourne, Australia; 2University of Melbourne,
Department of Biomedical Engineering, Melbourne, Australia; 3Swinburne
University of Technology, Telecommunication Electrical Robotics
and Biomedical Engineering, Melbourne, Australia
Correspondence: Parvin Zarei Eskikand (pzarei@unimelb.edu.au)
BMC Neuroscience 2020, 21(Suppl 1):P81
The middle temporal area (MT) within the extrastriate primate visualcortex contains a high proportion of direction-selective neurons.
When the visual system is stimulated with plaid patterns, a range ofcell-specific MT responses are observed. MT neurons that are selective
to the direction of the pattern motion are called “pattern cells”, while
those that respond optimally to the motion of the individual component gratings of the plaid pattern are called “component cells”. The
current theory on the generation of pattern selectivity of MT neurons
is based on a hierarchical relationship between component and pattern MT neurons, where the responses of pattern MT neurons result
from the summation of the responses of component MT neurons [1].

﻿BMC Neurosci 2020, 21(Suppl 1):54

Where the gratings cross in plaids, the crossing junctions of the gratings move in the pattern direction. However, revealing the ends ofthe moving gratings (terminators) in human perceptual experiments
breaks the illusion of the direction of pattern motion: the true directions of motion of the gratings are perceived.
Here, we propose a biologically plausible model of MT neurons thatuses as inputs the known properties of three types of cells in the primary visual cortex (V1): complex V1 neurons, end-stopped V1 neurons(which only respond to the end-points of the stimulus), and V1 neurons with suppressive extra-classical receptive fields. The receptive
fields of the neurons are modelled as spatiotemporal filters. There are
two types of MT neurons: integration MT neurons with facilitatory surrounds and segmentation MT neurons with antagonistic surrounds[2]. A neuron’s pattern or component selectivity is controlled by the
relative proportions of the inputs from the three types of V1 neurons.
The model provides a simple mechanism by which component andpattern selective cells can be described; the model does not requirea hierarchical relationship between component and pattern MT cells.
The results show that the responses of the model MT neurons are highly
dependent on two parameters: the excitatory input that the model neu‑rons receive from the complex V1 neurons with extra-classical RFs andthe inhibitory effect of the end-stopped neurons. The results also show
experimentally observed contrast dependency of the pattern motion
preference of MT neurons: the level of the pattern selectivity of MT neu‑rons drops significantly when the contrast of the bars is reduced.

Page 50 of 123

features, such as their spatial phase, orientation or spatial frequency,
were found to vary continuously. In the radial coordinate, the contrastof these features varied, c = (c1,…,cK) (Fig. 1A, labelled).
We found that the neural response above the spontaneous rate, r0,
was well approximated by a multiplicatively separable function of thefeature-contrast and feature-phase (Fig. 1C): r = fc(c) ­fφ(φ) + ­r0 (Eq.2).
To quantify the accuracy of this approximation, we calculated a relative error between the original and separable forms of the featurecontrast response function (i.e. Eq. (1) & (2)). This relative error varied
between 2% and 18% across the cell population, with a mean of 6%.
This indicates that for most cells, the separable form of the featurecontrast response function was a good approximation.
This result may be interpreted as demonstrating a form of contrast
invariant tuning to feature-phase in V1. This tuning to feature-phaseis given by the function fφ(φ) (Fig. 1E), and the contrast response function is given by fc(c) (Fig. 1D). As several feature characteristics suchas spatial phase, orientation or spatial frequency covary with featurephase, this also leads to contrast invariant tuning under covariation inthese characteristics as feature-phase varies.
Acknowledgements: The authors acknowledge the support the Australian Research Council Centre of Excellence for Integrative Brainfunction (CE140100007), the National Health and Medical ResearchCouncil (GNT1106390), and Lions Club of Victoria.

The presented model solves several problems associated with MT
motion detection, such as overcoming the aperture problem andextracting the correct motion directions from crossing bars. Apartfrom the mechanism of the computation of the pattern motion by MTneurons, the model inherently explains several important propertiesof pattern MT neurons, including their temporal dynamics, the contrast dependency of pattern selectivity, and the spatial and temporal
limits of pattern motion detection.
References
1. Kumbhani RD, El-Shamayleh Y, Movshon JA. Temporal and spatial limits ofpattern motion sensitivity in macaque MT neurons. Journal of Neurophysi‑
ology. 2015; 113(7): 1977-88.
2. Zarei Eskikand P, Kameneva T, Burkitt AN, Grayden DB, Ibbotson MR. Patternmotion processing by MT neurons. Frontiers in neural circuits. 2019; 13: 43.

P82
Contrast invariant tuning in primary visual cortex
Hamish ­Meffin1, Ali ­Almasi1, Michael R ­Ibbotson2
1
National Vision Research Institute, Carlton, Australia; 2Australian College
of Optometry, The National Vision Research Institute, Carlton, Australia
Correspondence: Hamish Meffin (hmeffin@yahoo.com)
BMC Neuroscience 2020, 21(Suppl 1):P82
Previous studies show that neurons in primary visual cortex (V1) exhibit
contrast invariant tuning to the orientation of spatial grating stimuli [1].
Mathematically this is equivalent to saying that their response is a multiplicatively separable function of contrast and orientation.
Here we investigated the contrast dependence of V1 tuning to visualfeatures in a more general framework. We used a data-driven modelling approach [2] to identify the spectrum of spatial features to which
individual V1 neurons were sensitive, from our recordings of single
unit responses in V1 to white (Gaussian) noise and natural scenes. For
each cell we identified between 1 and 5 spatial feature dimensions towhich the cell was sensitive (e.g. Fig. 1A, with 2 feature dimensions; feature 1 & 2 as labelled, with red showing bright and blue showing dark
regions of the feature). The response of a neuron to its set of features
was estimated from the data as the spike rate equal to a function of theindividual feature-contrasts: r = F(c1,…,cK) (Eq. 1) where c1,…,cK are thecontrast levels of a cell’s spatial features, 1,..K, embedded in any stimulus (e.g. Fig. 1B).These features spanned a subspace, giving a spectrumof interpolated features to which the cell was sensitive (Fig.1A, examples labelled). The identity of these features varied along the angularpolar coordinate in this subspace, which we term the feature-phase,φ(Fig. 1A, labelled). In this angular dimension, characteristics of theFig. 1 A Feature subspace of a cell. B Original feature-contrast
response function. C Separable approximation of the feature-contrast
response function. D Separable contrast response function. E Separa‑ble feature-phase tuning function
References
1. Alitto HJ, Usrey WM. Influence of contrast on orientation and temporalfrequency tuning in ferret primary visual cortex. Journal of neurophysiology.
2004; 91(6): 2797-808.
2. Almasi A, et al. Mechanisms of feature selectivity and invariance in primaryvisual cortex. bioRxiv 2020.

P83
One‑shot model to learn 7 quantal parameters simultaneously,
including desensitization
Emina Ibrahimovic
University of Zurich and Elvesys, Zurich, Switzerland
Correspondence: Emina Ibrahimovic (emina.ibrahimovic@imls.uzh.ch)
BMC Neuroscience 2020, 21(Suppl 1):P83
Synaptic transmission is the hallmark behind informative and adaptive
cognitive processes. Neurons store and enable retrieval of information by connecting via synapses. They can modulate the signal either
to maintain stability or decrease or increase their strength. To improve
quantification of synaptic dynamics with focus on computational efficiency and number of parameters, a fast variant of the binomial model
is applied to estimate θ={N,p,q,σ,τd,τf,τdes}.

﻿BMC Neurosci 2020, 21(Suppl 1):54

There is up to date no efficient method to simultaneously quantify
many synaptic parameters from physiological recordings. Generative models have heavy computational loads as they depend on thenumber of release sites N [1], and need to compute multiple iterations. Empirical methods usually require specific conditions, such aschanging the release probability p or requiring regular inter spikeintervals, which makes them unsuitable for fitting physiological data.
The fast algorithm proposed here is based on non-linear least squares
to retrieve quantal parameters. We apply this model to spontaneous
excitatory postsynaptic potentials (EPPs) patch-clamped at an in vitro
mouse neuromuscular junction (NMJ) taken in [2] (Fig. 1A). Results incontrol are relatively in line with estimations found in vivo [3] (Fig. 1C).
Estimated quantal parameters at control are subsequently comparedwith data undergoing curare poison (Fig. 1B). The addition of curare
shows a drop in quantal size q as expected. Other parameters remain
stable suggesting no homeostatic compensatory mechanisms in thiscase. The model is validated on synthetic data and compared with an
iterative expectation-maximization technique. The supremacy of thisone-shot method allows to retrieve many parameters without multiple batches. In contrast to [1], its temporal complexity is independentof the number of release sites. This method is adaptable to generalsynaptic recording, which makes it less constrained. It is also the first
phenomenological model that incorporates desensitization τdes. The
framework gives insights on synaptic weights and their long-shortdynamics. It permits to correlate between subjective and objective
variabilities in healthy and pathological cases to molecular functions.

Fig. 1 In vitro mouse NMJ EPPs with model reconstruction (blue). A
Control: number of release sites N=100, release probability p=0.4, quan‑tal size q=0.13mV, background noise σ=0.2mV, depression time con‑stant τd=95ms, refilling time constant τf=50ms, desensitization τdes=30ms. B Curare administration: N=100, p=0.5, q=0.05mV, σ=0.1mV,
τd=45ms, τf=50ms, τdes=30ms. C Cartoon of a synaptic junction. Com‑
parison of estimation of parameters with rat NMJ can be found in [3]Page 51 of 123

References
1. Barri A, Wang Y, Hansel D, Mongillo G. Quantifying repetitive transmission atchemical synapses: a generative-model approach. eNeuro. 2016; 3(2).
2. Vilmont V, Cadot B, Ouanounou G, Gomes ER. A system for studying
mechanisms of neuromuscular junction development and maintenance.
Development. 2016; 143(13): 2464-77.
3. Wilson DF. Estimates of quantal-release and binomial statistical-releaseparameters at rat neuromuscular junction. American Journal of PhysiologyCell Physiology. 1977; 233(5): C157-63.

P84
Brainpower – investigating the role of power constraints in neuralnetwork plasticity
Silviu ­Ungureanu1, Mark van R­ ossum2
1
University of Nottingham, School of Psychology, Nottingham, United
Kingdom; 2University of Nottingham, School of Psychology and Schoolof Mathematical Sciences, Nottingham, United Kingdom
Correspondence: Silviu Ungureanu (silviu.ungureanu@nottingham.
ac.uk)BMC Neuroscience 2020, 21(Suppl 1):P84
The central nervous system consumes approximately 20W of metabolic power in humans [1]. This is used for neural communication, andalso for neural plasticity and the formation of new memories. Persistent forms of plasticity in particular consume so much energy that
under sudden food scarcity, associative learning significantly reduces
lifespan of fruit flies [2].
It is reasonable therefore that neural plasticity has evolved to learn at
minimal power. However, how this changes plasticity and learning is
not known. While previous work has considered an energy constraint[3], a power constraint might be more biological as, unlike many othertissues, the brain cannot store energy. A power constraint might beable to explain why plasticity induction requires a refractory time
before it can be induced again [4], as well as spatial competition inplasticity between synapses and neurons [5,6].
Here, we developed a computational model of plasticity to examine
the effect of a power constraint on plasticity dynamics. We first use astandard perceptron augmented with two types of synaptic weights:
an inexpensive transient, decaying component and a costly longterm component, formed by the simultaneous consolidation of all
the transient weights. We further assume that the brain attempts toconsolidate new memories as soon as it is able to. Hence, the interval
between consolidation events is limited and synaptic consolidation
events occur at a fixed frequency, representing the refractory periodcaused by a dearth of energy. Higher consolidation frequencies correspond to more available power, and vice-versa. The perceptron istrained on a random-generated set of binary patterns until it correctly
learns the output value for each pattern.
Results show that the power in the system has a significant impact on
the training time. Unexpectedly, increasing the period between consolidations - thus reducing power - can reduce the required number of
epochs by as much as 30%, depending on the strength of the weightdecay, the number of patterns P in the training set, and the numberof synapses N. Further increasing the period between consolidations
increases the training time. This increase occurs not gradually, but in astaircase pattern, peaking whenever the period is 0 modulo P (Fig. 1).

﻿BMC Neurosci 2020, 21(Suppl 1):54

Fig. 1 A perceptron with 500 synapses trained on 800 patterns. In
blue, number of epochs necessary for the perceptron to learn theentire training set, as a function of the interval between consolida‑tion events. In red, the number of epochs needed by a standard
perceptron
The consequences of a power constraint are further explored in a
multi-layer neural network and extended to a probabilistic model
where the probability of consolidation increases proportional to thetime since the previous consolidation event.
In summary, our results show that incorporation of a metabolic power
constraint in synaptic plasticity can lead to important changes in thelearning dynamics.
References
1. Attwell D, Laughlin SB. An energy budget for signaling in the grey matterof the brain. Journal of Cerebral Blood Flow & Metabolism. 2001; 21(10):1133-45.
2. Mery F, Kawecki TJ. A cost of long-term memory in Drosophila. Science.
2005; 308(5725): 1148-.
3. Li HL, Van Rossum MC. Energy efficient synaptic plasticity. Elife. 2020;
e50804.
4. Kramár EA, et al. Synaptic evidence for the efficacy of spaced learning.
Proceedings of the National Academy of Sciences. 2012; 109(13): 5121-6.
5. Sajikumar S, Morris RG, Korte M. Competition between recently potentiatedsynaptic inputs reveals a winner-take-all phase of synaptic tagging andcapture. PNAS 2014; 111: 12217.
6. Josselyn SA, Tonegawa S. Memory engrams: Recalling the past and imagin‑
ing the future. Science. 2020; 367: 6473.

Page 52 of 123(H-H) equations can calculate action potentials based on ion channels.
H-H does not consider morphology, however, on actual cell membranes, the number of exiting ion channels and locations are basedon shapes of neurons. How quick or slow soma can produce action
potentials are depend on how narrow and how many ion channels on
the producing area. Therefore, we assume that there are strong relationships between cell shapes and characteristics of action potentials.
Expanded H-H equations can adapt to the quickness of producing
action potential by adding axon hillock parameters.
Connectivity between neurons is also important. Geometry varies
according to cell types. Purkinje cells which are one of the inhibitory
neurons have complex branches of the dendritic arbor. On the other
hand, Pyramidal cells which are one of the excitatory neurons andmultipolar type neurons have one axon and many dendrites, but thecomplexity of geometry is simpler than Purkinje cells. These differentials of geometry cause differences in connectivity.
In this paper, we propose a new neuron growth and deal model andsimulator considered neuron morphology and connectivity between
multi cell types. In our model, a characteristic of a growth cone isapplied to neuron growth and treated as a navigation system, an
L-system is adapted for creating the geometry of each neuron, andLife game is embedded for a cell division rule.
We also adopt glial cells for neuron growth, not only stimulus from otherneurons. In our model, each neuron receives the energy for growing
from contacted astrocytes which are one of the glial cells. The directionof growth of the growth cones has determined by set goal areas for far,and during growing, growth cones try to contract near oligodendrocytes
to obtain myelin around their axons. A cell division rule for Oligodendrocytes follows life game rules. The glial cells are treated as obstacles.
In our simulation system, a user can create various types of neurons, set
the goals for both dendrites and axons, create connections between
various functions and geometries of neurons with growth rules and add
injections such as inhibitory postsynaptic potential (IPSP) or excitatory
postsynaptic potential (EPSP) on purpose to calculate action potentials.
In conclusion, Fig. 1 shows simulation results of our proposed model.
In our simulator, variety of geometry can be produced automatically
based on expanded L-system, variety and flexible connectivity can bealso produced based on our proposed new neuron growth and deathmodel. Furthermore, we added two types of glial cells for growth andgoal rules and also treat as obstacles. Our proposed model and simulator is quite flexible to simulate cell geometry, action potentials, cellconnections in each brain region.

P85
The Neuron growth and death model and simulator
Yuko Ishiwaka, Tomohiro Yoshida, Tadateru Itoh
SoftBank Corp., Technology Unit, Hakodate, Japan
Correspondence: Yuko Ishiwaka (yuko.ishiwaka@g.softbank.co.jp)
BMC Neuroscience 2020, 21(Suppl 1):P85
Some kinds of characteristics of neurons depend on morphology.
There are many neuron types in a brain and the functions of each cell
type are varied. For example, auditory cells that receive sound stimulus from the external world and pyramidal cells that relate to thinking and memory have different functions. A bushy cell which is oneof the sensory neurons of auditory treats tempo of sounds, therefore
the immediate responses are required for producing action potential
and short time refractory period. On the other hand, pyramidal cells
that mainly exist in the hippocampus and amygdala treat memory andemotion, therefore the producing action potential is slower than sensor neurons and the refractory period is longer. Hodgkin and Huxley

Fig. 1 Simulation results of different geometries and neuron connec‑
tions. The upper red neurons are simulated as a Purkinje, middle blue
neurons are simulated as a Pyramidal cell, and the bottom neurons
show connections and created action potentials by expanded H-H
model﻿BMC Neurosci 2020, 21(Suppl 1):54

P86
Studying neural mechanisms in recurrent neural network trained
for multitasking depending on a context signal.
Cecilia Jarne
National University of Quilmes and CONICET, Departement of Scienceand Technology, Quilmes, Argentina
Correspondence: Cecilia Jarne (katejarne@gmail.com)
BMC Neuroscience 2020, 21(Suppl 1):P86
Most biological brains, as well as artificial neural networks, are capable of performing multiple tasks [1]. The mechanisms through which
simultaneous tasks are performed by the same set of units are notyet entirely clear. Such systems can be modular or mixed selective
through some variable such as sensory stimulus [2,3]. Based on simple
tasks studied in our previous work [4], where tasks consist of the processing of temporal stimuli, we build and analyze a simple model thatcan perform multiple tasks using a contextual signal. We study various
properties of our trained recurrent networks, as well as the responseof the network to the damage done in connectivity. In this way weare trying to illuminate those mechanisms similar to those that could
occur in biological brains associated with multiple tasks.
References
1. Yang GR, Joglekar MR, Song HF, Newsome WT, Wang XJ. Task representa‑
tions in neural networks trained to perform many cognitive tasks. Nature
Neuroscience. 2019; 22(2): 297-306.
2. Yang GR, Cole MW, Rajan K. How to study the neural mechanisms of multi‑
ple tasks. Current Opinion in Behavioral Sciences. 2019; 29: 134-43.
3. Rigotti M, et al. The importance of mixed selectivity in complex cognitive
tasks. Nature. 2013; 497(7451): 585-90.
4. Jarne C, Laje R. A detailed study of recurrent neural networks used to modeltasks in the cerebral cortex. arXiv preprint arXiv:1906.01094. 2019.

P87
Centrality of left inferior frontal gyrus reveals important aspectof motor learning generalization
Youngjo Song, Sang Jin Jang, Pyeong‑soo Kim, Byung Hyug Choi,
Jaeseung Jeong
Korea Advanced Institute of Science and Technology, Bio and Brain
Engineering, Daejeon, South Korea
Correspondence: Youngjo Song (syj1455@kaist.ac.kr)
BMC Neuroscience 2020, 21(Suppl 1):P87
Generalization of learning refers to the phenomenon in which knowledge learned in one context enhances performance in another context. Although the learning environment can never be precisely
the same in the real world, animals including humans demonstrate
excellent flexibility to adapt their learned skills in a new environment.
Despite the universal occurrence of generalization phenomena indaily life, there is much lack of understanding about how the brain
generalizes the skills and knowledge into different environments. In
particular, most of the previous studies have only focused on identifying the cerebral networks used during the generalization stage, butfailed to determine the elements during the preceding learning stage
that could have enabled generalization. Thus, the aim of this study
was to enhance understanding of the neural mechanisms that enablegeneralization, particularly the generalization of motor learning. In
this study, we designed a new experimental paradigm called ‘mirrorerasing generalization task.’ The subjects erased (1) a simple shape(square) for the training session, and (2) a complex shape (cursivealphabet letter y) for the generalization session, which took place both
before and after the training session, in an MRI scanner. We found thatthe subjects successfully generalized their motor skills (p < 0.0001)
acquired during square-erasing to the letter-erasing context. However,counterintuitively, skill improvement during training did not correlatewith the generalization of motor skills (p ~ 0.1). This result implicatesthat the dynamics underlying generalization is possibly nonlinear, andthat performance enhancement in one specific context is not a reliablemeasure to estimate the generalization performance in another. Then,
we computationally modeled the neuronal circuitry responsible formotor learning generalization and used the fMRI machine to construct

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a functional network model (using frequency between 0.049Hz and
0.09Hz). More interestingly, we found that the betweenness centralityof pars opercularis of left inferior frontal gyrus (IFG) had a significant
correlation with the generalization performance (R ~ 0.8, p < 0.05, FWE
corrected), which was the only measure before generalization sessionthat correlated with generalization performance. We should note thatthe human pars opercularis of the left IFG has been considered as partof a mirror neuron system, which is currently hypothesized to be facilitating motor abstraction. This finding suggests that the IFG-mediatedabstraction of new motor skill acquired during training may be the key
to generalization of the learned skills in a different context. This study
potentially provides evidence for the contemporary view that abstraction plays an essential role in generalization. Furthermore, we suggestthat the centrality of par opercularis of the left IFG is possibly used tomake predictions about future generalization performance, which
opens new possibilities in motor rehabilitation. This study suggeststhat measuring brain functional networks of the patients undergoing rehabilitation programs potentially predict how much their motor
function would be improved in real life.
Acknowledgements: This study was conducted as part of Global Singularity Research Program for 2020 financially supported by KAIST.
P88
Phosphorylation‑induced variation of NF kinetics and morphologyof axonZelin Jia, Yinyun Li
Beijing Normal University, School of System Science, Beijing, China
Correspondence: Zelin Jia (zljia@mail.bnu.edu.cn)
BMC Neuroscience 2020, 21(Suppl 1):P88
Neurofilaments (NFs) are transported along microtubule tracks in theaxons and are gradually phosphorylated in this process [1]. At thenode of Ranvier, the axon is not encased by myelin sheath. It is indicated that phosphorylation of NFs can reduce the transport rate of
NFs, and therefore influence the formation of axon morphology [2].
In the theory of slow axonal transport, the “stop-and-go” model [3]can well describe the random kinetic behavior of NFs in axons. On thebasis of “stop-and-go” model, we introduce the conversion between
phosphorylation and dephosphorylation of neurofilaments and build
the “eight-state” model. We assume that the phosphorylation anddephosphorylation of NFs has different “on-track” rate (γon), so as toachieve the effect of phosphorylation on the transport [4,5]. Throughour theoretical derivation and simulation, we draw the conclusion thatthe modification on the “on-track” rate and the conversion between
phosphorylation and dephosphorylation can both slow down the NFs
transport along the axon. Our conclusion is also consistent with theContinuity equation that flux by the multiplication of the number of
NFs and average velocity is constant at equilibrium state.
References
1. Nixon RA, Paskevich PA, Sihag RK, Thayer CY. Phosphorylation on carboxyl
terminus domains of neurofilament proteins in retinal ganglion cell
neurons in vivo: influences on regional neurofilament accumulation,interneurofilament spacing, and axon caliber. The Journal of cell biology.
1994; 126(4): 1031-46.
2. Jung C, Yabe JT, Shea TB. C-terminal phosphorylation of the high molecularweight neurofilament subunit correlates with decreased neurofilamentaxonal transport velocity. Brain research. 2000; 856(1-2): 12-9.
3. Wang L, Brown A. Rapid intermittent movement of axonal neurofilaments
observed by fluorescence photobleaching. Molecular biology of the cell.
2001; 12(10): 3257-67.
4. Yabe JT, Pimenta A, Shea TB. Kinesin-mediated transport of neurofilament
protein oligomers in growing axons. Journal of cell science. 1999; 112(21):3799-814.
5. Yabe JT, Jung C, Chan WK, Shea TB. Phospho‐dependent association of neu‑
rofilament proteins with kinesin in situ. Cell motility and the cytoskeleton.
2000; 45(4): 249-62.

﻿BMC Neurosci 2020, 21(Suppl 1):54

P89
Quantification of changes in motor imagery skill
during brain‑computer interface use
James Bennett, David Grayden, Anthony Burkitt, Sam John
University of Melbourne, Department of Biomedical Engineering,
Melbourne, Australia
Correspondence: James Bennett (bennettj1@student.unimelb.edu.au)
BMC Neuroscience 2020, 21(Suppl 1):P89
Oscillatory activity over the sensorimotor cortex, known as sensorimotor rhythms, can be modulated by the kinaesthetic imagination oflimb movement [1]. These event-related spectral perturbations can beobserved in electroencephalography (EEG), offering a potential way
to restore communication and control to people with severe neuromuscular conditions via a brain-computer interface (BCI). However,
the ability of individuals to produce these modulations varies greatly
across the population. Between 10-30% of people are unable to influence their SMRs sufficiently to be distinguishable by a BCI decoder [2].
Despite this, it has been shown that users can be trained to improvethe extent of their SMR modulations. This research utilised a datadriven approach to characterise the skill development of participants
undertaking a left- and right-hand motor imagery experiment.
Two publicly available motor imagery EEG datasets were analysed.
Dataset 1 consisted of EEG data from 47 participants performing 200
trials of left- and right-hand motor imagery within a single session [3].
No real-time visual feedback was provided to the participants. Dataset
2 contained EEG from two sessions of 200 trials each from 54 participants [4]. Visual feedback was provided to users in the second session
but not in the first. Various metrics characterising mental imagery skill
were calculated across time for each participant.
The discriminability of EEG in the 8-30Hz range from left- and righthand trials was found to increase across time for both datasets.
Despite the overall improvement, there was great variability in thechange of motor imagery skill across participants. For Dataset 1, theaverage change across time of the metric representing the discriminability of classes was 6.0±21.9%. For Sessions 1 and 2 of Dataset 2,
the discriminability increased by 11.8±44.0% and 17.4±30.7%, respectively. Session 2 of Dataset 2 contained visual feedback and produceda larger overall improvement in motor imagery skill with a lower variability compared with Session 1.
In this work, we investigated the level of motor imagery skill acquisition during BCI use. The results indicate a baseline level of skill
improvement that can be expected, and also emphasise the large variability across participants commonly seen in BCI studies. Overall, we
provide a useful reference of BCI skill acquisition for future researchthat seeks to increase the rate of skill improvement and decrease theamount of variability.

Page 54 of 123

An almost ubiquitous approach taken in systems neuroscience is thatof devising stimulus response functions (SRFs), which specify how astimulus is encoded into a neural response, e.g. place fields, tuning
curves [1]. However, it is clear that the brain itself is able to infer properties of the environment in real time via neural activity alone, without having to resort to performing experiments on its own responseto stimuli. A central aim in this work is to explore the relationshipbetween the geometric/topological structure of the stimulus space(also animal behaviour) and neural activity and to investigate to what
extent the former can be derived from the latter without having toperform the standard operation of constructing dictionaries between
the two as provided by an SRF. In the last decade, successful attempts
have been made to eliminate this albeit very useful middleman using
topological data analysis (TDA) [2,3].
We build on an approach initiated in [3], which uses clique topology, aform of TDA. Common statistics related to neural activity and connectivity derived from experimental data are often presented in the formof a matrix of correlations or connectivity strengths between pairs ofneurons, voxels, etc. Analogous statistics can be obtained from stimuli
presented to the animal, e.g. textures both visual, auditory, images ofnatural scenes, olfaction. Clique topology enables us to test whether
signatures of structures of stimulus spaces and environments aredetectable in the correlation structures of the raw data obtained fromneuronal recordings. The advantage of using clique topology over traditional eigenvalue-based methods is that the latter is badly distorted
by monotone nonlinearities, whereas the information encoded in the`order complex’ is invariant under such transformations.
The statistical topological approach in [3] could determine whether
correlations resulting from both the stimulus and response side wererandom or induced by a geometric process (e.g. pairwise distances
obtained via sampling points from a unit cube in Rd). Here we introduce a new regime of complexes that are derived instead from textures. In [3] experimental scenarios were considered where neurons
were tuned to features lying in a continuous coding space where correlations decrease with distance, e.g. hippocampal place cells. Textures
are in many ways the antithesis of this and also exhibit both repetitiveand random features. Clique topology techniques on textures have led
us to a menagerie of order complexes which have very small values for
Betti numbers throughout the filtration as compared to same-sized‘random’ and ‘geometric’ order complexes (Fig. 1). Analogous Betti
curves have been shown to have been induced by order complexes
derived from low-rank matrices by Curto (unpublished). The matricesthat emerge from textures, however, do not generally have low-rank
and it is an open question as to whether the two can be related.

References
1. Pfurtscheller G, Da Silva FL. Event-related EEG/MEG synchronization anddesynchronization: basic principles. Clinical Neurophysiology. 1999; 110(11):1842-57.
2. Allison BZ, Neuper C. Could anyone use a BCI? In Brain-computer interfaces
2010 (pp. 35-54). Springer, London.
3. Cho H, Ahn M, Ahn S, Kwon M, Jun SC. EEG datasets for motor imagery
brain–computer interface. GigaScience. 2017; 6(7): gix034.
4. Lee MH, et al. EEG dataset and OpenBMI toolbox for three BCI paradigms:
an investigation into BCI illiteracy. GigaScience. 2019; 8(5): giz002.

P90
Texture‑like neural representations
Emil ­Dmitruk1, Ritesh ­Kumar1, Volker ­Steuber2, Michael ­Schmuker2, Carina­Curto3, Vladimir ­Itskov3, Shabnam K­ adir4
1
University of Hertfordshire, School of Engineering and Computer Science,
Hatfield, United Kingdom; 2University of Hertfordshire, Biocomputation
Research Group, Hatfield, United Kingdom; 3Pennsylvania State University,
Department of Mathematics, State College, Pennsylvania, United Statesof America; 4University of Hertfordshire, Centre for Computer Scienceand Informatics Research, Hatfield, United Kingdom
Correspondence: Emil Dmitruk (e.dmitruk@herts.ac.uk)
BMC Neuroscience 2020, 21(Suppl 1):P90

Fig. 1 A An example of matrix ordering (values ordered according to
the scale). B Derivation of order complex from a visual texture using
Gabor filters. C Order matrices and Betti curves for textures (bricks,
water) and olfactory dataset; images of textures are shown as insets
on plots. D Order complexes (from A) as a simplicial complexes. E
Betti curves for random and geometry matrix

﻿BMC Neurosci 2020, 21(Suppl 1):54

We were surprised to find that datasets from a wider range of modalities appear to exhibit texture-like rather than ‘geometric’ structure. We
have extracted texture-like order complexes from olfactory datasets[4] as well as a simulated dataset from a spiking neural network modelling speech recognition.
References
1. Meyer AF, Williamson RS, Linden JF, Sahani M. Models of neuronal stimulusresponse functions: elaboration, estimation, and evaluation. Frontiers in
Systems Neuroscience. 2017; 10: 109.
2. Curto C, Itskov V. Cell groups reveal structure of stimulus space. PLoS Com‑
putational Biology. 2008; 4(10): e1000205.
3. Giusti C, Pastalkova E, Curto C, Itskov V. Clique topology reveals intrinsic
geometric structure in neural correlations. Proceedings of the National
Academy of Sciences. 2015; 112(44): 13455-60.
4. Si G, et al. Structured odorant response patterns across a complete olfactory
receptor neuron population. Neuron. 2019; 101(5): 950-62.

Page 55 of 123

physiological experiments, or were generated by more complex models than the one we were fitting.
We then tested the various algorithms on the different model optimization tasks, and compared the final error (after 10,000 model evaluations) and also the convergence speed (Fig. 1). We found that several
evolutionary and related search algorithms delivered consistently
good results across our entire test suite, even for higher-dimensional,
multi-objective problems. Therefore, we would recommend trying
these algorithms first for novel optimization problems. We also hope
to extend our test suite with new problems and algorithms.

P91
A unified framework for the application and evaluation of different
methods for neural parameter optimization
Máté Mohácsi1, Sára Sáray1, Márk P Török2, Szabolcs K­ ali2
1
Pázmány Péter Catholic University, Faculty of Information Technology
and Bionics, Budapest, Hungary; 2Institute of Experimental Medicine,
Hungarian Academy of Sciences, Budapest, Hungary
Correspondence: Szabolcs Kali (kali@koki.hu)
BMC Neuroscience 2020, 21(Suppl 1):P91
Currently available experimental data make it possible to create complex multicompartmental conductance-based models of neurons. In
principle, such models can approximate the behavior of real neurons
very well. However, these models have many parameters and some ofthese parameters often cannot be directly determined in experiments.
Therefore, a common approach is to tune parameter values to bring
the physiological behavior of the model as close as possible to theexperimental data. Rather than tuning the parameters by hand, a moreprincipled way of determining good model parameters is to carry out
a systematic parameter search using an appropriate global optimization algorithm. Although many such algorithms have been developed
and applied successfully in various domains, and high-quality general implementations of many popular algorithms are available, themajority of these solutions have not been tested in a neural context.
Our goal in this study was to create a software tool that provides uniform access to a large variety of different optimization algorithms; todevelop a set of benchmark problems for neural parameter tuning;
and to systematically evaluate and compare the various algorithms
and implementations using our software and benchmarking suite.
We have created an updated and enhanced version of our previously
developed software tool. In Optimizer, model evaluations can be performed either by the NEURON simulator (handled internally) or any
external (black-box) simulator. All functionalities can be accessed fromthe graphical user interface; there is also a command line interface
for batch processing. The new version was developed in Python 3 tosupport recent open-source Python modules. The repertoire of algorithms was extended by several new methods that proved effective inother studies. For many of these search algorithms, parallel optimization is also supported. A wide variety of features (including those in
the eFEL package) can be used to evaluate the error of the optimization; multiple, weighted features are also supported. Our optimization
tool currently supports about fifteen different optimization algorithms
implemented by four separate Python packages: Inspyred, Pygmo,
BluePyOpt, and Scipy.
Our neural optimization benchmark suite includes six separate problems that differ in complexity, model type, simulation protocol, fitness
functions, and the number of unknown parameters. Our examples
range from the classical Hodgkin-Huxley model (3 conductanceparameters) to an extended integrate-and-fire model (10 parameters) and a morphologically and biophysically detailed hippocampal
pyramidal cell (16 parameters). Some of our benchmarks use target
data generated by a neuronal model with known parameters. However, in most of our benchmarks, the target data were recorded inFig. 1 Rankings of the algorithms based on their combined perfor‑
mance on our multi-objective test case. Top: Ranking based on the
final error after 10,000 model evaluations. Bottom: Ranking based on
the area under the error curve (convergence speed). Lower scoresindicate better performance. Red bars are for multi-objective algo‑rithms, blue bars for single-objective algorithms

P92
Modelling the responses of ON and OFF retinal ganglion cells
to infrared neural stimulation
James ­Begeng1, Wei ­Tong2, Michael R I­bbotson2, Paul S­ toddart3, Tatiana­Kameneva4
1
Swinburne University of Technology, Faculty of Science, Engineeringand Technology, Melbourne, Australia; 2Australian College of Optometry,
The National Vision Research Institute, Melbourne, Australia; 3Swinburne
University of Technology, ARC Training Centre in Biodevices, Melbourne,
Australia; 4Swinburne University of Technology, Telecommunication
Electrical Robotics and Biomedical Engineering, Melbourne, Australia
Correspondence: James Begeng (jbegeng@swin.edu.au)
BMC Neuroscience 2020, 21(Suppl 1):P92
Retinal degenerative diseases such as retinitis pigmentosa and agerelated macular degeneration cause progressive photoreceptor
loss leading to partial or total patient blindness. Retinal prostheses
attempt to obviate this loss of photoreceptors by direct stimulationof the underlying retinal ganglion cell (RGC) circuitry, and are capableof restoring limited visual sensation to blind patients. Because thesedevices typically inject current through implanted electrode arrays,
their spatial resolution is significantly limited, and their capacity forselective stimulation of distinct RGC types has not yet been established. In particular, selective stimulation of ON and OFF RGCs (which
exhibit opposite light responses in vivo) constitutes a long-standing
open problem in retinal prosthesis design.
Infrared neural modulation (INM) uses pulsed infrared light to deliver
sharp thermal transients to neural tissue, and is capable of both neuralstimulation and inhibition with a high spatial precision. This techniquerelies on at least two distinct mechanisms: a temperature gradientdependent capacitive current, and thermosensitive activation of the
TRPV ion channels. For retinal prostheses, this high stimulus resolution
offers an attractive alternative to the low resolution of current electrical prostheses; however, it is unclear how infrared-evoked currents

﻿BMC Neurosci 2020, 21(Suppl 1):54

may vary between the wide variety of RGC types in mammalian
retina, or whether these differences may be harnessed for selective
stimulation.
In this study, a single-compartment Hodgkin-Huxley-type model
was simulated in a NEURON environment. The model included leak,sodium, potassium, calcium and low voltage activated calcium currents based on published data [1,2]. Thermally-evoked currents were
simulated by a dT/dt dependent capacitive current based on GCS theory of bilayer capacitance [3].
Our results show that INM responses differ between ON and OFF RGCs.
In particular, OFF cells have a prolonged depolarisation in response to
millisecond timescale heat pulses, whilst ON cells exhibit a short depolarisation with a larger post-pulse hyperpolarisation. This difference ismainly due to the low voltage activated calcium current that is present
in OFF and absent in ON RGCs. This prediction is yet to be confirmed
experimentally, but may have important implications for the development of infrared retinal prostheses.
References
1. Fohlmeister JF, Miller RF. Impulse encoding mechanisms of ganglion cells
in the tiger salamander retina. Journal of Neurophysiology. 1997; 78(4):1935-47.
2. Wang XJ, Rinzel J, Rogawski MA. A model of the T-type calcium current andthe low-threshold spike in thalamic neurons. Journal of Neurophysiology.
1991; 66(3): 839-50.
3. Eom K, Byun KM, Jun SB, Kim SJ, Lee J. Theoretical study on gold-nanorodenhanced near-infrared neural stimulation. Biophysical Journal. 2018;
115(8): 1481-97.

P93
Investigation of Stimulation Protocols in Transcutaneous Vagus
Nerve Stimulation (tVNS)Charlotte ­Keatch1, Paul S­ toddart2, Elisabeth L­ ambert3, Will ­Woods4,
Tatiana ­Kameneva5
1
Swinburne University of Technology, Biomedical Engineering,
Melbourne, Australia; 2Swinburne University of Technology, ARC Training
Centre in Biodevices, Melbourne, Australia; 3Swinburne Universityof Technology, Department of Health and Medical Sciences, Melbourne,
Australia; 4Swinburne University of Technology, Faculty of Health, Arts
and Design, Melbourne, Australia; 5Swinburne University of Technology,
Telecommunication Electrical Robotics and Biomedical Engineering,
Melbourne, Australia
Correspondence: Charlotte Keatch (ckeatch@swin.edu.au)
BMC Neuroscience 2020, 21(Suppl 1):P93
Transcutaneous vagus nerve stimulation (tVNS) is a type of non-invasive brain stimulation that is used increasingly in the treatment of anumber of different health conditions such as epilepsy and depression. Although there is a great deal of research into different medical conditions that can be improved by tVNS there is little conclusive
evidence into the optimal stimulation parameters, such as stimulation
frequency, pulse type or amplitude. Understanding whether variation of these stimulation parameters can directly influence the brain
response could improve treatment delivery.
The aim of this project is to determine whether varying the stimulation parameters of tVNS can influence the induced brain response,and if there is an optimal set of stimulation parameters that can be
determined for targeted treatment of different medical conditions.
Twenty healthy participants were selected based on their suitability
for both magnetoencephalography (MEG) and magnetic resonanceimaging (MRI) based on predetermined exclusion criteria. The experimental sessions were carried out at the Swinburne Imaging Facility,
Swinburne University of Technology. Four different stimulation protocols were delivered via electrical stimulation to the left ear; active stimulation to the cymba concha at stimulation frequency of 24 Hz regularpulses, sham stimulation to the ear lobe at stimulation frequency of
24 Hz regular pulses, stimulation to the cymba concha at stimulation
frequency of 1 Hz regular pulses, and stimulation to the cymba concha
at stimulation frequency of 24 Hz pulse frequency modulated (PFM)
pulses (modulated at 6 Hz).

Page 56 of 123

Participant brain dynamics were analysed in response to stimulation
through different signal processing techniques. First the raw data waspassed through the software MaxFilter which uses Signal Space Separation (SSS) of Maxwell’s equations to remove major sources of noise
and artifacts. The stimulation artifact was then removed from thedata by spline interpolation, which removed part of the data from theonset of the stimulation pulse and then interpolated to reconstruct
the signal. The data was then downsampled and filtered before applying Fast Fourier Transforms (FFT) to obtain power spectrums at sensor level. The response to different protocols could be contrasted bytaking ratios for all participants and was then averaged to see groupresponse at sensor level.
Preliminary results show that results vary between individuals, withdifferent brain areas activated due to the stimulation. Comparison
between the active stimulation of the vagus nerve at 24Hz to shamstimulation of the ear lobe shows a dipole response to the active
stimulation in the parietal lobe. Comparison of the PFM stimulation with the regular 24 Hz stimulation of the vagus nerve shows an
inhibited response in the modulation frequency of 6 Hz in comparison
with other frequency bands. Finally, comparing the 1 Hz with the 24
Hz active stimulation of the vagus nerve shows that the 1 Hz stimulation drives the brain more strongly than the 24 Hz across all frequency
bands. These preliminary results may be used as a stepping-stone toinvestigate the effect of tVNS on brain dynamics and setting up stimulation protocols that may have therapeutic effects.
P94
Brain rhythms enhance top‑down influence on V1
responses in perceptual learning
Ryo ­Tani1, Yoshiki ­Kashimori2
1
The University of Electro‑Communications, Tokyo, Japan; 2The University
of Electro‑Communications, Department of Engineering Science, Tokyo,
Japan
Correspondence: Ryo Tani (r.tani@uec.ac.jp)
BMC Neuroscience 2020, 21(Suppl 1):P94
Visual information is conveyed in a feedforward manner to progressively higher levels in the hierarchy, beginning with the analysis ofsimple attributes, such as orientation and contrast, and leading tomore complex object features from one stage to the next. In contrast,
visual systems have abundant feedback connections, whose number
is even larger than the feedforward ones. Top-down influences, conveyed by the feedback pathways across entire brain areas, modulate
the responses of neurons in early visual areas, depending on cognition and behavioral context. Li et al. [1] showed that top-down signals
allowed neurons of the primary visual cortex (V1) to engage stimulus
components that were relevant to a perceptional task and to discard
influences from components that were irrelevant to the task. They
showed that V1 neurons exhibited characteristic tuning patterns
depending on the array of stimulus components. Ramalingam et al.
[2] further examined dynamic aspects of V1 neurons in the tasks usedby Li et al., and revealed the difference in the dynamic correlations
between V1 responses evoked by the two tasks. Using a V1 model, we
also proposed the neural mechanism of the tuning modulations by
top-down signal [3].Top-down and bottom-up information are processed with different brain rhythms. Fast oscillations such as gamma
rhythms are involved in sensory coding and feature binding in local
circuits, while slower oscillations such as alpha and beta rhythms are
evoked in higher brain areas and may contribute to the coupling ofdistinct brain areas. In this study, we investigate how informationof top-down influence is conveyed by feedback pathway, and how
information relevant to task context is coordinated by different brain
oscillations. We present a model of visual system which consists of
networks of V1 and V2. We consider the two types of perceptual tasks
used by Li et al., bisection task and vernier one. We show that visual
information relevant to each task context is coordinated by a push-pull
effect of top-down signal. We also show that top-down signal reflecting a beta oscillation in V2 neurons, coupled with a gamma oscillationof V1 neurons, enable the efficient gating of task-relevant information in V1. This study provides a useful insight to understanding how﻿BMC Neurosci 2020, 21(Suppl 1):54

rhythmic oscillations in distinct brain areas are coupled to gate taskrelevant information encoded in early sensory areas.
References
1. Li W, Piech V, and Gilbert C D. Perceptual learning and top-down influencesin primary visual cortex. Nature Neuroscience 2004; 7(6): 651-657.
2. Ramalingam N, McManus JNJ, Li W, and Gilbert CD. Top-Down Modulationof Lateral Interactions in Visual Cortex. Journal of Neuroscience. 2013; 33(5):1773-1789.
3. Kamiyama A, Fujita K, and Kashimori Y. A neural mechanism of dynamic
gating of task-relevant information by top-down influence in primary visualcortex. BioSystems. 2016; 150: 138-148.

P95
A functional role of short‑term synapses in maintenanceof gustatory working memory in orbitofrontal cortex
Layla ­Antaket1, Yoshiki ­Kashimori2
1
University of Electro‑Communications, Tokyo, Japan; 2The University
of Electro‑Communications, Department of Engineering Science, Tokyo,
Japan
Correspondence: Layla Antaket (a1943002@edu.cc.uec.ac.jp)
BMC Neuroscience 2020, 21(Suppl 1):P95
Taste perception is an important function for life activities, such as
ingestion of nutrition and escape of toxic foods. Gustatory information is first processed by taste receptors in the taste buds present in
the tongue. After that, it is transmitted to the orbitofrontal cortex(OFC), the hypothalamus, and the amygdala. In the course of a series
of information processing processes, the gustatory cortex (GC) processes information on the quality and strength (concentration) of taste
itself. Currently, taste research is proceeding with electrophysiologicaland molecular biological research on receptors. However, the processing mechanism of taste information encoded in each part of the tastetransmission pathway is not well understood.
Furthermore, in addition to the higher-order processing of taste information, the OFC, located above the GC, integrates taste informationand other sensory information such as tactile sensation, smell, andcolor to determine the flavor (flavor) of food and guide behavior. We
proposed a binding mechanism of taste and odor information in the
OFC [1]. A recent study has shown an alternative function of OFC, or
working memory function of taste information [2]. The study showedthat OFC neurons of the rhesus monkeys encoded a gustatory working
memory in a delayed match-to-sample task. OFC neurons exhibited apersistent activity even when a gustatory stimulus presented in thesample period was turned off, whereas neurons of the primary gustatory cortex (GC) did not show a significant persistency of the activity. It
is unclear how the gustatory working memory in the OFC is shaped by
the interaction between the GC and the OFC.
To address this issue, we focus on a delayed match-to-sample task,
in which monkeys have to decide whether the first juice stimulus
is the same as the second stimulus separated by a delay period. We
develop a model of gustatory system that consists of network models of GC and OFC. Each model of GC and OFC has two-dimensional
array of neurons, which encode information of three kinds of foods,
orange, guava, and tomato. These network models were based on the
Izhikevich neuron model [3] and biophysical synapses mediated byneurotransmitters such as AMPA, NMDA, and GABA. The neural unit
consists of a main neuron and an inhibitory interneuron, mutually connected with AMPA and GABA synapses. Main neurons are reciprocallyconnected with AMPA and NMDA synapses. The NMDA-synaptic connections between these networks are formed by Hebbian learning ina task-relevant way. The gustatory information of three foods is represented by dynamical attractors in the GC and OFC networks. Simulating our model for match/nonmatch trails, we explored the neuralmechanism by which the working memory of gustatory information is
generated in the OFC. We show that the working memory of gustatory
information is shaped by the recurrent activation mediated by shortterm synapses of OFC neurons. In addition, we examined how working
memory formed by the OFC is used for match/nonmatch decisionmaking by adding a decision layer to the model.

Page 57 of 123

References
1. Shimemura T, Fujita K, Kashimori Y. A neural mechanism of taste perception
modulated by odor information. Chemical Senses. 2016; 41(7): 579-589.
2. Lara AH, Kennerley SW, Wallis JD. Encoding of Gustatory Working Memoryby Orbitofrontal Neurons. Journal of Neuroscience. 2009; 29(3): 765-774.
3. Izhikevich EM. Simple Model of Spiking Neurons. IEEE Trans Neural Net.
2003; 14(6): 1569-1572.

P96
Bayesian network change point detection for dynamic functional
connectivity
Lingbin Bian, Tiangang Cui, Adeel Razi, Jonathan Keith
Monash University, Melbourne, Australia
Correspondence: Lingbin Bian (lingbin.bian1@monash.edu)
BMC Neuroscience 2020, 21(Suppl 1):P96
We present a novel Bayesian method for identifying the change ofdynamic network structure in working memory task fMRI data via
model fitness assessment. Specifically, we detect dynamic community structure change-point(s) based on overlapped sliding window
applied to multivariate time series. We use the weighted stochastic
block model to quantify the likelihood of a network configuration,and develop a novel scoring criterion that we call posterior predictive discrepancy by evaluating the goodness of fit between modeland observations within the sliding window. The parameters for thismodel include latent label vector assigning network nodes to interacting communities, and the block model parameter determining theweighted connectivity within and between communities. The GLM
analyses were conducted in both subject level and group level and thecontrast between 2-back, 0-back and baseline were used to localise
the regions of interest in task fMRI data.
The working memory task fMRI data in the HCP were pre-processed
and GLM analyses were applied. With the extracted time series ofregions of interest, we propose to use the Gaussian latent block model[1], also known as the weighted stochastic block model (WSBM), toquantify the likelihood of a network and Gibbs sampling to sample aposterior distribution derived from this model. The Gibbs sampling
approach we adopt is based on the work of [1,2] for finite mixture
models. The proposed model fitness procedure draws parametersfrom the posterior distribution and uses them to generate a replicatedadjacency matrix; then calculates a disagreement matrix to quantify
the difference between the replicated adjacency matrix and realised
adjacency matrix. For the evaluation of the model fitness, we definea parameter-dependent statistic called the posterior predictive discrepancy (PPD) by averaging the disagreement matrix. Then we compute the cumulative discrepancy energy (CDE) from PPD by applying
another sliding window for smoothing and use CDE as a score criterion for change point detection. The CDE increases when change
points are contained within the window, and can thus be used toassess whether a statistically significant change point exists within aperiod of time.
We first applied the algorithm to the synthetic data simulated from
the Multivariate Gaussian distribution for validation. We visualise the
Gibbs iteration of sampled latent labels and the histogram of the blockparameters reflecting the characterisation of the connectivity within
and between communities. We then demonstrated the performanceof the change point detection with different window sizes. In realworking memory task fMRI data analyses, the fixed effects analyses are
conducted to estimate the average effect size across runs within subjects at the subject level. At group level, the mixed effects analyses are
conducted, where the subject effect size is considered to be random.
In this work, we mainly focus on the memory load contrast (2-back vs
0-back, 2-back vs baseline, or 0-back vs baseline).
References
1. Wyse J, Friel N. Block clustering with collapsed latent block models. Statis‑
tics and Computing. 2012; 22: 415-428.
2. Nobile A, Fearnside AT. Bayesian finite mixtures with an unknown numberof components: the allocation sampler. Statistics and Computing. 2007; 17:147-162.

﻿BMC Neurosci 2020, 21(Suppl 1):54

P97
Multiscale simulations of ischemia and spreading depolarization
with NEURON
Adam ­Newton1, Michael ­Hines2, William W ­Lytton3, Robert ­McDougal4,
Craig ­Kelley3
1
Yale University, Yale School of Public Health, Connecticut, United Statesof America; 2Yale University, School of Medicine, Connecticut, United Statesof America; 3SUNY Downstate Medical Center, Department of Physiology
and Pharmacology, New York, United States of America; 4Yale University,
Department of Neuroscience, Connecticut, United States of America
Correspondence: Adam Newton (adam.newton@yale.edu)
BMC Neuroscience 2020, 21(Suppl 1):P97
Recent improvements and performance enhancements in the NEURON (neuron.yale.edu) reaction-diffusion module (rxd) allow us tomodel multiple relevant concentrations in the intracellular and extracellular space. The extracellular space is a coarse-grained macroscopic model based on a volume averaging approach, allowing theuser to specify both the free volume fraction (the proportion of spacein which species are able to diffuse) and the tortuosity (the average
multiplicative increase in path length due to obstacles). These tissue
characteristics can be spatially dependent to account for regional or
pathological differences.
Using a multiscale modeling approach we have developed a pair ofmodels for spreading depolarization at spatial scales from microns tomm, and time scales from ms to minutes. The cellular/subcellular-scalemodel adapted existing mechanisms for a morphologically detailed
CA1 pyramidal neuron together with a simple astrocyte model. Thismodel included reaction-diffusion of K+, Na+, Cl− and glutamate,with detailed cytosolic and endoplasmic reticulum Ca2+ regulation. Homeostatic mechanisms were added to the model, including;
Na-K- ATPase pumps, Ca2+ pumps, SERCA, NKCC1, KCC2 and glutamate transporters. We use BluePyOpt to perform a parameter search,
constrained by the requirements of realistic electrophysiologicalresponses while maintaining ionic homeostasis. This detailed model
was used to explore the hypothesis that individual dendrites have distinct vulnerability to damage due to area-volume ratios leading to different intracellular Ca2+ levels.
At the tissue-scale we adapted a simpler point neurons model, anddensely packed them in a coarse-grained macroscopic 3D volume. The
models include a simple model for oxygen and dynamic changes involume fraction. This allows us to model the effect of changes in tissue
diffusion characteristics on the wave propagation during spreading
depolarization.
Acknowledgments: Research supported by NIH grant R01MH086638.
P98
Bayesian mechanics in the brain under the free‑energy principle
Chang S Kim
Chonnam National University, Department of Physics, Gwangju, South
Korea
Correspondence: Chang S Kim (cskim@jnu.ac.kr)
BMC Neuroscience 2020, 21(Suppl 1):P98
In the field of neurosciences, the free-energy principle (FEP) stipulates
that all viable organisms cognize and behave using probabilistic models embodied in their brain in a manner that ensures their adaptive fitness in the environment [1].
Here, we report on our recent theoretical study that supports the useof the FEP as a more physically plausible theory, based on the principle of least action [2]. We recapitulate the FEP carefully [3] and evaluate that some technical facets in its conventional formalism require
reformulation with finesse [4]. Accordingly, we articulate the FEP as
living organisms minimize the sensory uncertainty, which is the average surprisal over a temporal horizon, and reformulate the recognition dynamics of the brain’s ability for actively inferring the externalcauses of sensory inputs. We effectively cast the Bayesian inversion
problem in the organism’s brain to find the optimal neural trajectories
by minimizing the time integral of the informational free energy (IFE),

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which is the upper bound of the long-term average surprisal. Specifically, we abstain from i) the non-Newtonian extension of continuous
states, which yields the generalized motion, by recursively taking
higher-order derivatives of the sensory observation and state equations, and ii) the heuristic gradient-descent minimization of the IFE in amoving frame of reference in a generalized-state space by viewing thenonequilibrium dynamics of brain states as drift-diffusion flows that
locally conserve the probability density. The advantage of our formulation is that only bare variables (positions) and their first-order derivatives (velocities) are used in the Bayesian neural computation, thereby
dismissing the need for the extra-physical assumptions.
Bare variables are an organism’s representations of the causal environment, and their conjugate momenta resemble the precision-weighted
prediction errors in a predictive coding language [5].
Furthermore, we consider the sensory-data-generating dynamics tobe nonstationary on an equal footing with intra- and inter-hierarchical-level dynamics in a neuronally based biophysical model.
Consequently, our theory delivers a natural account of the descending
predictions and ascending prediction errors in the brain’s hierarchicalmessage-passing structure (Fig. 1).

Fig. 1 Schematic of the neural circuitry [4], where each cortical levelis specified by the perceptual states (S(i),V(i)) and their conjugate
momenta (Ps(i),Pv(i)). The prediction error Ps(0) of incoming sensory
data at the lowest level induces an inhibitory change in the per‑ceptual momenta (Ps(1),Pv(1)). Subsequently, the prediction error
propagates up the hierarchy
The ensuing neural circuitry may be related to the alpha-beta andgamma rhythms that characterize the feedback and feed-forward
influences, respectively, in the primate visual cortex [6].
References
1. Friston K. The free-energy principle: a unified brain theory? Nature reviews
neuroscience. 2010; 11(2): 127-38.
2. Landau LD, Lifshitz EM. Mechanics: Volume 1 (Course of Theoretical Physics
S). 3rd edition. Amsterdam: Elsevier Ltd.; 1976.
3. Buckley CL, Kim CS, McGregor S, Seth AK. The free energy principle foraction and perception: A mathematical review. Journal of Mathematical
Psychology. 2017; 81: 55-79.
4. Kim CS. Recognition dynamics in the brain under the free energy principle.
Neural computation. 2018; 30(10): 2616-59.
5. Rao RP, Ballard DH. Predictive coding in the visual cortex: a functional inter‑
pretation of some extra-classical receptive-field effects. Nature neurosci‑
ence. 1999; 2(1): 79-87.

﻿BMC Neurosci 2020, 21(Suppl 1):54

6. Michalareas G, et al. Alpha-beta and gamma rhythms subserve feedback
and feedforward influences among human visual cortical areas. Neuron.
2016; 89(2): 384-97.

P99
Integrated model of reservoir computing and autoencoder
for explainable artificial intelligence
Hoon‑Hee Kim
Korea Advanced Institute of Science and Technology, Daejeon, South
Korea
Correspondence: Hoon‑Hee Kim (hkma19gm@kaist.ac.kr)
BMC Neuroscience 2020, 21(Suppl 1):P99
Due to the development of machine learning such as a deep neuralnetwork, Artificial Intelligence (AI) has been used in many areas. Modern AI technology accurately solves problems such as classification,
regression, and prediction, but there is a lack of skill to explain the process of AI decision in terms of human understanding; it is called a blackbox AI. The black-box AI, in which humans cannot understand thedecision process, is difficult to use in high-risk areas such as importantsocial and legal decisions, medical diagnosis, and financial predictions[1]. Although there are highly explainable machine learning methods
such as a decision-tree, these machine learning methods tend to has
a low performance and are not suitable for solving a complex problem[2]. In this study, I suggest a novel explainable AI method which has ahigh performance based on an integrated model of Reservoir Computing and Autoencoder. Reservoir Computing, a recurrent neural network
consists of three layers: inputs, reservoir, and readouts can train nonlinear dynamics using linear learning methods [3]. Recently, a study was
published in which neural networks induced actual physical laws using
Variational Autoencoder which can extract interpretable features of thelearning data [4]. In the integrated model, the features of the training
data were learned by the autoencoder structure and linear learning
rule of reservoir computing. Therefore, these features could be represented as a linear formula form that a human can simply understand.
To validate the integrated model, I tested the model to predict trendsof the S & P500 index. The model showed more than 80% accuracy andreported that which features were most important to the prediction interms of weighted linear formula.
Acknowledgments: This study was supported by the National
Research Foundation of Korea [NRF-2019R1A6A3A01096892].
References
1. Rudin C. Stop explaining black box machine learning models for highstakes decisions and use interpretable models instead. Nature MachineIntelligence. 2019; 1(5): 206-215.
2. Defense Advanced Research Projects Agency. Broad Agency Announce‑
ment, Explainable Artificial Intelligence. 2016. At: https://www.darpa.mil/
attachments/DARPA-BAA-16-53.pdf
3. Jaeger H, Haas H. Harnessing nonlinearity: Predicting chaotic systems andsaving energy in wireless communication. Science. 2004; 304(5667): 78-80.
4. Iten R, Metger T, Wilming H, Del Rio L, Renner R. Discovering physical con‑cepts with neural networks. Physical Review Letters. 2020; 124(1): 010508.

P100
Role of breathing signals and intra‑vIRt connectivity
in the generation of vibrissa movements
David ­Golomb1, Jeffrey ­Moore2, Arash F­ assihi3, Fan ­Wang4, David­Kleinfeld3
1
Ben Gurion University, Physiology, Be’er‑Sheva, Israel; 2Harvard University,
Molecular and Cellular Biology, Cambridge, Massachusetts, UnitedStates of America; 3University of California San Diego, Physics, San Diego,
California, United States of America; 4Duke University, Neurobiology,
Durham, North Carolina, United States of America
Correspondence: David Golomb (golomb@bgu.ac.il)
BMC Neuroscience 2020, 21(Suppl 1):P100
The vIRt nucleus in the medulla, apparently composed of mainly
inhibitory neurons, is necessary for whisking rhythm generation. It

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innervates motoneurons in the facial nucleus (FN) that project tointrinsic vibrissa muscles. The nearby pre-Bötzinger complex (pBötC),which generates inhalation, sends inhibitory inputs to the vIRt
nucleus. We explore potential mechanisms of this vIRt synchronization, needed for whisking, using analysis of experimental data andcomputational modeling. Using time courses of breathing and whisking in rats, we compute the relative amplitude An of n-th whisking
cycles within a breathing cycle. For head-restrained rats, the averagevalues of An/A1 are 0.56, 0.46 and 0.43 of the amplitudes for n = 2,3, 4. For freely behaving rats, the value for A2/A1is 0.79. The observations that A1 is larger than subsequent amplitudes suggests that therecovery of vIRt neurons from inhibition by the pBötC contributes tothe synchronization of vIRt neurons. Lower-amplitude periodic whisking, however, can occur after decay of the pBötC signal. To explain
how vIRt network generates these “intervening” whisks, and why the
amplitude A1is larger than the following amplitudes, we construct
and analyze a conductance-based model of the vIRt circuit composedof hypothetical two groups, vIRtr and vIRtp, of bursting inhibitory neurons with spike-frequency adaptive currents and constant external
excitation. Only neurons in vIRtr are inhibited by the pBötC and inhibit
FN motoneurons. We denote the strengths of the inhibitory conductances between neurons across and within each group by gas and gs,
respectively. If gas is larger than gs, the two groups burst alternately,as observed experimentally. If gas is too large, however, one group is
active and the second is silent. The oscillation amplitude depends linearly on the constant external excitation, and the period increases as afunction of gas-gs. Thus, the external input to the circuit and the levelof inhibition within the circuit control the amplitude and frequencyof the intervening whisking, respectively. Our model thus provides ameans to control the wide range of whisking frequencies observed inexperiments.
Acknowledgements: supported by NIH grant 5U19NS107466-02.
P101
Representing predictability of sequence patterns in a random
network with short‑term plasticity
Vincent S C Chien, Richard Gast, Burkhard Maess, Thomas Knösche
Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig,
Germany
Correspondence: Thomas Knösche (knoesche@cbs.mpg.de)
BMC Neuroscience 2020, 21(Suppl 1):P101
The brain is capable of recognizing repetitive acoustic patterns withina few repetitions, which is essential for the timely identification ofsound objects and the prediction of upcoming sounds. Several studies found neural correlates regarding the predictability of sequence
patterns, but the underlying neural mechanism is not yet clear. To
investigate the mechanism supporting the fast emergence of thepredictive state, we use neural mass modeling to replicate the experimental observations during the sequential repetition [1]. First, weinvestigated the effect of short-term plasticity (STP) to the responseof a Wilson-Cowan node to a prolonged stimulus, where the nodeconsists of an excitatory (E) and an inhibitory (I) population. In total,
27 combinations of plasticity settings were examined, where the plasticity types include short-term depression (STD), short-term facilitation(STF), and no STP, and the connection types include E-to-E, E-to-I, I-toE, and I-to-I connections. The simulated signals that best explain theobserved MEG temporal profiles (i.e., an onset peak followed by a rising curve) rely on the setting where STD is applied on E-to-E connection and STF applied on E-to-I connection. Second, with the preferred
plasticity settings (i.e., STD on E-to-E and STP on E-to-I), we simulated
the dynamics of a random network in response to regular (REG) andrandom (RAND) sequences in PyRates [2]. The simulated signals canreproduce several experimental observations, including the abovementioned MEG temporal profiles, the predictability-dependent MEG
amplitude (i.e., dependency in terms of regularity and alphabet size ofthe input sequence), as well as the MEG responses in the switch conditions (i.e., from REG to RAND, and from RAND to REG). Third, we useda simplified two-level network to illustrate the main mechanisms supporting such representation of predictability during the sequential

﻿BMC Neurosci 2020, 21(Suppl 1):54

repetition. The simplified network consists of nodes that are selective
to sound tone (level 1) and nodes that are selective to tone direction(level 2). The simulation reveals higher firing rates of I populations
level-2 nodes during REG than RAND condition, which contributes
to stronger simulated MEG amplitude via I-to-E connections (Fig 1).
In conclusion, we provide a possible mechanism to account for theexperimental observations. First, the increased MEG amplitude ismainly due to increased inhibitory activities. Second, the effect ofalphabet size is due to two forms of STP (i.e., STD on E-to-E and STF
on E-to-I). Third, the effect of regularity relies on the inclusion of the
2nd-level nodes that sparsely encodes the repetitive patterns. In short,the more predictable sequence patterns cause a stronger accumulation of inhibitory activities in direction-selective areas via STP, which
in turn leads to a higher MEG amplitude. This mechanism emphasizes
the need for STP at each stage of the bottom-up process, whereas theinvolvement of top-down processes is not necessary.

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flexible synchronisation of high frequency oscillations (e.g. gamma
oscillations).

References
1. Barascud N, Pearce MT, Griffiths TD, Friston KJ, Chait M. Brain responses inhumans reveal ideal observer-like sensitivity to complex acoustic patterns.
Proceedings of the National Academy of Sciences. 2016; 113(5): E616-25.
2. Gast R, et al. PyRates—A Python framework for rate-based neural simula‑tions. PloS one. 2019; 14(12): e0225900.

P102
Ephaptic coupling in white matter fibre bundles modulates axonaltransmission delays
Helmut ­Schmidt1, Gerald ­Hahn2, Gustavo D­ eco3, Thomas Knösche1
1
Max Planck Institute for Human Cognitive and Brain Sciences, Brain
Networks, Leipzig, Germany; 2Universitat Pompeu Fabra, Computational
Neuroscience Group, Barcelona, Spain; 3Universitat Pompeu Fabra,
Barcelona, Spain
Correspondence: Thomas Knösche (knoesche@cbs.mpg.de)
BMC Neuroscience 2020, 21(Suppl 1):P102
Axonal connections are widely regarded as faithful transmitters ofneuronal signals with fixed delays. The reasoning behind this is that
local field potentials (LFPs) caused by spikes travelling along axons
are too small to have an effect on other axons. We demonstrate that,
although the local field potentials generated by single spikes are of
the order of microvolts, the collective local field potential generated
by spike volleys can reach several millivolts. As a consequence, theresulting depolarisation of the axonal membranes (i.e. ephaptic coupling) increases the velocity of spikes, and therefore reduces axonaltransmission delays between brain areas.
We first compute the local field potential using the line approximation[1,2] for a spike in a single axon. We find that it generates an LFP with
about 20 microvolts amplitude, which is too weak to have a significant
effect on neighbouring axons (Fig. 1A). Next, we extend this formalism to fibre bundles to compute the LFP generated by spike volleys,
with different levels of synchrony. Such spike volleys can generate
LFPs with amplitudes of several millivolts (Fig. 1B), and the amplitudeof the LFP depends strongly on the level of synchrony of the spikevolley. Finally, we devise a spike propagation model in which the
LFPs generated by spikes modulate their propagation velocity. Thismodel reveals that with increasing number of spikes in a spike volley,
the axonal transmission delays decrease (Fig. 1C). To the best of ourknowledge, this study is the first that investigates the effect of LFPs
on axonal signal transmission in macroscopic fibre bundles. The main
result is that axonal transmission delays decrease if spike volleys are
sufficiently large and synchronous. This is in contrast to studies investigating ephaptic coupling between spikes at the microscopic level (e.g.
[3]), which have used a different model setup that resulted in increasing axonal transmission delays. Our results are a possible explanation
for the decreasing stimulus latency with increasing stimulus intensity
observed in many psychological experiments (e.g. [4]). We speculatethat the modulation of axonal transmission delays contributes to theFig. 1 A Spike profile (top) and resulting LFP in a single axon(bottom). B Spike profile (top) and LFP generated by fully synchro‑nised spike volley (bottom). C Axonal delays as function of numberof spikes and bundle radius in the presence (solid) and absence(dashed) of ephaptic coupling
Acknowledgements: This work has been supported by the German
Research Foundation (DFG), SPP2041.
References
1. Holt GR, Koch C. Electrical interactions via the extracellular potential nearcell bodies. Journal of Computational Neuroscience. 1999; 6 :169-184.
2. McColgan T, et al. Dipolar extracellular potentials generated by axonal
projections. eLife. 2017, 6: e25106.
3. Binczak S, Eilbeck JC, Scott AC. Ephaptic coupling of myelinated nerve
fibres. Physica D. 2001; 148: 159-174
4. Ulrich R, Rinkenauer G, Miller J. Effects of stimulus duration and intensity on
simple reaction time and response force. Journal of Experimental Psychol‑ogy. 1998; 24: 915-928.

P103
Constructing model surrogates of populations of dopamine neuron
and medium spiny neuron models for understanding phenotypic
differences
Tim ­Rumbell1, Sushmita ­Allam2, Tuan Hoang‑Trong2, Jaimit P­ arikh2,
Viatcheslav ­Gurev2, James ­Kozloski2
1
IBM Research, Healthcare and Life Sciences, Raleigh, North Carolina,
United States of America; 2IBM Research, Yorktown Heights, New York,
United States of America
Correspondence: Tim Rumbell (timrumbell@gmail.com)
BMC Neuroscience 2020, 21(Suppl 1):P103
Neurons of a specific type have intrinsic variety in their electrophysiological properties. Intracellular parameters, such as ion channel
conductances and kinetics, also have high variability within a neuron
type, yet reliable functions emerge from a wide variety of parameter
combinations. Recordings of electrophysiological properties from
populations of neurons under different experimental conditions or
perturbations produce sub-groups that form “electrophysiologicalphenotypes”. For example, different properties may derive from wildtype vs. disease model animals or may change across multiple agegroups [1].

﻿BMC Neurosci 2020, 21(Suppl 1):54

Populations of neuron models can represent a neuron type by varying parameter sets, each able to produce the outputs of a recording, and all spanning the ranges of recorded features. We previously
generated model populations using evolutionary search with anerror function that combines soft-thresholding with a crowdedness
penalty in feature space, allowing coverage of the empirical range offeatures with models. The technique was used to generate a population of dopamine neuron (DA) models, which captured the majorityof empirical features, generalized to perturbations, and revealed setsof coefficients predicted to reliably modulate activity [2]. We also used
this technique to construct striatal medium spiny neuron (MSN) model
populations, which recapitulated the effects of extracellular potassiumchanges [3] and captured differences in electrophysiological phenotype between MSNs from wild-type mice and from the Q175 modelof Huntington’s disease. Our approach becomes prohibitively computationally expensive, however, when we seek to produce multiple
populations that represent many phenotypes from across a spectrum.
For example, to recreate the non-linear developmental trajectoryobserved across postnatal development of DAs [1] we would need toperform multiple optimizations.
Here we demonstrate the construction of model surrogates that map
model parameters to features spanning the range of multiple electrophysiological phenotypes. We sampled from parameter space andsimulated models to create a surrogate training set. Using our evolutionary search as prior knowledge of our parameter space enabled a
dense sampling in regions of the high-dimensional model parameter
space that were likely to produce valid features. We trained a deepneural network with our datasets, producing a surrogate for our modelthat maps parameter set distributions to output feature distributions.
This can be used in place of the neuron model for model sampling,allowing rapid construction of populations of models that match different distributions of features from across multiple phenotypes. We
demonstrate this approach using DA developmental age groups and
MSN disease progression states as targets, facilitating a mechanistic
understanding of parameter modulations that generate differences inphenotypes.
References
1. Dufour MA, Woodhouse A, Amendola J, Goaillard JM. Non-linear devel‑
opmental trajectory of electrical phenotype in rat substantia nigra pars
compacta dopaminergic neurons. Elife. 2014; 3: e04059.
2. Rumbell T, Kozloski J. Dimensions of control for subthreshold oscillations
and spontaneous firing in dopamine neurons. PLoS Computational Biology.
2019; 15(9): e1007375.
3. Octeau JC, et al. Transient, consequential increases in extracellular potas‑
sium ions accompany channelrhodopsin2 excitation. Cell reports. 2019;27(8): 2249-61.

P104
Integrated cortical, thalamic and basal ganglia model of brain
function: validation against functional requirements
Sébastien ­Naze1,2, James K­ ozloski2
1
IBM Research, Melbourne, Australia; 2IBM Research, Yorktown Heights,
New York, United States of America
Correspondence: Sébastien Naze (sebastien.naze@gmail.com)
BMC Neuroscience 2020, 21(Suppl 1):P104
Large scale brain models encompassing cortico-cortical, thalamo-cortical and basal ganglia processing are fundamental to understand thebrain as an integrated system in healthy and disease conditions but
are complex to analyze and interpret. Neuronal processes are typicallysegmented by region and modality in order to explain an experimental observation at a given scale and then integrated to a global framework [1]. Here, we present a set of functional requirements applied tovalidate the recently developed IBEx model [2] against a learning task
involving coordinated activity across cortical and sub-cortical regions
in a brain-computer interface (BCI) context involving volitional control
of a sensory stimulus [3]. The original IBEx model comprises interacting modules for supra-granular, infra-granular cortical layers, thalamic
integration, basal ganglia parallel processing and dopamine-mediatedreinforcement learning. We decompose and analyze each subsystem

Page 61 of 123

in the context of the BCI learning task whereby parameters are tuned
to comply to its functional requirements. Intermediate conclusions arepresented for each subsystem according to the constraints imposed tosatisfy the requirements, before re-incorporating the subsystem in the
global framework. Consequences of model modifications and parameter tuning are assessed at the scales of the subsystem and the wholebrain system. The relation between infra-granular spiking activity indifferent cortical regions, thalamo-cortical delta rhythms and higherlevel description of cognitive or motor trajectories (according to thebrain region) is displayed. The relation to phenotypes associated to
Huntington’s disease is exposed and the framework is discussed inperspective to other state-of-art integrative efforts to understandcomplex high-order brain functions [4,5].
References
1. Eliasmith C, Trujillo O. The use and abuse of large-scale brain models. Cur‑rent Opinion in Neurobiology. 2014; 25: 1-6.
2. Kozloski J. Closed-loop brain model of neocortical information-based
exchange. Frontiers in Neuroanatomy. 2016; 10: 3.
3. Koralek AC, Jin X, Long II JD, Costa RM, Carmena JM. Corticostriatal plasticity
is necessary for learning intentional neuroprosthetic skills. Nature. 2012;
483(7389): 331-5.
4. Oizumi M, Albantakis L, Tononi G. From the phenomenology to the mecha‑
nisms of consciousness: integrated information theory 3.0. PLoS Computa‑
tional Biology. 2014; 10(5): e1003588.
5. Mashour GA, Roelfsema P, Changeux JP, Dehaene S. Conscious process‑ing and the global neuronal workspace hypothesis. Neuron. 2020; 105(5):776-98.

P105
Generating executable mushroom body and lateral horn circuits
from the hemibrain dataset with FlyBrainLab
Aurel A Lazar, Mehmet K Turkcan, Yiyin Zhou
Columbia University, Electrical Engineering, New York, United Statesof America
Correspondence: Yiyin Zhou (yiyin@ee.columbia.edu)
BMC Neuroscience 2020, 21(Suppl 1):P105
We introduce executable circuits for two higher order olfactory processing centers in the brain of Drosophila melanogaster implicated inlearning and memory, the lateral horn (LH) and mushroom body (MB).
Despite the large amounts of data available on these two neuropils,
there is a dearth of executable neural circuit models governing learning and memory in the MB and the LH. We use the recently-released
Hemibrain dataset [1] as a biological basis of our implementations toanalyze cell types, numbers and connectivity for realizing our computational models for both neuropils. Our implementations utilize the
FlyBrainLab environment [2] to deliver the capability to visualize neural circuits morphologically or with diagrams, can run on GPUs, andare designed to facilitate customization of neuron and synapse models at a per-cell and per-cell type level.
We first study the neural types and connectivity of mushroom bodyneurons, a neuropil that implements the capability for associative
learning. We model individual lobes that comprise the mushroom
body and each of their so-called compartments, along with connectivities between the so-called Kenyon cells (KCs), mushroom body output
neurons (MBONs) and dopaminergic neurons (DANs), thereby providing an interactive circuit diagram that allows for customizable ablationor activation experiments (Fig. 1). Implementing an interface between
the input to the MB from antennal lobe projection neurons (PNs), we
utilize the observed PN-to-KC connectivity and provide comparisons
against randomly generated instantiations of PN-to-KC connectivity, along standing hypothesis about the nature of PN-to-KC connectivity.

﻿BMC Neurosci 2020, 21(Suppl 1):54

Page 62 of 123

P106
Efficient communication in distributed simulations of spiking
neuronal networks with gap junctions­ elias2, Markus D­ iesmann2, Susanne ­Kunkel3
Jakob ­Jordan1, Moritz H
1
University of Bern, Department of Physiology, Bern, Switzerland; 2Jülich
Research Centre, Institute of Neuroscience and Medicine (INM‑6, INM‑10)
and Institute for Advanced Simulation (IAS‑6), Jülich, Germany; 3Norwegian
University of Life Sciences, Faculty of Science and Technology, Ås, Norway
Correspondence: Markus Diesmann (diesmann@fz‑juelich.de)
BMC Neuroscience 2020, 21(Suppl 1):P106

Fig. 1 Visualization and exploration of the entire MB circuit in the
Hemibrain dataset using FlyBrainLab (top). Customizable circuit
diagram of the mushroom body (bottom)
Second we investigate the connectivity within the LH, a neuropil associated with innate memories, and the connectivity between the MB
and the LH. We analyze in detail the connectivity between PNs, lateralhorn local neurons and output neurons. We construct an executable
circuit of the LH based on our analysis.
The implementations we provide are a step towards building integrated models of sensory systems derived from biological data. Our
approach showcases the impact an integrative ecosystem can have onbuilding executable circuit models for understanding the functional
logic of neurocomputation in the brain of model organisms.
Acknowledgments: The research reported here was supported by
DARPA under contract #HR0011-19-9-0035.
References
1. Xu CS, et al. A connectome of the adult drosophila central brain. BioRxiv.
2020.
2. Turkcan MK, et al. FlyBrainLab: an interactive computing environment for
the fruit fly brain. Society for Neuroscience Meeting. 2019.

Investigating the dynamics and function of large-scale spiking neuronal networks with realistic numbers ofsynapses is made possible
today by state-of-the-art simulation code that scales to the largest
contemporary supercomputers. These implementations exploit thedelayed and point-event like nature of the spike interaction between
neurons. In a network with only chemical synapses the dynamics of all
neurons is decoupled for the duration of the minimal synaptic transmission delay such that the dynamics of each neuron can be propagated independently for the duration of the minimal delay without
requiring information from other neurons. Hence, in distributed simulations of such networks, compute nodes need to communicate spikedata only after this period [1].
Electrical interactions, also called gap junctions at first seem to beincompatible with such a communication scheme as they couple
membrane potentials of pairs of neurons instantaneously. Hahne
et al. [2] however demonstrate that communication of spikes andgap-junction data can be unified using waveform-relaxation methods [3]. Despite these advances, simulations involving gap junctions
scale only poorly due to a communication scheme that collects global
data on each compute node. In comparison to chemical synapses, gap
junctions are far less abundant. To improve scalability we exploit thissparsity by integrating the existing framework for continuous interactions with a recently proposed directed communication scheme for
spikes [4]. Using a reference implementation in the NEST simulator(www.nest-simulator.org, [5]) we demonstrate excellent scalability of
the integrated framework, accelerating large-scale simulations with
gap junctions by more than an order of magnitude. This allows, for the
first time, the efficient exploration of the interactions of chemical andelectrical coupling in large-scale neuronal networks models with natural synapse density distributed across thousands of compute nodes.
Acknowledgements: Partly supported by Helmholtz young investigator group VH-NG-1028, European Union’s Horizon 2020 funding
framework under grant agreement no. 785907 (Human Brain Project
HBP SGA2) and no. 754304 (DEEP-EST), and Helmholtz IVF no. SO-092(Advanced Computing Architectures, ACA). We would also like toacknowledge use of the JURECA supercomputer through VSR grant
JINB33.
References
1. Morrison A, Mehring C, Geisel T, Aertsen AD, Diesmann M. Advancing theboundaries of high-connectivity network simulation with distributed com‑puting. Neural Computation. 2005; 17(8): 1776-801.
2. Hahne J, et al. A unified framework for spiking and gap-junction interac‑tions in distributed neuronal network simulations. Frontiers in Neuroinfor‑
matics. 2015; 9: 22.
3. Lelarasmee E, Ruehli AE, Sangiovanni-Vincentelli AL. The waveform relaxa‑
tion method for time-domain analysis of large scale integrated circuits. IEEE
transactions on computer-aided design of integrated circuits and systems.
1982; 1(3): 131-45.
4. Jordan J, et al. Extremely scalable spiking neuronal network simulation
code: from laptops to exascale computers. Frontiers in Neuroinformatics.
2018; 12: 2.
5. Gewaltig MO, Diesmann M. Nest (neural simulation tool). Scholarpedia.
2007; 2(4): 1430.

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P107
Inferring parameters of DBS‑induced short‑term synaptic plasticity
from in‑vivo recordings of human brain
Alireza ­Ghadimi1, Luka ­Milosevic2, Suneil ­Kalia3, Mojgan ­Hodaie3, Andres­Lozano3, William ­Hutchison4, Milos ­Popovic1, Milad ­Lankarany1
1
University of Toronto, Institute of Biomaterials and Biomedical Engineering,
Toronto, Canada; 2University of Tübingen, Department of Neurosurgery,
Tübingen, Germany; 3University of Toronto, Department of Surgery,
Toronto, Canada; 4University of Toronto, Department of Physiology,
Toronto, Canada
Correspondence: Alireza Ghadimi (alireza.ghadimi@mail.utoronto.ca)
BMC Neuroscience 2020, 21(Suppl 1):P107
Background: Short-term synaptic plasticity (STP) is the dynamic ofchange in the synaptic weight with respect to the presynaptic spiking
activity. Recent studies showed that STP is involved in several brain
processes. Several computational works have been done on inferring
STP parameters [1-4]; however, these studies utilized in-vitro signals of
intracellular recordings (except Ghanbari et al.) from rodents to create
their models, which is not necessarily representative of in-vivo humanbrain dynamics. To this end, we developed a parameter inference
method which estimates parameters of STP induced by DBS, verified
by experimental data obtained from intracranial recordings of singleneuron activity in surgical patients.
Method: To acquire spiking activity, two closely spaced microelectrodes
were placed in the thalamic ventral intermediate nucleus (Vim) during awake DBS surgeries. One electrode was used to record single-unit
spiking activity, while the other was used to deliver stimulation pulses atvarious frequencies (5Hz, 20Hz, 30Hz, 50Hz, 100Hz, 200Hz). The included
data were collected from 15 patients [5]. The narrow stimulus pulses
were removed, after which data were high-pass filtered to better isolate
single unit activity. Using stimulus artifacts as triggers, we extracted theinstantaneous firing rate of neurons in response to each DBS pulse of the
5Hz stimulation trains, and averaged over all inter-pulse intervals. Dueto the low stimulation rate (i.e. 5Hz), no STP is induced in this data. The
resultant waveform is equivalent to the impulse response.
In order to mimic STP behavior, we used the Tsodyks-Markram phenomenological model, which generates the postsynaptic current
according to the spiking history of the presynaptic neuron [6]. To
reconstruct the firing rate induced by DBS, we give the pulse train of
DBS as the input of the Tsodyks-Markram model. The model generates
a postsynaptic current in response to DBS pulses. We use this response
to make modulated pulse trains that represent the effect of STP bychanging the amplitude of each pulse. The modulated pulse train isconvolved with the impulse response of the neuron in order to makean estimation of the DBS-induced firing rate. The estimated firing rateis compared with the experimental instantaneous firing rates throughout the stimulation trains at each of the other stimulation frequencies.
To achieve the true parameters of the Tsodyks-Markram model weshould minimize the error between experimental and estimated firingrate. True parameters should be valid for all frequencies, therefore we
define the error function as the average error of 30Hz, 50Hz, 100Hz,and 200Hz frequencies. To minimize the error, we employ Bayesian
Adaptive Direct Search [7], which is a fast non-derivative optimization
algorithm. The optimization algorithm should select parameters suchthat the output of the model most accurately represents the dynamicchanges which occur to the synaptic weights induced by each individual successive stimulus pulse throughout individual stimulation trains.
Results: The figure below (Fig. 1) shows the experimental data versus
the model output generated by the parameter estimation algorithm.
The estimated parameters show a good match for all frequencies, verifying the validity of this approach. Overall, the results suggest that thismethod can be used for the assessment of STP dynamics of in-vivo
human neuronal recordings.

Fig. 1 The output of the model with estimated parameters thatshows matching between experimental firing rate extraxted during
DBS surgery (black), and the model output (blue)
References
1. Ghanbari A, Malyshev A, Volgushev M, Stevenson IH. Estimating short-term
synaptic plasticity from pre-and postsynaptic spiking. PLoS Computational
Biology. 2017; 13(9): e1005738.
2. Costa RP, Sjostrom PJ, Van Rossum MC. Probabilistic inference of short-term
synaptic plasticity in neocortical microcircuits. Frontiers in Computational
Neuroscience. 2013; 7: 75.
3. Bird AD, Wall MJ, Richardson MJ. Bayesian inference of synaptic quantal
parameters from correlated vesicle release. Frontiers in Computational
Neuroscience. 2016; 10: 116.
4. Bykowska OS, Gontier C, Sax AL, Jia DW, Llera-Montero M, Bird AD,
Houghton CJ, Pfister JP, Costa RP. Model-based inference of synaptic trans‑mission. Frontiers in Synaptic Neuroscience. 2019; 11: 21.
5. Milosevic L, et al. Deciphering Mechanism of DBS-induced Synaptic Plastic‑
ity in Different Sub-structures of Basal Ganglia Network BGN. [in prep]
6. Tsodyks MV, Markram H. The neural code between neocortical pyramidal
neurons depends on neurotransmitter release probability. Proceedings of
the National Academy of Sciences. 1997; 94(2): 719-23.
7. Acerbi L, Ji W. Practical Bayesian optimization for model fitting with Bayes‑
ian adaptive direct search. In: Advances in neural information processing
systems. 2017; 1836-1846.

P108
Ensemble empirical mode decomposition of noisy local filed
potentials from optogenetic data
Sorinel ­Oprisan1, Xandre ­Clementsmith2, Tamas T­ ompa3, Antonieta L­ avin4
1
College of Charleston, Department of Physics and Astronomy, Charleston,South Carolina, United States of America; 2College of Charleston,
Psychology, Charleston, South Carolina, United States of America;
3
University of Miskolc, Department of Preventive Medicine, Miskolc,
Hungary; 4Medical University of South Carolina, Neuroscience, Charleston,South Carolina, United States of America
Correspondence: Sorinel Oprisan (oprisans@cofc.edu)
BMC Neuroscience 2020, 21(Suppl 1):P108
Gamma rhythms with frequencies over 30 Hz are thought to reflectcortical information processing. However, signals associated withgamma rhythms are notoriously difficult to record due to their low
energy and relatively short duration of the order of a few seconds. In
our experiments, of particular interest was the 40 Hz synchronizationof neurons, which is believed to be indicative of temporal binding.
Temporal binding glues together spatially distributed representations of different features of sensory input, e.g., during the analysis ofa visual stimulus, to produce a coherent description of constituent elements.

﻿BMC Neurosci 2020, 21(Suppl 1):54

Our goal was to investigate the effect of systemic cocaine injection
on the local field potentials (LFPs) recorded from the medial prefrontal cortex (mPFC) of mice. We used male PV-Cre mice infected witha viral vector [1] that makes some proteins sensitive to light, such as
the members of the opsin family, including retinal pigments in visualsystems. By genetic engineering, channelrhodopsins was coupled tosodium channels express in neurons and increase their excitability
when exposed to blue light [1].
We previously used nonlinear dynamics tools, such as delay embedding and false nearest neighbors [2,3], to estimate the embedding
dimension and the delay time for attractor reconstruction from
LFPs [4]. While nonlinear dynamics is a powerful tool for data analysis, recent developments suggested that ensemble empirical mode
decomposition (EEMD) could be better suited for short and noisy timeseries. The traditional EMD method is a data-driven decomposition of
the original data into orthogonal Intrinsic Mode Functions (IMFs) [5]. In
the presence of noise, the time scale separation is not perfect, and IMF
mixing produces significant energy leaks between modes. The advantage of EEMD is that by adding a controlled amount of noise to thedata leads to a between demixing of the IMFs. We also performed a
Hilbert-Huang [5] transform to the demixed IMFs and computed theinstantaneous frequency spectrum. Our results indicate that cocaine
significantly shifts the energy distribution towards earlier durations
during the trial compared to control. Our findings allow us to estimate
the contribution of different spectral components quantitatively anddevelop a dynamical model of the data.
References
1. Dilgen JE, Tompa T, Saggu S, Naselaris T, Lavin A. Optogenetically evokedgamma oscillations are disturbed by cocaine administration. Frontiers in
Cellular Neuroscience. 2013; 7: 213.
2. Oprisan SA, Lynn PE, Tompa T, Lavin A. Low-dimensional attractor for neuralactivity from local field potentials in optogenetic mice. Frontiers in Compu‑
tational Neuroscience. 2015; 9: 125.
3. Oprisan SA, Imperatore J, Helms J, Tompa T, Lavin A. Cocaine-inducedchanges in low-dimensional attractors of local field potentials in optoge‑
netic mice. Frontiers in Computational Neuroscience. 2018; 12: 1.
4. Oprisan SA, Clementsmith X, Tompa T, Lavin A. Dopamine receptor
antagonists effects on low-dimensional attractors of local field potentials in
optogenetic mice. PLoS ONE. 2019; 14(10): e0223469.
5. Huang NE, et al. The Empirical Mode Decomposition and the Hilbert Spec‑
trum for Nonlinear and Nonstationary Time Series Analysis. Proceedings of
the Royal Society of London A. 1998; 454: 903-995.

P109
Investigating water transport mechanisms in astrocytes with highdimensional parameter estimation
Pierre‑Louis ­Gagnon1, Kaspar ­Rothenfusser2, Nicolas D­ oyon3, Pierre­Marquet4, François ­Laviolette5
1
Université Laval, Département d’Informatique et Génie Logiciel,
Quebec, Canada; 2Centre hospitalier universitaire vaudois, Département
de psychiatrie, Lausanne, Switzerland; 3Centre hospitalier universitairevaudois, Department of Mathematics and Statistics, Quebec, Canada;
4
Université Laval, Department of Psychiatry and Neuroscience, Quebec,
Canada; 5Université Laval, Department of Software Engineeringand Computer Science, Quebec, Canada
Correspondence: Pierre‑Louis Gagnon (pierre‑louis.gagnon.1@ulaval.ca)
BMC Neuroscience 2020, 21(Suppl 1):P109
Holographic microscopy allows one to measure subtle volume
changes of cells submitted to challenges such as an osmotic shockor sudden increase in extracellular potassium. Interpreting volumetric data however remains a challenge. Specifically, relating theamplitude of volume changes to the biophysical properties of cells
such as passive permeability to water or the rate of water transport
by cation chloride cotransporter is a difficult but important task.
Indeed, mechanisms of volume regulation are key for cell resilienceand survival. Experimentally, the second author measured the volume
response as well as the change in sodium concentration of astrocytes
submitted to bath applied: hypo-osmotic solutions, solutions withhigh potassium concentration or solutions containing glutamate.

Page 64 of 123

Overall, he measured the time course of the response of over 2000
astrocytes. In order to interpret this rich data, we developed a mathematical model based on our biophysical knowledge of astrocytes.
This model relates on the one hand the experimental perturbations of
the extracellular medium and on the other the properties of the cellsuch as its various conductances or strengths of transporters to its
responses in terms of volume change, changes in ionic concentrations
and in membrane potential. Determining the biophysical properties ofcells thus boils down to a problem of model calibration. This presentation is mainly focused on the work of the first author who designed
and implemented a gradient-based optimization algorithm, to estimate model parameters and find the values of the parameters which
best explain the data coming from distinct modalities and astrocytes.
A first computational challenge is to combine data from different
modalities. In some experiments, the sodium response is measured
while in others, the volume response is inferred from phase measurements. We also take advantage of the fact that expert knowledge
provides information on variables which are not measured. For example, even if membrane potential is not measured, we impose that it isbetween -100 mV and -50 mV at equilibrium. Combining these different information sources translate into a complex loss function. Furthermore, using a priori knowledge on the value of parameters, wedeveloped a Bayesian approach. Another challenge comes from thefact that different measurements come from different cells. Our goal isthus not to infer a single set of parameters but rather to infer how biophysical parameters are distributed within the population of cells. This
was achieved by using a Tikhonov approach which penalizes parameter values laying far from the average of the distribution.
With our algorithm, we were able to infer the strength of the sodium
potassium ATPase pump in each cell with a good precision. This could
be useful in identifying cells which are more vulnerable. Parameters
related to water transport such the passive membrane permeability towater or the rate of water transport through cation chloride cotransporters are elusive and cannot be determined by conventional methods. Our inference algorithms provided information on these values.
Finally, our algorithm is flexible enough to adapt rapidly to takeadvantage of new experiment type or new data modality.
P110
Synchronization and resilience in the Kuramoto white matter
network model with adaptive state‑dependent delays
Daniel ­Park1, Jeremie ­Lefebvre2
1
University of Toronto, Department of Mathematics, Toronto, Canada;
2
University of Ottawa, Department of Biology, Ottawa, Canada
Correspondence: Daniel Park (seonghyun.park@mail.utoronto.ca)
BMC Neuroscience 2020, 21(Suppl 1):P110
Myelin sheaths around axonal lengths are formed by mature oligodendrocytes, and play a critical part in regulating signal transmission
in the nervous system. Contrary to traditional assumptions, recent
experiments have revealed that myelin remodels itself in an activitydependent way, during both developmental stages and well into
adulthood in mammalian subjects. Indeed, it has shown that myelin
structure is affected by extrinsic factors such as one’s social environment and intensified learning activity. As a result, axonal conduction
delays continuously adjust in order to regulate the timing of neuralsignals propagating between different brain regions. While there isstrong empirical support for such phenomena, the plasticity mechanism has yet to be extensively modeled in neurocomputational fields.
As a preliminary step, we incorporate adaptive myelination in the formof state-dependent delays into neural network models, and analyze
how it consequently alters its dynamics. In particular, we ask whatrole myelin plasticity plays in brain synchrony, which is a fundamental element of neurological function. Brain synchrony is simplistically
represented in coupled phase-oscillator models such as the Kuramoto
network model. As a prototype, we equip the Kuramoto model witha distribution of variable delays governed by a plasticity rule withphase difference gain that allows the delays and oscillatory phases to
evolve over time with mutually dependent dynamics. We analyzed theequilibria and stability of this system, and applied our results to large

﻿BMC Neurosci 2020, 21(Suppl 1):54

dimensional networks. Our joint mathematical and numerical analysis demonstrates that plastic delays act as a stabilizing mechanism
promoting the network’s ability to maintain synchronous activity. Ata high-dimensional network level, our work also shows that global
synchronization is more resilient to perturbations and injury towards
network architecture. Specifically, our conducted numerical experiments imply that plastic delays play a positive role in improving alarge-dimensional system’s resilience in achieving synchrony from asustained injury. Our results provide key insights about the analysis
and potential significance of activity-dependent myelination in largescale brain synchrony.
P111
The impact of noise on the temporal patterning of neuralsynchronization
Leonid ­Rubchinsky1, Joel ­Zirkle2
1
Indiana University Purdue University Indianapolis and Indiana University
School of Medicine, Department of Mathematical Sciences and Stark
Neurosciences Research Institute, Indianapolis, United States of America;
2
Indiana University Purdue University Indianapolis, Departmentof Mathematical Sciences, Indianapolis, United States of America
Correspondence: Leonid Rubchinsky (lrubchin@iupui.edu)
BMC Neuroscience 2020, 21(Suppl 1):P111
Neural synchrony in the brain is often present in an intermittent fashion, i.e. there are intervals of synchronized activity interspersed withintervals of desynchronized activity. A series of experimental studies
showed that the temporal patterning of neural synchronization may
be very specific, exhibiting predominantly short (although potentiallynumerous) desynchronized episodes [1], and may be correlated withbehavior (even if the average synchrony strength is not changed) [2-4].
Prior computational neuroscience research showed that a networkwith many short desynchronized intervals may be functionally different than a network with few long desynchronized intervals [5]. In thisstudy, we investigated the effect of noise on the temporal patterns ofsynchronization. We employed a simple network of two conductancebased neurons that were mutually connected via excitatory synapses.
The resulting dynamics of the network was studied using the sametime-series analysis methods used in prior experimental and computational studies. It has been well known that synchrony strength
degrades with noise. We found that noise also affects the temporal
patterning of synchrony. Increase in the noise level promotes dynamics with predominantly short desynchronizations. Thus, noise may be
one of the mechanisms contributing to the short desynchronization
dynamics observed in multiple experimental studies.
Acknowledgements: This work was supported by NSF grant DMS
1813819.
References
1. Ahn S, Rubchinsky LL. Short desynchronization episodes prevail in synchro‑nous dynamics of human brain rhythms. Chaos. 2013; 23; 013138.
2. Ahn S, Rubchinsky LL, Lapish CC. Dynamical reorganization of synchronous
activity patterns in prefrontal cortex - hippocampus networks during
behavioral sensitization. Cerebral Cortex. 2014; 24: 2553-2561.
3. Ahn S, Zauber SE, Worth RM, Witt T, Rubchinsky LL. Neural synchronization:
average strength vs. temporal patterning. Clinical Neurophysiology. 2018;
129: 842-844.
4. Malaia E, Ahn S, Rubchinsky LL. Dysregulation of temporal dynamics ofsynchronous neural activity in adolescents on autism spectrum. Autism
Research. 2020; 13: 24-31.
5. Ahn S, Rubchinsky LL. Potential mechanisms and functions of intermittent
neural synchronization. Frontiers in Computational Neuroscience. 2017; 11:44.

Page 65 of 123

P112
Comparing Drosophila neural circuit models with FlyBrainLab
Aurel A Lazar, Tingkai Liu, Mehmet K Turkcan, Yiyin Zhou
Columbia University, Electrical Engineering, New York, United Statesof America
Correspondence: Mehmet K Turkcan (mkt2126@columbia.edu du)
BMC Neuroscience 2020, 21(Suppl 1):P112
In recent years, a wealth of Drosophila neuroscience data havebecome available. These include cell type, connectome and synaptome datasets for both the larva and adult fly [1-4]. To facilitate integration across data modalities and to accelerate understanding thefunctional logic of the fly brain, we developed an interactive computing environment called FlyBrainLab [5].
FlyBrainLab brings together tools enabling the morphological visualization and exploration of large connectomics datasets, interactive
circuit construction and visualization, multi-GPU execution of neural circuit models for in silico experimentation, and libraries to aid
data analysis. FlyBrainLab provides the flexibility to readily navigate
between the in vivo circuits and executable in silico circuits. Moreover, FlyBrainLab methodologically supports the efficient comparisonof fly brain circuit models, either across model instances developed bydifferent researchers, or across different developmental stages of the
fruit fly. We provide two example comparisons below.
First, we constructed a wild-type biological central complex (CX)
circuit followed by a corresponding interactive baseline CX circuit
diagram (Fig. 1, left). This enabled us to automatically map three CX
circuit models described in the literature into executable circuits(Fig. 1, middle-right). With these circuits on the same platform, we
devised the same set of inputs to the CX models and an evaluation
criterion. The evaluation of the function of the executable circuits
revealed the differences in modeling assumptions and the less visible
details underlying the mapping between neuroanatomy, neurocircuitry and computation in the executable circuits. Based on these andother comparisons, new executable CX circuit models can be developed, evaluated and scrutinized by the research community.

Fig. 1 From the wild-type biological CX circuit and its circuit diagram(left column), two additional models of the CX are instantiated andcompared (middle and right columns)
Second, by integrating biological data across adult and larval flies
we developed executable models for early olfactory systems inboth developmental stages. Circuit models are implemented for the
Antenna, the Antennal Lobe and the Mushroom Body, and are interactively configurable. We evaluated the I/O characteristics of adultand larva, and discovered significant differences in odorant encoding
capabilities. Noticeably, the adult excels in disparately representing
differing odorant identities while requiring 10x higher neural hardware and as such much higher energy consumption. Such tradeoff
between computation and energy requirement suggests a generalprinciple of adaptation to environmental niches which we are currently exploring.
Acknowledgments: The research reported here was supported
by AFOSR under grant #FA9550-16-1-0410 and DARPA undercontract #HR0011-19-9-0035.

﻿BMC Neurosci 2020, 21(Suppl 1):54

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References
1. Chiang AS, et al. Three-dimensional reconstruction of brain-wide wiring
networks in Drosophila at single-cell resolution. Current Biology. 2011;21(1): 1-1.
2. Takemura SY, et al. Synaptic circuits and their variations within different
columns in the visual system of Drosophila. Proceedings of the National
Academy of Sciences. 2015; 112(44): 13711-6.
3. Berck ME, et al. The wiring diagram of a glomerular olfactory system. Elife.
2016; 5: e14859.
4. Xu CS, et al. A connectome of the adult drosophila central brain. BioRxiv.
2020.
5. Turkcan MK, et al. FlyBrainLab: an interactive computing environment for
the fruit fly brain. Society of Neuroscience. 2019.

P113
Dynamically damped stochastic alpha‑band relaxation activity
in 1/f noise and alpha blocking in resting M/EEG
Rick ­Evertz1, Damien ­Hicks2, David L­ iley3
1
Swinburne University of Technology, Melbourne, Australia; 2Centre
for Human Psychopharmacology, Optical Sciences Centre, Melbourne,
Australia; 3The University of Melbourne, Department of Medicine,
Melbourne, Australia
Correspondence: Rick Evertz (revertz@swin.edu.au)
BMC Neuroscience 2020, 21(Suppl 1):P113
Dynamical and physiological basis of alpha band activity and 1/f noise
is a subject of continued speculation. Here we conjecture, on the basisof empirical data analysis, that both of these features can be dynamically unified if resting EEG is conceived of being the sum of multiple
stochastically perturbed alpha band oscillatory relaxation processes.
The modulation of alpha-band and 1/f noise activity by dynamic
damping is explored in eyes closed (EC) and eyes open (EO) restingstate Magneto/Electroencephalography (M/EEG). We assume thatthe resting M/EEG being recorded is composed of a superposition ofstochastically perturbed alpha-band relaxation processes with a distribution of dampings, the functional form of which is unknown. We
perform the inverse problem and take measured M/EEG power spectra and compute the distribution of dampings using Tikhonov regularization methods. The characteristics of the damping distribution are
examined across subjects, sensors and recording condition (EC/EO).
We find that there are robust changes in the estimated damping distribution between EC/EO recording conditions across participants.
Our findings suggest that alpha-blocking and the 1/f noise structure
are both explicable through a singular process of dynamically dampedalpha-band activity. The estimated damping distributions are typically found to be bimodal or trimodal (Fig. 1). The number and position of the modes is related to the sharpness of the alpha resonance(amplitude, FWHM) and the slope of the power spectrum. The results
suggest that there exists an intimate relationship between restingstate alpha activity and 1/f noise with changes in both governed bychanges to the damping of the underlying alpha relaxation processes.
In particular, alpha-blocking is observed to be the result of the mostweakly damped distribution mode (peak at 0.4 - 0.6s^-1) becoming
more heavily damped (peak at 1.0 - 1.5s^-1). Reductions in the slopeof the 1/f noise are the result of the alpha relaxation processes becoming more broadly distributed in their respective dampings with moreweighting towards heavily damped alpha activity. The results suggest
a novel way of characterizing resting M/EEG power spectra and provides new insight into the central role that damped alpha-band activity may play in the interesting spatio-temporal features of resting state
M/EEG.

Fig. 1 Top panel: Power spectrum for EC and EO (dashed) condi‑
tions plotted in logarithmic and linear coordinates. Power spectrum
generated using the estimated damping distributions in the forward
problem are shown for EC and EO (solid) alongside the measured
power spectra. Bottom panel: Typical damping distribution results
found in EC and EO for subjects who demonstrate alpha blocking
Future work will explore the more complex case where we expect a
distribution over both frequency and damping for the stochastic
relaxation processes, elucidating any frequency dependent damping effects between conditions. The inverse problem can be solved
via gradient descent methods where we estimate the 2-dimensional
probability density function over frequency and damping from a given
power spectrum.
P114
Effect of diverse recoding of granule cells on delay eyeblinkconditioning in a cerebellar network
Sang‑Yoon Kim, Woochang Lim
Daegu National University of Education, Institute for Computational
Neuroscience and Department of Science Education, Daegu, South Korea
Correspondence: Woochang Lim (wclim@icn.re.kr)
BMC Neuroscience 2020, 21(Suppl 1):P114
We consider a ring network for the delay eyeblink conditioning, andinvestigate the effect of diverse firing activities of granule (GR) cells on
the eyeblink conditioning under conditioned stimulus (tone) by varying the connection probability pc from Golgi to GR cells. For an optimal value of p*c, individual GR cells exhibit diverse spiking patterns
which are well- or poor-matched with the unconditioned stimulus(airpuff). Then, these diversely-recoded signals via parallel-fibers (PFs)from GR cells are effectively depressed by the error teaching signals via
climbing fibers (CFs) from the inferior olive. Synaptic weights at wellmatched PF–Purkinje cell (PC) synapses of active GR cells are strongly
depressed via strong long-term depression (LTD), while no LTD occurs
at poor-matched PF–PC synapses. This kind of “effective” depression
at PF-PC synapses coordinates firings of PCs effectively, which thenexert effective inhibitory coordination on cerebellar nucleus (CN)(which evokes conditioned response (CR; eyeblink)). When the learning trial passes a threshold, CR occurs. In this case, the timing degree
Td becomes good due to presence of poor-matched spiking groupwhich plays a role of protection barrier for the timing. With further
increase in trials, strength of CR SCR increases due to strong LTD in thewell-matched spiking group, while its timing degree decreases. Thus,
the overall efficiency degree Le (taking into consideration both timing
and strength of CR) for the eyeblink increases with trials, and eventually saturates. By changing pc, we also investigate the delay eyeblinkconditioning and find that a plot of Le versus pc forms a bell-shaped
curve with a peak at p*c (where the diversity degree D in firing of GR
cells is also maximum). The more diverse in spiking patterns of GR
cells, the more effective in CR for the eyeblink.

﻿BMC Neurosci 2020, 21(Suppl 1):54

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Fig. 1 a Diversity degree D versus the connection probability ­pc
from Golgi cells to granule cells. b1 Timing degree ­Td versus p­ c, b2
strength of conditional response ­SCR versus p­ c, and b3 overall effi‑
ciency degree ­Le versus ­pc
P115
Approximating information filtering of a two‑stage neural system
Gregory Knoll, Žiga Bostner, Benjamin Lindner
Humboldt‑Universitaet zu Berlin, Physics, Berlin, Germany
Correspondence: Gregory Knoll (gregory.knoll@bccn‑berlin.de)
BMC Neuroscience 2020, 21(Suppl 1):P115
Information streams are processed in the brain by populations of neurons tuned to perform specific computations, the results of which areforwarded to subsequent processing stages. Building on theoreticalresults for the behavior of single neurons and populations, we investigate the extent to which a postsynaptic cell (PSC) can detect the
information present in the output stream of a population which has
encoded a signal. In this two-stage system, illustrated in Figure 1A, thepopulation is a simple feedforward network of integrate-and-fire neurons which integrate and relay the signal, reminiscent of auditory orelectroreceptor afferents in the sensory periphery. Depending on theapplication, the information relevant for the PSC may be contained ina specific frequency band of the stimulus, requiring the PSC to properly tune its information encoding to that band (information filtering).
In the specific setup studied here, information filtering is associatedwith detecting synchronous activity. It was found that synchronous
activity of a neural population selectively encodes information abouthigh-frequency bands of a broadband stimulus, and it was hypothesized that this information can be read out by coincidence detector
cells that are activated only by synchronous input. Firstly, we test thishypothesis and match the key characteristics of information filtering,
the spectral coherence function, of the PSC and the stimulus and of
the time-dependent synchrony in the population output and thestimulus (Fig. 1B, left); we show that the relations between the synchrony and PSC thresholds and between the synchrony window and
PSC time constant are roughly linear (Fig. 1B, right), which implies thatthe synchronous output of the population can be taken as a proxy
for the postsynaptic coincidence detector and, conversely, that the
PSC can be made to detect synchrony (or coincidence) by adjusting
its time constant and threshold. Secondly, we develop an analytical
approximation for the coherence function of the PSC and the stimulus
and demonstrate its accuracy by comparison against numerical simulations (Fig. 1C), both in the fluctuation-dominated and mean-driven
regimes of the PSC.

Fig. 1 A System diagram of the two-stage neural system. B Matchingcoherence functions to relate synchronous output criteria to PSC
parameters. C Band-pass filtering of information from a broadband
stimulus by the two-stage neural system

P116
Astrocyte simulations with realistic morphologies reveal diverse
calcium transients in soma and processes
Ippa ­Seppala1, Laura K­ eto2, Iiro A­ hokainen1, Nanna Förster1, Tiina­Manninen2, Marja‑Leena ­Linne1
1
Tampere University, Medicine and Health Sciences, Tampere, Finland;
2
Tampere University, Faculty of Medicine and Health Technology, Tampere,
Finland
Correspondence: Ippa Seppala (ippa.seppala@tuni.fi)
BMC Neuroscience 2020, 21(Suppl 1):P116
The role of astroglia has long been overlooked in the field of computational neuroscience. Lately their involvement in multiple higherlevel brain functions, including neurotransmission, plasticity, memory,and neurological disorders, has been found to be more significant
than previously thought. It has been hypothesised that astrocytes
fundamentally affect the information processing power of the mammalian brain. As the glia to neuron ratio increases when movingfrom simpler organisms to those more complex, it is clear that more
attention should be directed to glial involvement. Despite the recent
advances in neuroglial research there still exists a lack of glia-specific﻿BMC Neurosci 2020, 21(Suppl 1):54

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computational tools. Astroglia differ considerably from neurons intheir morphology as well as their biophysical functions [1], making it
difficult to acquire reliable simulation results with simulators madefor studying neuronal behaviour. As the differences in cellular dynamics of astrocytes compared to those of neurons are significant, there
clearly exists a need for tailored methods for simulating the behaviourof glial cells.
One such astrocyte specific simulator has been developed [2]. In
simulations ASTRO uses MATLAB and NEURON environments [3]and is capable of representing various biologically relevant astroglial
mechanisms such as calcium waves and diffusion. In this work we used
ASTRO to simulate several astrocytic functions with the help of existing in vivo morphologies from various brain areas. We concentrated
on calcium transients, as calcium-mediated signaling is thought to be
the main mechanism of intra- and intercellular messaging between
astroglia and other neural cell types. The time-scales of these calciummediated events have recently been shown to differ considerably in
different spatial locations of astrocytes. We were able to reproduce
these results in silico by simulating a morphologically detailed computational model that we developed based on previous work [4,5]. Thiswas partly due to ASTRO’s capability to analyse the microscopic calcium dynamics in fine processes, branches and leaves.
With our model ASTRO proved to be a promising tool in simulating
astrocytic functions and could potentially offer novel insights to glianeuron interactions also in future work.
Acknowledgements: The work was supported by Academy of Finlandthrough grants (297893, 326494, 326495) and the European Union’s
Horizon 2020 Framework Programme for Research and Innovation
under the Specific Grant Agreement No. 785907 (Human Brain ProjectSGA2).

data was analysed to extract, among other properties, duration, intensity and frequency of burst events [1].
The computational model incorporates fast burst propagation anddecay mechanisms, as well as the slower burst initiation mechanisms.
We first constructed the fast part of the model as a generic spiking
neuronal network and optimized it to the experimental data describing intra-burst properties. We developed a model fitting routine relying on multi-objective optimization [2]. The optimized ‘fast’ model
was then extended with a selected astrocytic mechanism operating
on a similar time-scale as the network burst initiation [3]. Typically,
the burst initiation is attributed to a combination of noisy inputs andthe dynamics of neuronal (and synaptic) adaptation currents. Whilenoise provides necessary depolarization of cell membrane the adaptation currents prohibit fast initiation of the next burst event. The noise
might account for the randomness in ion channel opening and closing, the spontaneous synaptic release and other sources of randomness. The adaptation accounts for the kinetics of various ion channels.
We explore the role of a non-standard deterministic mechanism introduced through slow inward current from astrocytes to neurons.
We demonstrate that the fast neuronal part of the model successfully
reproduces intra-burst dynamics, including the duration and intensityof network bursts. The model is flexible enough to account for several experimental conditions. Coupled to the slower astrocyte-neuron
interaction mechanism the system becomes capable of generating
bursts with the frequency proportional to the one seen in vitro.
Acknowledgements: This research has received funding from theEuropean Union’s Horizon 2020 Framework Programme for Researchand Innovation under the Specific Grant Agreement No. 785907(Human Brain Project SGA2). The funding has also been received fromthe Academy of Finland through grants No. 297893, 326494, 326495.

References
1. Calì C, et al. 3D cellular reconstruction of cortical glia and parenchymal mor‑phometric analysis from Serial Block-Face Electron Microscopy of juvenile
rat. Progress in Neurobiology. 2019;183: 101696.
2. Savtchenko LP, et al. Disentangling astroglial physiology with a realistic cell
model in silico. Nature Communications. 2018; 9(1): 1-15.
3. Carnevale T. Neuron simulation environment. Scholarpedia. 2007; 2(6): 1378.
4. Manninen T, Havela R, Linne M-L. Reproducibility and comparability of com‑putational models for astrocyte calcium excitability. Frontiers in Neuroinfor‑
matics. 2017; 11: 11.
5. Manninen T, Havela R, Linne M-L. Computational models for calcium-medi‑
ated astrocyte functions. Frontiers in Computational Neuroscience. 2018;
12: 14.

References
1. Teppola H, Aćimović J, Linne ML. Unique features of network bursts emerge
from the complex interplay of excitatory and inhibitory receptors in rat
neocortical networks. Frontiers in Cellular Neuroscience. 2019; 13: 377.
2. Aćimović J, Teppola H, Mäki-Marttunen T, Linne M-L. Data-driven study ofsynchronous population activity in generic spiking neuronal networks: Howmuch do we capture using the minimal model for the considered phenom‑
ena? BMC Neuroscience 2018;19(2): 136.
3. Aćimović J, et al. Modeling the influence of neuron-astrocyte interactions
on signal transmission in neuronal networks. BMC Neuroscience 2019; 20(1):
178.

P117
The interplay of neural mechanisms regulates spontaneous cortical
network activity: inferring the role of key mechanisms using
data‑driven modelingJugoslava Acimovic, Tiina Manninen, Heidi Teppola, Marja‑Leena Linne
Tampere University, Faculty of Medicine and Health Technology, Tampere,
Finland
Correspondence: Jugoslava Acimovic (jugoslava.acimovic@tuni.fi)
BMC Neuroscience 2020, 21(Suppl 1):P117

P118
Compartmental models for mammalian cortical pyramidal neurons:a survey of published models, model complexity and parameter
sensitivity
Emma ­Huhtala1, Jugoslava ­Acimovic2, Mikko Lehtimäki2, Marja‑Leena­Linne31
Tampere University, Tampere, Finland; 2Tampere University, Facultyof Medicine and Health Technology, Tampere, Finland; 3Tampere University,
Medicine and Health Sciences, Tampere, Finland
Correspondence: Emma Huhtala (emma.huhtala@tuni.fi)
BMC Neuroscience 2020, 21(Suppl 1):P118

In isolated neural systems devoid of external stimuli, the exchange
between neuronal, synaptic and putatively also glial mechanisms
gives rise to spontaneous self-sustained synchronous activity. Thisphenomenon has been extensively documented in dissociated cortical cultures in vitro that are routinely used to study neural mechanisms in health and disease. We examine these mechanisms using a
new data-driven computational modeling approach. The approach
integrates standard spiking network models, non-standard glial mechanisms and network-level experimental data.
The experimental data represents spontaneous activity in dissociated rat cortical cultures recorded using microelectrode arrays. The
recordings were performed under several experimental protocols thatinvolved pharmacological manipulation of network activity. Undereach protocol the activity exhibited characteristic network bursts, theshort intervals (100ms to 1s) of intensive network-wide spiking interleaved by longer (~10s) periods of sparse uncorrelated spikes. The

Pyramidal neurons are abundant in the neocortex and known to contribute to diverse and complex cognitive functions. To better understand the role of individual pyramidal cell types we need neuronmodels that can be efficiently and reliably simulated and analyzed
when embedded into circuit-level models yet that maintain the necessary structural and functional complexity. In this study we contribute to this goal the following ways: 1) We review and compare over
50 published mammalian cortical pyramidal neuron models available
in the literature and public repositories (with the focus on models
implemented in the NEURON simulation environment). 2) We test two
recently published tools that tackle critical issues for detailed neuronmodeling, the role of model complexity and size and the sensitivity tonumerous and occasionally unreliable model parameters.
In goal 1, we compared the models based on the following criteria: thebrain area and layer, number of compartments, biophysical properties,
software used to simulate the model, and the amount of experimental

﻿BMC Neurosci 2020, 21(Suppl 1):54

data used to construct and fine-tune the model. Based on this work,
we chose a layer 2/3 pyramidal cell model from the rat somatosensory
cortex, acquired from the Blue Brain Project data portal [1], as our test
case. We used two recently published toolboxes, Neuron_Reduce andUncertainpy, to analyze the model. Neuron_Reduce [2] is designed forsimplifying the morphology of the model while replicating the model
dynamics and accelerating the simulations considerably. Uncertainpy[3] allows the user to examine the impact of uncertainty and sensitivityof the model parameters.
The analyses carried out in this work show that Neuron_Reduce toolbox is an effective tool for simplifying the morphological structure ofneuron models. With moderate reduction of the model (preserving
10-25% of the original model compartments) the Neuron_Reduce
toolbox simplifies the dendritic structure of the cell while replicating
the behavior of the original model. However, a dramatic reduction(preserving 3% of the original model compartments) led to changes
in the shape of action potentials. The results obtained with Uncertainpy suggest that the reduced model shows sensitivity to only a subset of model parameters among those that affect the original model.
For example, while the original model depends on several somatic
conductances, the reduced model is sensitive mainly to sodium andpotassium channel conductances. Based on our testing, both toolboxes will be useful tools for analyzing models in neuroscience. In
addition, they can help the re-use of compartmental models in newmodeling initiatives, particularly when modeling multiple spatiotemporal scales of the brain phenomena.

Page 69 of 123

Importantly, these results also have implications for directed functional network inference from time series, which is one of the main
applications of transfer entropy in neuroscience. The asymmetry of
information transfer suggests that links from hubs to peripheral nodes
would generally be easier to infer than links between hubs, as well as
links from peripheral nodes to hubs. This could bias the estimation of
network properties such as the degree distribution and the rich-clubcoefficient.
In addition to the dependence on the in-degree, the transfer entropy
is directly proportional to the weighted motifs involving common
parents or multiple walks from the source to the target (Fig. 1). These
motifs are more abundant in clustered or modular networks than in
random networks, suggesting a higher transfer in the former case. Further, if the network has only positive edge weights, we have a positive
correlation to the number of such motifs. This applies in the mammalian cortex (on average, since the majority of connections are thought
to be excitatory) – implying that directed functional network inferencewith transfer entropy is better able to infer links within brain modules(where such motifs enhance transfer entropy values) in comparison tolinks across modules.

Acknowledgments: This work was supported by the Academy of Finland (decision Nos 297893 and 318879).
References
1. Blue Brain Portal – Digitally reconstructing and simulating the brain[Online]. Available: https://portal.bluebrain.epfl.ch/. [Accessed:
12-May-2020]
2. Amsalem O, et al. An efficient analytical reduction of detailed nonlinear
neuron models. Nature Communications. 2020; 11(1): 1-3.
3. Tennøe S, Halnes G, Einevoll GT. Uncertainpy: A Python toolbox for uncer‑
tainty quantification and sensitivity analysis in computational neuroscience.
Frontiers in Neuroinformatics. 2018; 12: 49.

P119
Relating transfer entropy to network structure and motifs,
and implications for brain network inference
Leonardo Novelli, Joseph Lizier
The University of Sydney, Centre for Complex Systems, Sydney, Australia
Correspondence: Leonardo Novelli (lnov6504@uni.sydney.edu.au)
BMC Neuroscience 2020, 21(Suppl 1):P119
Transfer entropy is an established method for the analysis of directed
relationships in neuroimaging data. In its original formulation, transfer
entropy is a bivariate measure, i.e., a measure between a pair of elements or nodes [1]. However, when two nodes are embedded in a
network, the strength of their direct coupling is not sufficient to fully
characterize the transfer entropy between them. This is because transfer entropy results from network effects due to interactions between
all the nodes.
In this theoretical work, we study the bivariate transfer entropy as afunction of network structure, when the link weights are known. In
particular, we use a discrete-time linear Gaussian model to investigate
the contribution of small motifs, i.e., small subnetwork configurations
comprising two to four nodes. Although the linear model is simplistic, it is widely used and has the advantage of being analytically tractable. Moreover, using this model means that our results extend to
Granger causality, which is equivalent to transfer entropy for Gaussianvariables.
We show analytically that the dependence of transfer entropy on the
direct link weight is only a first approximation, valid for weak coupling.
More generally, the transfer entropy increases with the in-degree ofthe source and decreases with the in-degree of the target, which suggests an asymmetry of information transfer between hubs and peripheral nodes.

Fig. 1 Network motifs involved in the bivariate transfer entropy fromnode X to node Y
Reference
1. Schreiber T. Measuring information transfer. Physical review letters. 2000;
85(2): 461.

P120
Ageing‑related changes in prosocial reinforcement learningand links to psychopathic traits
Jo ­Cutler1, Marco ­Wittmann1, Ayat ­Abdurahman2, Luca ­Hargitai1, Daniel­Drew1, Masud ­Husain1, Patricia L­ ockwood1
1
University of Oxford, Psychology, Oxford, United Kingdom; 2Universityof Cambridge, Psychology Department, Cambridge, United Kingdom
Correspondence: Jo Cutler (jo.cutler@psy.ox.ac.uk)
BMC Neuroscience 2020, 21(Suppl 1):P120
Prosocial behaviours, actions that help others, are vital for maintaining social bonds and are linked with improved health. However, ourability to learn which of our actions help others could change as we
get older, as existing studies suggest declines in reinforcement learning across the lifespan [1]. This decline in associative learning could be
explained by the significant age-related decrease in dopamine transmission [2] which has been suggested to code prediction errors [3].
Alternatively, prosocial learning might not only rely on learning abilities but also on the motivation to help others. This motivation, whichis reduced in disorders such as psychopathy, might also shift with age,with a trend for lower levels of antisocial behaviour in older adults [4].
Interestingly, the decrease in dopamine levels in older adults could
also support this hypothesis of increased prosociality, as higher dopamine has been linked to lower altruism [5].
Here, using computational modelling of a probabilistic reinforcement
learning task (Fig. 1), we tested whether younger (age 18-36) and older(age 60-80, total n=152) adults can learn to gain rewards for themselves, another person (prosocial), or neither individual (control). We

﻿BMC Neurosci 2020, 21(Suppl 1):54

replicated existing work showing younger adults were faster to learn
when their actions benefitted themselves, compared to when they
helped others [6]. Strikingly however, older adults showed a reduced
self-bias, compared to younger adults, with learning rates that did
not significantly differ between self and other. In other words, older
adults showed a relative increase in the willingness to learn about
actions that helped others. Moreover, we find that these differences inprosocial learning could emerge from more basic changes in personality characteristics over the lifespan. In older adults, psychopathic traits
were significantly reduced and correlated with the difference between
prosocial and self learning rates. Importantly, the difference between
self and other learning rate was most reduced in older people with thelowest psychopathic traits. Overall, we show that older adults are lessself-biased than younger adults, and this change is associated with adecline in psychopathic traits. These findings highlight the importanceof examining individual differences across development and haveimportant implications for theoretical and neurobiological accounts ofhealthy ageing.

Fig. 1 Behavioural task and data. A Reinforcement learning task:
participants played for either themselves, the other participant, orno one. B Group-level learning curves showing choice behaviour inthe three learning conditions for each age group. C Comparison oflearning rates from the computational model. D Median difference
between learning rates in the other and self conditions. Asterisks
represent significant differences (p<.05)
References
1. Mell T, Heekeren HR, Marschner A, Wartenburger I, Villringer A, Reischies FM.
Effect of aging on stimulus-reward association learning. Neuropsychologia.
2005; 43(4): 554-563.
2. Li S-C, Lindenberger U, Bäckman L. Dopaminergic modulation of cognition
across the life span. Neuroscience and Biobehavioral Reviews. 2010; 34(5):625-630.
3. Schultz W. Dopamine reward prediction error coding. Dialogues in Clinical
Neuroscience. 2016; 18(1): 23-32.
4. Gill DJ, Crino RD. The Relationship between Psychopathy and Age in a NonClinical Community Convenience Sample. Psychiatry, Psychology and Law.
2012; 19(4): 547-557.
5. Crockett MJ, et al. Dissociable effects of serotonin and dopamine on thevaluation of harm in moral decision making. Current Biology. 2015; 25(14):1852-9.

Page 70 of 123

6. Lockwood PL, Apps MAJ, Valton V, Viding E, Roiser JP. Neurocomputational
mechanisms of prosocial learning and links to empathy. Proceedings of the
National Academy of Sciences. 2016; 113(35): 9763-9768.

P121
Avalanches and emergent activity patterns in simulated mouseprimary motor cortex
Donald Doherty, Salvador Dura‑Burnal, William W Lytton
SUNY Downstate Medical Center, Department of Physiology
and Pharmacology, Brooklyn, New York, United States of America
Correspondence: Donald Doherty (donald.doherty@actionpotential.
com)
BMC Neuroscience 2020, 21(Suppl 1):P121
Avalanches display non-Poisson distributions of correlated neuronal
activity that may play a significant role in signal processing. The phenomenon appears robust but mechanisms remain unknown due toan inability to gather large sample sizes, and difficulties in identifying
neuron morphology, biophysics, and connections underlying avalanche dynamics. We set out to understand the relationship between
power-law activity patterns, their values, and the neural responses
observed from every neuron across different layers, cell populations,and the entire cortical column using a detailed M1 model with 15
neuron types that simulated the full-depth of a 300μm diameter column with 10,073 neurons and ~18e6 connections. Self-organized andself-sustained activity from our simulations have power-law valuesof -1.51 for avalanche size and -1.98 for duration distributions, which
are in the range noted in both in vitro and in vivo neural avalanche
preparations reported by Beggs and Plentz (2003). We applied a 0.57
nA, 100ms stimulus across 40μm in diameter and full column depth
at each of 49 gridded locations (40μm) across the pia surface of our
400μm diameter cylindrical cortical column. Stimuli applied to 4 locations (8.2%) produced no sustained responses. Self-sustained activity
was seen in the other 45 locations, which always included activity in
IT5B or IT5B and IT6. In 6 locations activity was restricted to IT5B or
IT5B/IT6 alone (avalanche size: ~ -2.8). Intermittent spread of activity from IT5B/IT6 across other neuron types and layers was seen in
24 locations (avalanche size: ~ -2.0). In 15 locations, frequent spreadof activity to other neuron types and layers was observed (avalanche
size: ~ -1.5). Avalanches were defined using binned spiking activity(1ms bins). Each avalanche was composed of adjacent bins filled with
one or more action potentials, preceded and followed by at least one
empty bin. A prolonged 10-minute M1 simulation with different connectivity produced 15,579 avalanches during sustained activity after
the initial 100ms stimulation. Again, IT5B/IT6 activity was constant andpunctuated by more widespread activity. Three distinct patterns ofactivity spontaneously recurred and could be characterized by delta,
beta, or gamma frequency dominance. All large-scale avalanches werecomposed of 1 or a combination of these 3 recognizable patterns.
Between the large-scale avalanches we saw three patterns of activity:
1) continuous IT5B and IT6 neuron activity, 2) vigorous layer 5 and IT6
activity, or 3) vigorous layer 5 and IT6 activity that transitioned to continuous IT5B and IT6 activity. Since cortical column activity with just
IT5B and IT6 activity showed little correlation (very steep and narrow
distributions of avalanche sizes and durations), we hypothesize thatthe addition of avalanches with layer 5 and IT6 activity (activity patterns 2 and 3 above) result in more correlated activity and power-law
values closer to -1.51 and -1.98 for size and duration respectively. In
conclusion, the increase in correlated activity among neuronal components parallels the emergence of clearly identifiable activity patterns
across time and cortical layers and may generate rhythmic activity.

﻿BMC Neurosci 2020, 21(Suppl 1):54

Page 71 of 123

where conduction delay statistics are autonomously adjusted toensure efficient neural communication. Specifically, our analysis
shows that adaptive conduction velocities along axonal connections
stabilizes oscillatory neural activity across a wide range of connectivity gain and frequency bands. Resulting conduction delays become
statistically similar, promoting phase-locking irrespective of the distances. As a corollary, global phase-locked states are more resilient todiffuse decreases in connectivity, reflecting damage caused by a neurological disease, for instance. Our work suggests that adaptive myelination may be a mechanism that enable brain networks with a meansof temporal self-organization, resilience and homeostasis.
P123
Computational implementation in NEURON of inter‑cellular calcium
waves in syncytial tissue
Nilapratim Sengupta, Rohit Manchanda
Indian Institute of Technology Bombay, Department of Biosciences
and Bioengineering, Mumbai, India
Correspondence: Nilapratim Sengupta (nilapratim@iitb.ac.in)
BMC Neuroscience 2020, 21(Suppl 1):P123
Fig. 1 Forty-nine raster plots each showing 60 seconds of activity
across all 10,073 neurons after a 100 millisecond 0.57 nano-amperes
current was applied to a volume of the cortical column 40 um indiameter and spanning the full 1,350 um depth from pia to whitematterP122
Adaptive activity‑dependent myelination promotes
synchronization in large‑scale brain networks
John ­Griffiths1, Jeremie ­Lefebvre2, Rabiya N­ oori3, Daniel ­Park4, Sonya­Bells5, Paul F­ rankland5, Donald M­ abbott5
1
Centre for Addiction and Mental Health, Krembil Centre
for Neuroinformatics, Toronto, Canada; 2University of Ottawa, Departmentof Biology, Ottawa, Canada; 3Krembil Research Institute, Toronto, Canada;
4
University of Toronto, Toronto, Canada; 5The Hospital for Sick Children,
Neurosciences and Mental Health, Toronto, Canada
Correspondence: Jeremie Lefebvre (jeremie.lefebvre@uottawa.ca)
BMC Neuroscience 2020, 21(Suppl 1):P122
Communication and oscillatory synchrony between distributed neural populations is believed to play a key role in multiple cognitive
and neural functions. These interactions are mediated by long-range
myelinated axonal fibre bundles, collectively termed as white matter.
While traditionally considered to be static after development, whitematter properties have been shown to change in an activity-dependent way through learning and behavior: a phenomenon known aswhite matter plasticity. In the central nervous system this plasticity
stems from oligodendroglia, which form myelin sheaths to regulate
the conduction of nerve impulses across the brain, hence critically
impacting neural communication. We here shift the focus from neuralto glial contribution to brain synchronization and examine the impactof adaptive, activity-dependent change in conduction velocity on thelarge-scale phase-synchronization of neural oscillators.
We used a network model built of reciprocally coupled Kuramotophase oscillators whose connections are based on available primate
large-scale white matter neuroanatomy data. Our computational andmathematical results show that such adaptive plasticity endows whitematter networks with self-regulatory and self-organizing properties,

Calcium plays highly critical roles in various physiological processes
such as muscle contraction, neurotransmission, cell growth and proliferation. Intracellular calcium handling mechanisms have been studied
extensively. However, concepts of intracellular calcium dynamics must
be complemented with the knowledge of calcium spread in tissues
where neighbouring cells are coupled to each other forming a syncytium. Intercellular calcium waves (ICW) are ‘complex spatiotemporal events’ essentially comprising of an elevated level of intracellular
calcium that appears to spread from the initiating/stimulated cell tothe coupled neighbours [1]. Gap junction mediated diffusion has been
identified as a crucial mechanism for ICW in syncytial tissues [2].
The complex structure of syncytial tissue, coupled with different signalling molecules and their varied mechanisms of involvement makes
it difficult to study ICW from a quantitative point of view using in vitro
or in vivo experiments. Though mathematical models describing ICW
propagation in two-dimensions exist, there is no report of a biophysical model to account for three dimensional propagation of ICW in vivo
in syncytial tissues. A key objective of our work was to realize, using
the NEURON platform, a model for 3-D propagation. Several computational labs (primarily working on neural networks) make use of thisplatform to build models. However to our knowledge, in none of theexisting cellular network models has chemical coupling of cells been
incorporated. Successful implementation of our developed technique would produce a biophysically detailed model incorporating
structural, electrical as well as chemical aspects that could be used to
upgrade all existing models once suitable changes in parameters are
made.
Gap junctions, in the NEURON platform, have usually been modelled
as low resistance electrical shunts between connected cells [3]. In anovel approach we modelled the gap junction such as to enable it totransfer calcium between connected cells, based on the ion’s electrochemical gradient. We have equipped the detrusor smooth muscle
cell model with intracellular calcium handling mechanisms and thenmodified the gap junctional connection to incorporate intercellular
calcium flow, besides non-specific current. We have successfully simulated calcium spread from the source cell to its adjoining neighbours
and beyond. Within the network, as the distance from the source cellincreases, extent of calcium flow diminishes as it propagates due todiffusion, buffering and its being pumped out of the cells (Fig. 1).

﻿BMC Neurosci 2020, 21(Suppl 1):54

Page 72 of 123

reconstructing the astroglial nanoscopic architecture. The stem tree
was built with the NEURON CellBuilder tool [4] with values found fromliterature [2,5]. For the nanoscopic architecture, a 3D reconstructionof a Bergmann glial appendage was recreated based on a video file[2] with AgiSoft Metashape and Blender. As the exact reconstructionof the nanoscopic architecture would be computationally unfeasible,
a novel computational tool ASTRO [6] was used to define statisticalproperties from the reconstructed appendage. The final morphology
was assembled with ASTRO and verified functionally by simulating
microscopic calcium dynamics with the tool.
Acknowledgements: We are very grateful to Prof. Helmut Kettenmann for providing us the video file of Bergmann glia appendage. The
work was supported by Academy of Finland (Nos. 326494, 326495).

Fig. 1 Calcium transients triggered in connected neighbouring cells
due to ICW propagation from stimulated source cell
Our model is in a preliminary stage and is yet to be tuned. Literature
pertaining to ICW in detrusor is scant. Hence the model would need tobe tuned in terms of overall cellular response, with active ion channels
integrated. Subsequently, techniques mimicking messenger regeneration would need to be incorporated, besides passive diffusion.
References
1. Leybaert L, Sanderson MJ. Intercellular Ca2+ waves: mechanisms and func‑
tion. Physiological Reviews. 2012; 92(3): 1359-92.
2. Sanderson MJ, Charles AC, Dirksen ER. Mechanical stimulation and intercel‑
lular communication increases intracellular Ca2+ in epithelial cells. Cell
regulation. 1990; 1(8): 585-96.
3. Appukuttan S, Brain KL, Manchanda R. A computational model of urinary
bladder smooth muscle syncytium. Journal of Computational Neurosci‑
ence. 2015; 38(1): 167-87.

P124
Morphologically‑detailed reconstruction of cerebellar glial cells
Laura Keto, Tiina Manninen
Tampere University, Faculty of Medicine and Health Technology, Tampere,
Finland
Correspondence: Laura Keto (laura.keto@tuni.fi)
BMC Neuroscience 2020, 21(Suppl 1):P124
The cerebellar circuitry has been modeled widely, with realistic andaccurate models now existing for most of the cerebellar neurons[1]. The glial cell population of the cerebellum, however, has been
largely neglected by computational modelers. No realistic whole-cellmodels have been previously implemented for any of the cerebellar
glial cell types; oligodendrocytes, microglia, or astroglia. In this work,
we were interested in reconstructing a detailed morphology for the
best-known cerebellar astroglial cell type, Bergmann glia. Bergmannglia are radial astrocytes of the cerebellar cortex, with somata locatedat the Purkinje cell layer, 3-6 long processes extending throughthe molecular layer, and endfeet. The processes give rise to smaller
appendages characterized by microdomains that enwrap neuronal
synapses [2]. The reciprocal communication between Bergmann glia
and the neighboring neurons is vital for development and plasticity of
the cerebellum [3].
Currently no reconstructions of cerebellar glial cells are available inpublic databases. The reconstruction of Bergmann glia required bothan astroglial stem tree as well as a more detailed morphology for

References
1. D’Angelo E, et al. Modeling the cerebellar microcircuit: New strategies for along-standing issue. Frontiers in Cellular Neuroscience. 2016; 10: 176.
2. Grosche J, et al. Microdomains for neuron-glia interaction: Parallel fiber
signaling to Bergmann glial cells. Nature Neuroscience. 1999; 2(2): 139–143.
3. Bellamy TC. Interactions between Purkinje neurones and Bergmann glia.
The Cerebellum. 2006; 5(2): 116–126.
4. Carnevale NT, Hines ML. The NEURON book. Cambridge University Press;
2006.
5. Lippman JJ, Lordkipanidze T, Buell ME, Yoon SO, Dunaevsky A. Morphogene‑sis and regulation of Bergmann glial processes during Purkinje cell dendritic
spine ensheathment and synaptogenesis. Glia. 2008; 56(13): 1463–1477.
6. Savtchenko LP, et al. Disentangling astroglial physiology with a realistic cell
model in silico. Nature Communications. 2018; 9: 3554.

P125
A predictive coding model of transitive inference
Moritz ­Moeller1, Rafal B­ ogacz2, Sanjay ­Manohar1
1
University of Oxford, Nuffield Department of Clinical Neurosciences,
Oxford, United Kingdom; 2University of Oxford, MRC Brain Network
Dynamics Unit, Oxford, United Kingdom
Correspondence: Moritz Moeller (moritz.moeller@ndcn.ox.ac.uk)
BMC Neuroscience 2020, 21(Suppl 1):P125
Transitive inference – deducing that “A is better than C” from thepremises “A is better than B” and “B is better than C” – is a basic formof deductive reasoning; both humans and animals are capable ofit. However, the mechanism that enables transitive inference is notunderstood. Partly, this is due to the absence of a concrete, falsifiable
formulation of the so-called cognitive explanation of transitive inference (which suggests that subjects combine the facts they observe
into a mental model, which they then use for reasoning). In this work,
we use the predictive coding method to derive a precise, mathematical implementation of the cognitive explanation of transitive inference(Fig. 1A shows a schematic representation of the model we use). We
test our model by simulating a set of typical transitive inference experiments and show that it reproduces several phenomena observed inanimal experiments. For example, our model reproduces the gradualacquisition of premise pairs (A > B, B > C) and the simultaneously
emerging capability for transitive inference (A>C) (Fig. 1B). We expect
this work to lead to novel testable predictions that will inspire future
experiments and help to uncover the mechanism behind transitive
inference. Further, our work adds support to predictive coding as auniversal organising principle of brain function.

﻿BMC Neurosci 2020, 21(Suppl 1):54

Page 73 of 123

distributions by modelling persistence diagrams as marked Poisson
point processes.
We test our Bayesian topological method to classify synthetic EEG. We
employ three common classifiers: linear regression and support vector machines with linear and radial kernels, respectively. We simulate
synthetic EEG with an autoregressive (AR) model, which works byrecasting a standard AR model as linearly filtered white noise, enabling straightforward computation of PSDs. The AR model allows us
to control the location and width of peaks in PSDs. With this model in
hand, we create five classes of signals with peaks in their PSDs at zero
to simulate the approximate 1/f behavior of EEG PSDs, four of which
also have oscillatory components at 6 Hz (theta), 10 Hz (alpha), 14 Hz(low beta), and 21 Hz (high beta); the fifth class (null) lacks any suchcomponent. We repeat this process for two different widths of peaks,
narrow (4 Hz) and wide (32 Hz). With data in hand, we extract featuresusing periodograms, persistence diagrams, and our Bayesian topological method, then independently use these features in classification for
the wide and narrow width cases. Preliminarily, while both the Bayesian topological method and periodogram features obtain near perfectfor the narrow peak case, the Bayesian topological method outperforms the periodogram features over all tested classifiers in the wide
peak case.

Fig. 1 A Schematic representation of the predictive coding network
used to model transitive inference. B Model predictions of pairwise
value differences in a hierarchy stimuli A > B > C. As the premises A >B and B > C are learned, the model’s evaluations become consistent
with the hierarchy

P126
Topological Bayesian signal processing with application to EEG
Christopher ­Oballe1, Alan C­ zerne1, David B­ oothe2, Scott K­ erick2, Piotr­Franaszczuk2, Vasileios ­Maroulas1
1
University of Tennessee, Department of Mathematics, Knoxville,
Tennessee, United States of America; 2U.S. Army Research Laboratory,
Human Research Engineering Directorate, Aberdeen Proving Ground,
Maryland, United States of America
Correspondence: Piotr Franaszczuk (pfranasz@gmail.com)
BMC Neuroscience 2020, 21(Suppl 1):P126
Electroencephalography is a neuroimaging technique that works bymonitoring electrical activity in the brain. Electrodes are placed on thescalp and local changes in voltage are measured over time to produce
a collection of time series known as electroencephalograms (EEGs).
Traditional signal processing metrics, such as power spectral densities(PSDs), are generally used to analyze EEG since frequency content of
EEG is associated with different brain states. Conventionally, PSD estimates are obtained via discrete Fourier transforms. While this method
effectively detects low-frequency components because of their highpowers, high-frequency activity may go unnoticed because of its relatively weaker power. We employ a topological Bayesian approach thatsuccessfully captures even these low-power, high-frequency components of EEG.
Topological data analysis encompasses a broad set of techniques thatinvestigate the shape of data. One of the predominant tools in topological data analysis is persistent homology, which creates topological descriptors called persistence diagrams from datasets. In particular,
persistent homology offers a novel technique for time series analysis.
To motivate our use of persistent homology to study frequency content of signals, we establish explicit links between features of persistence diagrams, like cardinality and spatial distributions of points,
to those of the Fourier series of deterministic signals, specifically thelocation of peaks and their relative powers. The topological Bayesian approach allows for quantification of these cardinality and spatial

P127
A quantification of cross‑frequency coupling via topologicalmethods
Alan ­Cherne1, Christopher ­Oballe1, David ­Boothe2, Melvin F­ elton2, Piotr­Franaszczuk2, Vasileos ­Maroulas1
1
University of Tennessee, Department of Mathematics, Knoxville,
United States of America; 2U.S. Army Combat Capabilities Development
Command, United States of America
Correspondence: David Boothe (david.l.boothe7.civ@mail.mil)
BMC Neuroscience 2020, 21(Suppl 1):P127
A key feature in electroencephalograms (EEG) is the existence of distinct oscillatory components – theta (4-7 Hz), alpha (8-13Hz), beta
(14-30Hz), and gamma (40-100Hz). Cross frequency coupling has
been observed between these frequency bands in both the local fieldpotential (LFP) and electroencephalogram (EEG). While the association between activity in distinct oscillatory frequencies and important
brain functions is well established, the functional role of cross frequency coupling is poorly characterized, but has been hypothesized
to underlie cortical functions like working memory, learning, and computation [1,2].
The most common form of cross frequency coupling observed in brain
activity recordings is the modulation of the amplitude of a higher frequency oscillation by the phase of a lower frequency oscillation, a phenomenon known as phase-amplitude coupling (PAC). We present a
method for detecting PAC in signals that avoids some pitfalls in existing methods and combines techniques developed in the field of topological data analysis (TDA). When analyzing data using TDA, an object
called a persistence diagram, is commonly constructed. In the case oftime series the persistence diagram that is generated represents compactly all the peaks and valleys that occur in the signal. We inspect thepersistence diagrams to detect the presence of phase-amplitude coupling using the intuition that PAC will impart asymmetry to the upperand lower segments of the diagram. This representation of the data
has the advantage that it does not require the choice of Fourier analysis parameters, binning sizes, and phase estimations that are necessary in current methods [3].
We test the performance of our metric on two kinds of synthetic signals, the first is a phenomenological model with varying levels ofphase-amplitude coupling [4] as defined by the Kullback-Liebler
divergence from the uniform case of signals with no PAC. The second
is from simulated single cell neuronal data based on a layer 5 pyramidal cell [5,6]. Finally, we benchmark this method against methods
explored previously [4] in EEG data recorded from human subjects.

﻿BMC Neurosci 2020, 21(Suppl 1):54

References
1. VanRullen R, Koch C. Is perception discrete or continuous?. Trends in cogni‑
tive sciences. 2003; 7(5): 207-13.
2. Canolty RT, Knight RT. The functional role of cross-frequency coupling.
Trends in Cognitive Sciences. 2010; 14(11), 507-515.
3. Cohen MX. Assesing transient cross-frequency coupling in EEG data. Jour‑nal of Neuroscience Methods. 2008; 168: 494-499.
4. Tort ABL, Komorowski R, Eichenbaum H, Kopell N. Measuring PhaseAmplitude Coupling Between Neural Oscillations of Different Frequencies.
Journal of Neurophysiology. 2010; 104: 1195-210.
5. Felton Jr MA, Yu AB, Boothe DL, Oie KS, Franaszczuk PJ. Resonance analysis
as a tool for characterizing functional division of layer 5 pyramidal neurons.
Frontiers in Computational Neuroscience. 2018; 12:29.
6. Traub RD, et al. Single-column thalamocortical network model exhibiting
gamma oscillations, sleep spindles, and epileptogenic bursts. Journal of
Neurophysiology. 2005; 93(4): 2194-232.

P128
An integrate‑and‑fire model of narrow band modulation in mouse
visual cortex
Nicolò ­Meneghetti1,2, Alberto ­Mazzoni1,21
The Biorobotics Institute, Scuola Superiore Sant’Anna, Pisa, Italy;
2
Department of Excellence for Robotics and AI, Scuola SuperioreSant’Anna, Pisa, Italy
Correspondence: Nicolò Meneghetti (nicolo.meneghetti@santannapisa.
it)
BMC Neuroscience 2020, 21(Suppl 1):P128
Gamma band neuronal oscillations are involved with sensory processing ubiquitously in the central nervous system. They emerge from thecoordinated interaction of excitation and inhibition and are a biological marker of local active network computations [1]. Visual features ascontrast and orientation are known to modulate broad band gamma
activity in the primary visual cortex (V1) of primates [2]. In mouse V1,
however, a narrow band within gamma oscillation was found to display specific functional sensitivity to visual features [3].
Here we present a network of recurrent excitatory-inhibitory spiking
neurons reproducing the gamma narrow band dynamics in mouse
V1 observed in [3], building on previous works of our group [4,5]. By
combining experimental data analysis and simulations, we show thata proper design of the simulated thalamic input results in the network
to exhibit both narrow and broad band gamma activity.
We reproduced the spectral and temporal modulations of V1 local
field potentials of awake mice presented with gratings of different
contrast levels by approximating the thalamic input rate with two linear functions defined over complementary contrast ranges. We propose a theoretical framework in which the external thalamic drive is
responsible for inducing the emergence of broad by triggering corticalresonances and narrow band gamma activity by inducing entrainment
to an oscillatory drive. Our results support in particular the hypothesisof a subcortical origin of the narrow gamma band [3].
Our network provides a simple and effective model of contrastinduced gamma activity in rodents V1. The model could be easily
extended to reproduce the modulation of V1 gamma activity inducedby other visual stimulus features. Moreover, the model could help toinvestigate network dynamics responsible for pathological dysfunctions of physiological visual information processing in mice.
References
1. Buzsáki G, Wang XJ. Mechanisms of gamma oscillations. Annual Review of
Neuroscience. 2012; 35: 203-25.
2. Henrie JA, Shapley R. LFP power spectra in V1 cortex: the graded effect ofstimulus contrast. Journal of Neurophysiology. 2005; 94(1): 479-90.
3. Saleem AB, et al. Subcortical source and modulation of the narrowband
gamma oscillation in mouse visual cortex. Neuron. 2017; 93(2): 315-22.
4. Mazzoni A, Brunel N, Cavallari S, Logothetis NK, Panzeri S. Cortical dynamics
during naturalistic sensory stimulations: experiments and models. Journalof Physiology-Paris. 2011; 105(1-3): 2-15.
5. Mazzoni A, Linden H, Cuntz H, Lansner A, Panzeri S, Einevoll GT. Computing
the local field potential (LFP) from integrate-and-fire network models. PLoS
Computational Biology. 2015; 11(12): e1004584.

Page 74 of 123

P129
Correlated inputs to the striatum generate beta
over‑synchronization in in silico cortico‑basal ganglia network
Elena ­Manferlotti1, Matteo ­Vissani2, Arvind K­ umar3, Alberto ­Mazzoni2
1
Scuola Superiore Sant’Anna, The Biorobotics Institute and Department
of Excellence for Robotics and AI, Pisa, Italy; 2Scuola Superiore Sant’Anna
Pisa, The Biorobotic institute, Pisa, Italy; 3KTH Royal Institute of Technology,
Computational Brain Science, Stockholm, Sweden
Correspondence: Elena Manferlotti (elena.manferlotti@gmail.com)
BMC Neuroscience 2020, 21(Suppl 1):P129
Parkinson’s Disease (PD) is known to be associated with over-synchronized oscillations in the beta frequency range (13-35Hz) in motor cortex and basal ganglia (BG) [1]. Although the mechanisms underlying
the emergence of these oscillations are poorly understood, several
excitatory-inhibitory loops have been identified in the cortex-BG networks that might initiate or generate them.
Recent experimental data suggests that striatal spiny projection neurons (SPNs) are phase locked to the beta oscillation cycles [2]. Indeed,transient change in the SPNs firing rate is sufficient to unleash oscillations into the mutually connected globus pallidus externus (GPe) subthalamic nucleus (STN) network [3].
Here, we investigate the effect of temporal synchrony of SPNs activity on beta oscillations simulating a biologically plausible BG modelwith spiking neurons [4]. The likely source of correlations in the SPNs isthalamo-cortical input, since striatal connectivity is too sparse. Therefore, we injected correlated inputs to the SPNs. Our model showed
the emergence of beta band aberrant synchronization as the network
switches from the uncorrelated to the correlated input. Crucially,
inputs displayed a fixed firing rate, that is the over-synchronization
emerged only because of the input synchrony. Furthermore, increased
input correlation resulted in enhanced Globus Pallidus internus (GPi)firing rate as observed experimentally [5]. Next, we investigated thepossible consequences of these results for Deep Brain Stimulation(DBS) simulating high frequency injections into the STN. Our preliminary results showed that even a short window of stimulation wasenough to reduce beta oscillations in the firing rate of STN, GPe andGPi nuclei.
Our study provides innovative observations about the origin andpropagation of PD-related beta oscillations in the BG and their reduction due to DBS. It paves the way toward in silico testing of DBS
parameters that could be used to determine optimal parameters of
stimulation offline rather than during surgical implants.
References
1. Oswal A, Brown P, Litvak V. Synchronized neural oscillations and the patho‑
physiology of Parkinson’s disease. Current Opinion in Neurology. 2013; 26:
662–70.
2. Sharott A, et al. A Population of Indirect Pathway Striatal Projection Neurons
Is Selectively Entrained to Parkinsonian Beta Oscillations. Journal of Neuro‑science. 2017; 37: 9977–98.
3. Mirzaei A, et al. Sensorimotor processing in the basal ganglia leads totransient beta oscillations during behavior. Journal of Neuroscience. 2017;37(46): 11220-32.
4. Lindahl M, Hellgren Kotaleski J. Untangling Basal Ganglia Network Dynam‑
ics and Function: Role of Dopamine Depletion and Inhibition Investigatedin a Spiking Network Model. eNeuro. 2016; 3.
5. Magnin M, Morel A, Jeanmonod D. Single-unit analysis of the pallidum,
thalamus and subthalamic nucleus in parkinsonian patients. Neuroscience.
2000; 96: 549–64.

P130
Unifying information theory and machine learning in a modelof cochlear implant electrode discrimination
Xiao ­Gao1, David ­Grayden1, Mark M­ cDonnell2
1
University of Melbourne, Department of Biomedical Engineering,
Melbourne, Australia; 2University of South Australia, School of Information
Technology and Mathematical Sciences, Adelaide, Australia
Correspondence: Xiao Gao (xiao.gao@unimelb.edu.au)
BMC Neuroscience 2020, 21(Suppl 1):P130

﻿BMC Neurosci 2020, 21(Suppl 1):54

Despite the success of cochlear implants (CIs) over more than three
decades, wide inter-subject variability in speech perception isreported [1]. The key factors that cause variability between users areunclear. We previously developed an information theoretic modelling framework that enables estimation of the optimal number ofelectrodes and quantification of electrode discrimination ability [2,3].
However, the optimal number of electrodes was estimated based
only on statistical correlations between channel outputs and inputs,
and the model did not quantitatively model psychophysical measurements and study inter-subject variability.
Here, we unified information theoretic and machine learning techniques to investigate the key factors that may limit the performanceof CIs. The framework used a neural network classifier to predict whichelectrode was stimulated for a given simulated activation patternof the auditory nerve, and mutual information was then estimatedbetween the actual stimulated electrode and the predicted one.
Using the framework, electrode discrimination was quantified witha range of parameter choices, as shown in Figure 1. The columns
from left to right show how the distance between electrodes andauditory nerve fibres, r, the number of surviving fibres, N, the maximum current level (modelled as the percentage of surviving fibres,N, that generate action potentials for a given stimulated electrode),and the attenuation in electrode current, A, affect the model performance, respectively. The parameters were chosen to reflect the key
factors that are believed to limit the performance of CIs. The modelshows sensitivity to parameter choices, where smaller r, larger N, andhigher attenuation in current lead to higher mutual information andimproved classification.

Fig. 1 Model performance with a range of parameter choices
This approach provides a flexible framework that may be used toinvestigate the key factors that limit the performance of cochlear
implants. We aim to investigate its application to personalised configurations of CIs.
Acknowledgments: This work is supported by a McKenzie Fellowship,
The University of Melbourne.
References
1. Holden LK, et al. Factors affecting open-set word recognition in adults withcochlear implants. Ear and hearing. 2013; 34(3): 342.
2. Gao X, Grayden DB, McDonnell MD. Stochastic information transfer fromcochlear implant electrodes to auditory nerve fibers. Physical Review E.
2014; 90(2): 022722.
3. Gao X, Grayden DB, McDonnell MD. Modeling electrode place discrimi‑
nation in cochlear implant stimulation. IEEE Transactions on Biomedical
Engineering. 2016; 64(9): 2219-29.

Page 75 of 123

P131
Unsupervised metadata tagging of computational neuroscience
literature, towards question answering
Evan ­Cudone1, Robert ­McDougal2
1
Yale University, Computational Biology and Bioinformatics, New Haven,
Connecticut, United States of America; 2Yale University, Departmentof Neuroscience, New Haven, Connecticut, United States of America
Correspondence: Evan Cudone (evan.cudone@yale.edu)
BMC Neuroscience 2020, 21(Suppl 1):P131
Curation and knowledge dissemination of the computational neuroscience field requires many unique considerations as it utilizes
language, methods, and ideas from diverse areas including biology,
chemistry, physics, mathematics, medicine, and computer science.
In order to effectively facilitate curation and knowledge dissemination for the computational neuroscience community we must firstdevelop a robust representation of its existing literature. Using unsupervised topic modeling approaches, a metadata tagging schema wasdeveloped for computational neuroscience literature from ModelDB(a repository of computational neuroscience models), and comparedto that of a larger neuroscience corpus. This analysis shows key differences in the types of discoveries and knowledge addressed in neuroscience and its computational subdiscipline, and gives insight into
how an automated question answering system might differ between
the two.
P132
Preventing retinal ganglion cell axon bundle activation
with oriented rectangular electrodes
Wei ­Tong1, Michael R I­bbotson2, Hamish M­ effin3
1
National Vision Research Institute, Melbourne, Australia; 2AustralianCollege of Optometry, The National Vision Research Institute, Melbourne,
Australia; 3University of Melbourne, Biomedical Engineering, Melbourne,
Australia
Correspondence: Wei Tong (wei.tong@unimelb.edu.au)
BMC Neuroscience 2020, 21(Suppl 1):P132
Retinal prostheses can restore visual sensations in people that havelost their photoreceptors by electrically stimulating surviving retinal
ganglion cells (RGCs). Currently, there are mainly three types of retinal
prostheses under development, based on their implantation locations:
epi-retinal, sub-retinal and suprachoroidal [1]. Clinical studies from all
three types of devices indicate that, although a sense of vision can berestored, the visual acuity obtained is limited and functional vision,
such as navigation and facial recognition remains challenging. One
major difficulty is associated with the low spatial resolution obtained
from electrical stimulation, i.e. the large spread of activation amongst
RGCs leads to blurred or distorted visual percepts. Particularly, with
epi-retinal implants, experiments have revealed that the leading causeof widespread activation is the unintended activation of passing RGC
axons, which lead to elongated phosphines in patients [2].
This work proposes to use rectangular electrodes oriented parallel tothe axon bundles to prevent the activation of passing axon bundles.
Here, we first used simulation to investigate the interaction of neuraltissue orientation and stimulation electrode configuration on the RGC
activation patterns. A four-layer computational model of epiretinal
extracellular stimulation that captures the effect of neurite orientationin anisotropic tissue was applied, as previously described [3], using a
volume conductor model known as the cellular composite model. As
shown in Figure 1a, our model shows that stimulating with rectangular electrode aligned with the nerve fiber layer (i.e. passing axon
bundles), can be used to achieve selective activation of axon initial
segments, rather than passing fibers.

﻿BMC Neurosci 2020, 21(Suppl 1):54

Fig. 1 Simulated membrane potentials a and experimental calcium
imaging b results both indicate that a rectangular electrode oriented
parallel to the axon bundles can lead to localised RGC activation by
avoiding the unintended activation of passing axon bundles
The simulation results were then confirmed with experiments. Here,data were acquired from adult Long Evan rats by recording the
response of RGCs from whole-mount retina preparations using calcium imaging. Electrical stimulation was delivered through a diamondcoated carbon fiber electrode with a length of 200µm and diameterof 10µm. The electrode was placed either parallel or perpendicular to
the RGC axon bundles. Biphasic stimuli with different pulse durationsof 33-500µs were tested. Our experimental observations (Fig. 1b) areconsistent with the expectations of the simulations, and the use of rectangular electrodes placed parallel to axon bundles can significantly
reduce the activation of RGC axon bundles. When using biphasic
stimulation as short as 33µs, the activated RGCs were mostly confinedto the region below or very close-to the electrode, as observed using
confocal microscopy.
To conclude, this work provides a stimulation strategy for reducing the
spread of RGC activation for epi-retinal prostheses. Using ultrashortpulses together with rectangular electrodes parallel to the RGC axon
bundles, the performance of epi-retinal prostheses will be improved
significantly, thus promising to restore a higher quality of vision to theblind.
References
1. Weiland JD, Walston ST, Humayun MS. Electrical stimulation of the retina to
produce artificial vision. Annual Review of Vision Science. 2016; 2: 273-94.
2. Nanduri D, et al. Frequency and amplitude modulation have different
effects on the percepts elicited by retinal stimulation. Investigative ophthal‑mology & visual science. 2012; 53(1): 205-14.
3. Esler TB, et al. Minimizing activation of overlying axons with epiretinal
stimulation: The role of fiber orientation and electrode configuration. PloS
one. 2018; 13(3): e0193598.

P133
Recurrent neural networks trained in multisensory integration tasks
reveal a diversity of selectivity and structural properties
Amparo ­Gilhuis1, Shirin ­Dora2, Cyriel P­ ennartz1, Jorge M­ ejias1
1
University of Amsterdam, Swammerdam Institute for Life Sciences,
Amsterdam, Netherlands; 2Ulster University, Intelligent Systems ResearchCentre, Londonderry, United Kingdom
Correspondence: Jorge Mejias (jorge.f.mejias@gmail.com)
BMC Neuroscience 2020, 21(Suppl 1):P133
The brain continuously processes sensory information from multiple
modalities, giving rise to internal representations of the outside world.
If and how the information from multiple modalities is being integrated has extensively been investigated over the past years, leading

Page 76 of 123

to more insight in multisensory integration (MSI) and its underlying
mechanisms [1]. However, the different experimental paradigms used
to investigate MSI involve different cognitive resources and situational
demands. In this study, we investigated how different experimental
paradigms of MSI reflect on behavior output and in their corresponding neural activity patterns. We did so by designing a recurrent neuralnetwork (RNN) with the biological plausible feature of differentiating
between excitatory and inhibitory units [2]. For each of the three multisensory processing tasks considered [3,4], an RNN was optimized toperform the tasks with similar performance as found in animals. Network models trained on different experimental paradigms showedsignificant distinct selectivity and connectivity patterns. Selectivity
for both modality and choice was found in network models that weretrained on the paradigm that involved higher cognitive resources.
Network models trained on paradigms that involve more bottomup processes mostly experienced choice selectivity. Increasing thelevel of network noise in network models that at first did not experience modality selectivity led to an increase in modality selectivity. We
propose that a higher range of selectivity arises when a task is moredemanding, either due to higher network noise (which makes the taskharder for the animal) or a more difficult experimental paradigm. The
higher range of selectivity is thought to improve the flexibility of the
network model, which could be a necessity for the network models
to achieve good performance, and the resulting neural heterogeneity could be used for more general information processing strategies[5,6].
Acknowledgements: This work was partially supported by theEuropean Union’s Horizon 2020 Framework Programme for
Research and Innovation under the Specific Grant Agreement No.
785907.
References
1. Chandrasekaran C. Computational principles and models of multisensory
integration. Current Opinion in Neurobiology. 2017; 43: 25-34.
2. Song HF, Yang GR, Wang XJ. Training excitatory-inhibitory recurrent neuralnetworks for cognitive tasks: a simple and flexible framework. PLoS Compu‑
tational Biology. 2016; 12(2): e1004792.
3. Raposo D, Kaufman MT, Churchland AK. A category-free neural population
supports evolving demands during decision-making. Nature Neuroscience.
2014; 17: 1784.
4. Meijer GT, Pie JL, Dolman TL, Pennartz C, Lansink CS. Audiovisual integration
enhances stimulus detection performance in mice. Frontiers in Behavioral
Neuroscience. 2018; 12: 231.
5. Mejias JF, Longtin A. Optimal heterogeneity for coding in spiking neuralnetworks. Physical Review Letters. 2012; 108(22): 228102.
6. Mejias JF, Longtin A. Differential effects of excitatory and inhibitory hetero‑geneity on the gain and asynchronous state of sparse cortical networks.
Frontiers in Computational Neuroscience. 2014; 8: 107.

P134
A fluid hierarchy in multisensory integration propertiesof large‑scale cortical networks
Ronaldo ­Nunes1, Marcelo R­ eyes1, Raphael de C
­ amargo1, Jorge ­Mejias2
1
Universidade Federal do ABC, Center for Mathematics, Computing
and Cognition, Santo Andre, Brazil; 2University of Amsterdam,
Swammerdam Institute for Life Sciences, Amsterdam, Netherlands
Correspondence: Jorge Mejias (jorge.f.mejias@gmail.com)
BMC Neuroscience 2020, 21(Suppl 1):P134
A fundamental ingredient for perception is the integration of information from different sensory modalities. This process, known as multisensory integration (MSI), has been studied extensively using animal
and computational models [1]. It is not yet clear, however, how different brain areas contribute to MSI, and identifying relevant areas
remains challenging. Part of the reason is that simultaneous electrophysiological recordings from different brain areas has developed only
recently [2], and the intensity, noise profile and delay responses arediverse for different sensory signals [1]. Furthermore, computationalmodels have traditionally focused only on a few areas, a limitation

﻿BMC Neurosci 2020, 21(Suppl 1):54

imposed by the lack of reliable anatomical data on brain networks.
We present here a theoretical and computational study of the mechanisms underlying MSI in the mouse brain, by constraining our modelwith a recently acquired anatomical brain connectivity dataset [3].
Our simulations of the resulting large-scale cortical network reveal
the existence of a hierarchy of crossmodal excitability properties, withareas at the top of the hierarchy being the best candidates for integrating information from multiple modalities. Furthermore, our model
predicts that the position of a given area in such hierarchy is highly
fluid and depends on the strength of the sensory input received by the
network. For example, we observe that the particular set of areas integrating visuotactile stimuli changes depending on the level of visual
contrast. By simulating a simplified network model and developing its
corresponding mean-field approximation, we determine that the origin of such hierarchical dynamics is the structural heterogeneity of the
network, which is a salient property of cortical networks [3,4]. Finally,
we extend our results to macaque cortical networks [5] to show thatthe hierarchy of crossmodal excitability is also present in other mammals, and we characterize how frequency-specific interactions areaffected by hierarchical dynamics and define functional connectivity[6]. Our work provides a compelling explanation as of why is it notpossible to identify unique MSI areas even for a well-defined multisensory task, and suggests that MSI circuits are highly context-dependent.
Acknowledgements: This study was financed in part by the Universityof Amsterdam and the Coordenacao de Aperfeicoamento de Pessoal
de Nivel Superior Brasil (Capes) - Finance Code 001.
References
1. Chandrasekaran C. Computational principles and models of multisensory
integration. Current Opinion in Neurobiology. 2017; 43: 25-34.
2. Hong G, Lieber CM. Novel electrode technologies for neural recordings.
Nature Reviews Neuroscience. 2019; 20(6): 330-45.
3. Gamanut R, et al. The mouse cortical connectome, characterized by an
ultra-dense cortical graph, maintains specificity by distinct connectivity
profiles. Neuron. 2018; 97: 698-715.
4. Mejias JF, Longtin A. Differential effects of excitatory and inhibitory hetero‑geneity on the gain and asynchronous state of sparse cortical networks.
Frontiers in Computational Neuroscience. 2014; 8: 107.
5. Mejias JF, Murray JD, Kennedy H, Wang XJ. Feedforward and feedback
frequency-dependent interactions in a large-scale laminar network of the
primate cortex. Science Advances. 2016; 2(11): e1601335.
6. Nunes RV, Reyes MB, De Camargo RY. Evaluation of connectivity estimatesusing spiking neuronal network models. Biological Cybernetics. 2019;113(3): 309-20.

P135
A cortical model examining mismatch negativity deficits
in schizophrenia
Gili ­Karni1, Christoph ­Metzner2
1
Minerva at KGI, San Francisco, California, United States of America;
2
Technische Universität Berlin, Department of Software Engineeringand Theoretical Computer Science, Berlin, Germany
Correspondence: Christoph Metzner (cmetzner@ni.tu‑berlin.de)
BMC Neuroscience 2020, 21(Suppl 1):P135
Recent advances in computational modeling, genome-wide association studies, neuroimaging, and theoretical neuroscience pose better
opportunities to study neuropsychiatric disorders, such as schizophrenia (SZC) [1]. However, despite a repeated examination of its well-characterized phenotypes, our understanding of SZC’s neurophysiologicalbiomarkers or cortical dynamics remain elusive.
This study presents a biophysical spiking neuron model of perceptual
inference, based on the predictive coding framework [2]. The model,
implemented in NetPyNE [3], incorporates various single-cell models
of both excitatory and inhibitory neurons [4,5], mimicking the circuitsof the primary auditory cortex. This model allows for the exploration of
the effects bio-genetic variants (expressed via ion-channels or synaptic
mechanism alterations, see [6]) have on auditory mismatch negativity(MMN) deficits, a common biomarker for SZC [7]. More particularly, themodel distinguishes between repetition suppression and prediction
error and examines their respective contribution to the MMN. The first

Page 77 of 123

part of this report establishes the model’s explanatory power using
two well-known paradigms: the oddball paradigm and the cascade
paradigm. Both can reproduce the electrophysiological measures ofthe MMN among healthy subjects. Later, via tuning the parameters of
single-neuron equations or the network’s synaptic weights, the model
exhibits the expected LFP changes associated with SZC [8].
Therefore, this model enables exploring how biogenetic alterations
affect the underlying components of the observed MMN deficits.
Novel, yet preliminary, predictions are presented and suggested
future steps for validations are listed. This model could support studies
exploring genetic effects on the MMN (or other aspects of predictive
coding) in the auditory cortex.
References
1. Krystal JH, et al. Computational psychiatry and the challenge of schizophre‑nia. 2017.
2. Bastos AM, et al. Canonical microcircuits for predictive coding. Neuron.
2012; 76(4): 695-711.
3. Garrido MI, Kilner JM, Stephan KE, Friston KJ. The mismatch negativity: areview of underlying mechanisms. Clinical Neurophysiology. 2009; 120(3):453-63.
4. Beeman, D. Comparison with human layer 2/3 pyramidal cell dendritic
morphologies. Poster session presented at the meeting of Society for
Neuroscience 2018, San Diego.
5. Vierling-Claassen D, Cardin J, Moore CI, Jones SR. Computational modelingof distinct neocortical oscillations driven by cell-type selective optogenetic
drive: separable resonant circuits controlled by low-threshold spiking andfast-spiking interneurons. Frontiers in Human Neuroscience. 2010 Nov; 4:198.
6. Mäki-Marttunen T, et al. Functional effects of schizophrenia-linked genetic
variants on intrinsic single-neuron excitability: a modeling study. BiologicalPsychiatry: Cognitive Neuroscience and Neuroimaging. 2016; 1(1): 49-59.
7. Dura-Bernal S, et al. NetPyNE, a tool for data-driven multiscale modeling of
brain circuits. Elife. 2019; 8: e44494.
8. Michie PT, Malmierca MS, Harms L, Todd J. The neurobiology of MMN andimplications for schizophrenia. Biological psychology. 2016; 116: 90-7.

P136
Effect of independent noise on the synchronization of interacting
excitatory‑inhibitory networks
Lucas ­Rebscher1, Christoph ­Metzner2
1
Technische Universität Berlin, Neural Information Processing, Berlin,
Germany; 2Technische Universität Berlin, Department of Software
Engineering and Theoretical Computer Science, Berlin, Germany
Correspondence: Lucas Rebscher (lucas.rebscher@campus.tu‑berlin.de)
BMC Neuroscience 2020, 21(Suppl 1):P136
Gamma rhythms play a major role in different processes in the brain,such as attention, working memory and sensory processing. The communication-through-coherence (CTC) hypothesis [1,2] suggests thatsynchronization in the gamma band is one of the key mechanisms inneuronal communication and counterintuitively noise can have beneficial effects on the communication [3].
Recently, Meng et al. [4] showed that synchronization across interacting networks of inhibitory neurons increases while synchronization
within these networks decreases when neurons are subject to independent noise. They focused on inhibitory-inhibitory connections
with gamma band activity produced by the interneuronal network
gamma mechanism (ING). However, experimental and modeling studies [5] point towards an important role of the pyramidal-interneuronal
network gamma (PING) mechanism in the cortex and the establishedview is that cortico-cortical connections are predominately excitatory[6].
We build up on Meng et al. [4] results and intend to verify if their findings can be observed in interacting gamma rhythms produced by a
PING mechanism. In our ongoing research we model interacting excitatory-inhibitory networks and analyze how synchronization changes
depending on strength and correlation of noise in different network
settings. We expect to see the same effect in our model as (1) the delayof spiking by inhibition is integral to both ING and PING and (2) Meng﻿BMC Neurosci 2020, 21(Suppl 1):54

et al. [4] replicated the effect in different neuron models as well as
relaxation oscillators.
Uncovering whether, and if yes, under which conditions, stochastic
fluctuations can also have beneficial effects on gamma oscillations
produced by a PING mechanism, would further our understanding ofinter-regional communication. However, importantly, it might also
yield mechanistic explanations for altered neuronal dynamics in psychiatric disorders, since for example, disturbances in neuronal oscillations in the gamma band, especially reduced synchronization, are a
key finding in schizophrenia [7].
References
1. Fries, P. A mechanism for cognitive dynamics: neuronal communication
through neuronal coherence. Trends in Cognitive Sciences. 2005; 9(10):474-480.
2. Fries P. Rhythms for cognition: communication through coherence. Neuron.
2015, 88(1), 220-235.
3. McDonnell MD, Ward LM. The benefits of noise in neural systems: bridging
theory and experiment. Nature Reviews Neuroscience. 2011; 12(7): 415-425.
4. Meng JH, Riecke H. Synchronization by uncorrelated noise: interacting
rhythms in interconnected oscillator networks. Scientific Reports. 2018; 8(1):
1-4.
5. Tiesinga P, Sejnowski TJ. Cortical enlightenment: are attentional gamma
oscillations driven by ING or PING?. Neuron. 2009; 63(6): 727-732.
6. Lodato S, Arlotta P. Generating neuronal diversity in the mammalian cer‑ebral cortex. Annual Review of Cell and Developmental Biology. 2015; 31:
699-720.
7. Uhlhaas P, Singer W. Oscillations and neuronal dynamics in schizophrenia:
the search for basic symptoms and translational opportunities. BiologicalPsychiatry. 2015; 77(12): 1001-1009.

P137
General anesthesia reduces complexity and temporal asymmetryof the informational structures derived from neural recordings
in Drosophila
Roberto ­Munoz1, Angus L­ eung2, Aidan Z­ ecevik1, Felix ­Pollock1, Dror
­Cohen3, Bruno can S­ winderen4, Naotsugu ­Tsuchiya5, Kavan M­ odi1
1
Monash University, School of Physics and Astronomy, Melbourne, Australia;
2
Monash University, School of Psychological Sciences, Melbourne, Australia;
3
National Institute of Information and Communications Technology,
Osaka, Japan; 4The University of Queensland, St Lucia, Australia; 5Monash
University, School of Psychological Sciences and Turner Institute for Brainand Mental Health, Melbourne, Australia
Correspondence: Roberto Munoz (roberto.munoz@monash.edu)
BMC Neuroscience 2020, 21(Suppl 1):P137
We apply techniques from the field of computational mechanics
to evaluate the statistical complexity of neural recording data fromfruit flies. First, we connect statistical complexity to the flies’ level ofconscious arousal, which is manipulated by general anaesthesia (isoflurane). We show that the complexity of even single channel timeseries data decreases under anaesthesia. The observed difference incomplexity between the two states of conscious arousal increases as
higher orders of temporal correlations are taken into account. We thengo on to show that, in addition to reducing complexity, anaesthesia
also modulates the informational structure between the forward andreverse-time neural signals. Specifically, using three distinct notionsof temporal asymmetry we show that anaesthesia reduces temporal asymmetry on information-theoretic and information-geometricgrounds. In contrast to prior work, our results show that: (1) Complexity differences can emerge at very short time scales and across broad
regions of the fly brain, thus heralding the macroscopic state ofanaesthesia in a previously unforeseen manner, and (2) that generalanaesthesia also modulates the temporal asymmetry of neural signals.
Together, our results demonstrate that anaesthetised brains become
both less structured and more reversible.

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P138
Processing Capacity of recurrent spiking networks
Tobias Schulte to ­Brinke1, Fahad K­ halid2, Renato ­Duarte3, Abigail­Morrison1
1
Forschungszentrum Jülich, Jülich, Germany; 2Forschungszentrum Jülich,
Institute for Advanced Simulation and Jülich Supercomputing Centre,
Jülich, Germany; 3Jülich Research Center, Institute of Neuroscienceand Medicine (INM‑6) and Institute for Advanced Simulation (IAS‑6), Jülich,
Germany
Correspondence: Tobias Schulte to Brinke (t.schulte.to.brinke@fz‑juelich.
de)
BMC Neuroscience 2020, 21(Suppl 1):P138
One of the most prevalent characteristics of neurobiological systems
is the abundance of recurrent connectivity. Regardless of the spatial
scale considered, recurrence is a fundamental design principle and acore anatomical feature, permeating the micro-, meso- and macroscopic levels. In essence, the brain (and, in particular, the mammalian
neocortex) can be seen as a large recurrent network of recurrent networks. Despite the ubiquity of these observations, it remains unclear
whether recurrence and the characteristics of its biophysical properties correspond to important functional specializations and if so, towhat extent.
Intuitively, from a computational perspective, recurrence allows information to be propagated in time, i.e. past information reverberates so
as to influence online processing, endowing the circuits with memoryand sensitivity to temporal structure. However, even in its simpler formulations, the functional relevance and computational consequencesof recurrence in biophysical models of spiking networks are not clear
or unambiguous and its effects vary depending on the type and characteristics of the system under analysis and the nature of the computational task. Therefore, it would be extremely useful, from bothan engineering and a neurobiological perspective, to know to what
extent is recurrence necessary for neural computation.
In this work, we set out to quantify the extent to which recurrence
modulates a circuit’s computational capacity, by systematically measuring its ability to perform arbitrary transformations on an input,
following [1]. By varying the strength and density of recurrent connections in balanced networks of spiking neurons, we evaluate the effectof recurrence on the complexity of the transformations the circuit can
carry out and on the memory it is able to sustain. Preliminary results
demonstrates some constraints on recurrent connectivity that optimize its processing capabilities for mappings that involve both linear
memory and varying degrees of nonlinearity.
Additionally, given that the metric we employ is particularly computationally-heavy (evaluating the system’s capacity to represent thousands of target functions), a careful optimization and parallelization
strategy is employed, enabling its application to networks of neuroscientific interest. We present a highly scalable and computationally efficient software, which pre-computes the thousands of necessary target
polynomial functions for each point in a large combinatorial space,accesses these target functions through an efficient lookup operation, caches functions that need to be called multiple times with thesame inputs and optimizes the most compute-intensive hotspots with
Cython. In combination with MPI for internode communication thisresults in a highly scalable and computationally efficient implementation to determine the processing capacity of a dynamical system.
Acknowledgments: The authors gratefully acknowledge the computing time granted by the JARA Vergabegremium and provided on the
JARA Partition part of the supercomputer JURECA at Forschungszentrum Jülich.
Reference
1. Dambre J, Verstraeten D, Schrauwen B, Massar S. Information Processing
Capacity of Dynamical Systems. Scientific Reports. 2012; 2: 514.

﻿BMC Neurosci 2020, 21(Suppl 1):54

P139
Stereotyped population dynamics in the medial entorhinal cortex
Soledad G C­ ogno1, Flavio ­Donato2, Horst A ­Obenhaus1, Irene R J­ acobsen1,­ oser1
May‑Britt ­Moser1, Edvard I M
1
NTNU, Kavli Institute for Systems Neuroscience and Centre for NeuralComputation, Trondheim, Norway; 2University of Basel, Biozentrum, Basel,
Switzerland
Correspondence: Soledad G Cogno (soledad.g.cogno@ntnu.no)
BMC Neuroscience 2020, 21(Suppl 1):P139
The medial entorhinal cortex (MEC) supports the brain’s representation of space with distinct cell types (grid, border, object-vector,
head-directions and speed cells). Since no single sensory stimulus canfaithfully predict the firing of these cells, attractor network models
postulate that spatially-tuned firing emerges from specific connectivity motives. To determine how those motives constrain the self-organized activity in the MEC, we tested mice in a spontaneous locomotion
task under sensory-deprived conditions, when activity likely is determined by the intrinsic structure of the network. Using 2-photon calcium imaging, we monitored the activity of large populations of MEC
neurons in mice running on a wheel in darkness.
To reveal network dynamics we applied dimensionality reduction
techniques to the spike matrix. This way we unveiled the presenceof motifs that involve the sequential activation of neurons (“waves”).
Waves lasted from tens of seconds to minutes, swept through theentire network of active cells and did not exhibit any anatomicalorganization. Waves were not found in spike-time-shuffled data. Furthermore, waves did not map the position of the mouse on the wheel
and were not restricted to running epochs. Single neurons exhibiteda wide range of locking degrees to the waves, indicating that theobserved dynamics is a population effect rather than a single cell phenomenon. Overall, our results suggest that a large fraction of MEC-L2
neurons participates in common global dynamics that often takes theform of stereotyped waves. These activity patterns might couple theactivity of neurons with distinct tuning characteristics in MEC.
P140
How hard are NP‑complete problems for humans?
Pablo Franco, Karlo Doroc, Nitin Yadav, Peter Bossaerts, Carsten Murawski
University of Melbourne, Department of Finance, Melbourne, Australia
Correspondence: Pablo Franco (jfranco1@student.unimelb.edu.au)
BMC Neuroscience 2020, 21(Suppl 1):P140
It is widely accepted that humans have limited cognitive resources
and that these finite resources impose restrictions on what the brain
can compute. Although endowed with limited computational power,
humans are still presented daily with decisions that require solving complex problems. This raises a tension between computational
capacity and the computational requirements of solving a problem. In
order to understand how hardness of problems affect problem-solving ability we propose a measure to quantify the difficulty of problems
for humans. For this we make use of computational complexity theory,a widely studied theory used to quantify the hardness of problems
for electronic computers. It has been proposed that computational
complexity theory can be applied to humans, but it remains an open
empirical question whether this is the case.
We study how difficulty of problems affects decision quality in complex problems by studying a measure of expected difficulty over random instances (i.e. random cases) of a problem. This measure, which
we refer to as instance complexity (IC), quantifies the expected hardness of a decision problems; that is, problems that have a yes/noanswer. More specifically, this measure captures how constrained the
problem is, based on a small number of features of the instance. Overall, IC has three main advantages. Firstly, it is a well-studied measurethat has been proven to be applicable to a large range of problems forelectronic computers. Secondly, it allows calculation of expected hardness of a problem ex-ante, that is, before solving the problem. And
lastly, it captures complexity that is independent of a particular algorithm or model of computation. Thus, it is considered to characterize

Page 79 of 123

the inherent computational complexity of random instances, which is
independent of the system solving it.
In this study we test whether IC is a generalizable measure, forhumans, of the expected hardness of solving a problem. For this purpose, we ran a set of experiments in which human participants solved
a set of instances of one of three widely studied NP-Complete problems, namely the Traveling Salesperson, the Knapsack Problem or
Boolean Satisfiability. Instances varied in their IC. We show that participants expended more effort on instances with higher IC, but thatdecision quality was lower in those instances. Together, our results
suggest that IC can be used to measure the expected computational
requirements of solving random instances of a problem, based on an
instance’s features.
The findings of this study speak to the broader question of whether
there is a link between the computation model in humans and electronic computers. Specifically, this study gives evidence that the average hardness of random instances can be characterized via the same
set of parameters for both computing systems. This provides support
that computational complexity theory applies to humans. Moreover,
we argue that decision-makers could use IC to estimate the expected
costs of performing a task. One reason is that the estimation of IC
can be done without having to solve the problem. Furthermore, theresults of this study suggest that IC captures the hardness of a random
instance. Most importantly, our findings suggest that people modulate their effort according to IC. Altogether, this generates future avenues for research, based on IC, that could shed light into the cognitive
resource allocation process in the brain.
P141
A synthetic likelihood solution to the silent synapse estimation
problemMichael ­Lynn1, Jean‑Claude ­Beique1, Kevin ­Lee1, Cary S­ oares1, Richard­Naud2
1
University of Ottawa, Cellular and Molecular Medicine, Ottawa, Canada;
2
University of Ottawa, Cellular and Molecular Medicine; Departmentof Physics, Ottawa, Canada
Correspondence: Michael Lynn (mlynn101@uottawa.ca)
BMC Neuroscience 2020, 21(Suppl 1):P141
Functional features of populations of synapses are typically inferredfrom random electrophysiological sampling of small subsets of synapses. Are these samples unbiased? Here, we developed a biophysically constrained statistical framework for addressing this question
and applied it to assess the performance of a widely used method
based on a failure-rate analysis to quantify the occurrence of silent(AMPAR- lacking) synapses in neural networks. We simulated thismethod in silico and found that it is characterized by strong and systematic biases, poor reliability and weak statistical power. Key conclusions were validated by whole-cell recordings from hippocampal
neurons. To address these shortcomings, we developed a simulator of
the experimental protocol and used it to compute a synthetic likelihood. By maximizing the likelihood, we inferred silent synapse fraction
with no bias, low variance and superior statistical power over alternatives. Together, this generalizable approach highlights how a simulator of experimental methodologies can substantially improve theestimation of physiological properties.
P142
Using reinforcement learning to train biophysically detailed modelsof visual‑motor cortex to play Atari games
Haroon ­Anwar1, Salvador Dura‑Bernal1, Cliff C K­ err2, George L­Chadderdon3, William W ­Lytton4, Peter L­ akatos1, Samuel A ­Neymotin11Nathan Kline Institute for Psychiatric Research, Orangeburg, New York,
United States of America; 2University of Sydney, School of Physics, Sydney,
Australia; 3Burnet Institute, Melbourne, Australia; 4SUNY Downstate
Medical Center, Department of Physiology and Pharmacology, Brooklyn,
New York, United States of America
Correspondence: Haroon Anwar (haroon.anwar@nki.rfmh.org)
BMC Neuroscience 2020, 21(Suppl 1):P142

﻿BMC Neurosci 2020, 21(Suppl 1):54

Computational neuroscientists build biophysically detailed modelsof neurons and neural circuits primarily to understand the origin of
dynamics observed in experimental data. Much of these efforts are
dedicated to match ensemble activity of the neurons in the modeled brain region while often ignoring multimodal information flow
across brain regions and associated behaviors. Although these efforts
have led us to improved mechanistic understanding of electrophysiological behavior of diverse types of neurons and neural networks,
these approaches fall short of linking detailed models with associated behaviors in a closed-loop setting. In this study, we bridged thatgap by developing biophysically detailed multimodal models of brain
regions involved in processing visual information, generating motor
behaviors and making associations between visual and motor neuralrepresentations by deploying reward-based learning mechanisms. We
build a simple model of visual cortex receiving topological inputs fromthe interfaced Atari-game ‘pong’ environment (provided by the OpenAI’s Gym). This modeled region processed, integrated and relayed
visual information about the game environment across the hierarchyof higher order visual areas (V1/V2 -> V4 -> IT). As we moved from V1
to IT, the number of neurons in each area decreased whereas the synaptic connections increased. This feature was included in the model toreflect the anatomical convergence suggested in the literature and tohave a broader tuning for input features in progression up the visual
cortical hierarchy. We used compartmental models of both excitatory and inhibitory neurons interconnected via AMPA (for excitation)
or GABA (for inhibition) synapses. The strengths of synaptic connections were adjusted so that the information was reliably transmitted
across visual areas. In our motor cortex model, neurons associatedwith a particular motor action were grouped together and received
inputs from all visual areas. For the game Pong, we used two populations of motor neurons, for generating “up” and “down” move commands. All the synapses between visual and motor cortex were plastic,so that the connection strengths could be increased or decreased via
reinforcement learning. When an action was generated in the modelof motor cortex driven by visual representation of the environment in
the model of visual cortex, that action generated a move in the game,
which in turn updated the environment and triggered a response to
the action: reward (+1), punishment (-1) or no-response (0). Thesesignals drove the reinforcement learning at the synapses between
visual cortex and motor cortex by strengthening or weakening themso that the model could learn which actions were rewarding in a given
environment. Here we present an exploratory analysis as a proof-ofconcept for using biophysically detailed modeling of neural circuits tosolve problems that have so far only been tackled using artificial neural networks. We aim to use this framework to further simplify to makeit more deep-learning-like and also to extend the architecture to makeit biologically realistic. Comparing the performance of trained models
using different architectures will allow us to dissect the mechanisms
underlying production of behavior and will bridge the gap between
the electrophysiological dynamics of neural circuits and associatedbehaviors.
P143
Biophysically‑detailed multiscale model of macaque auditory
thalamocortical circuits reproduces physiological oscillations
Salvador Dura‑Bernal1, Erica Y G­ riffith1, Annamaria ­Barczak2, Monica N
O’Connell2, Tammy ­McGinnis2, Haroon A­ nwar3, William W ­Lytton1, Peter­Lakatos3, Samuel A ­Neymotin3
1
SUNY Downstate Medical Center, Department of Physiology
and Pharmacology, Brooklyn, New York, United States of America; 2Nathan
Kline Institute for Psychiatric Research, Center for Biomedical Imaging
and Neuromodulation, Orangeburg, New York, United States of America;
3
Nathan Kline Institute for Psychiatric Research, Orangeburg, New York,
United States of America
Correspondence: Salvador Dura‑Bernal (salvadordura@gmail.com)
BMC Neuroscience 2020, 21(Suppl 1):P143
We used the NEURON simulator with NetPyNE to develop a biophysically-detailed model of the macaque auditory thalamocortical system.
We simulated a cortical column with a cortical depth of 2000um andPage 80 of 123

200um diameter, containing over 12k neurons and 30M synapses.
Neuron densities, laminar locations, classes, morphology and biophysics, and connectivity at the long-range, local and dendritic scale were
derived from published experimental data (Fig. 1). We used the model
to investigate the mechanisms and function of neuronal oscillatory
patterns observed in the auditory system in electrophysiological data
recorded simultaneously from nonhuman primate primary auditory
cortex (A1) and the medial geniculate body (MGB), while the awake
subjects were presented with different classes of auditory stimuli,
including speech.

Fig. 1 Dimensions of simulated A1 column with overall laminar celldensities, layer boundaries, cell morphologies and distribution of
populations
The model A1 includes 6 cortical layers and multiple populationsof neurons consisting of 4 excitatory (intratelencephalic (IT), spiny
stellate (ITS), pyramidal-tract (PT), and corticothalamic (CT)), and 4
inhibitory types (somatostatin (SOM), parvalbumin (PV), vasoactive
intestinal peptide (VIP), and neurogliaform (NGF)). Cells were distributed across layers 2-6, except NGF cells which were also included in
L1, as these have been identified as important targets of the thalamic
matrix. The A1 model was reciprocally connected to the thalamic
model to mimic anatomically verified connectivity. The thalamic
model included the medial geniculate body (MGB) and the thalamic
reticular nucleus (TRN). MGB includes core and matrix populations ofthalamocortical (TC) neurons with distinct projection patterns to different layers of A1, and thalamic interneurons (TI) projecting locally.
TRN included thalamic reticular neurons (RE) primarily inhibiting MGB.
Thalamocortical neurons were driven by artificial spike generators
simulating background inputs from non-modeled brain regions. Auditory stimulus related inputs were simulated using phenomenological models of the cochlear auditory nerve and the inferior colliculus(IC) that captured the main physiological transformations occurringin these regions. The output of the IC model was then used to drive
the thalamocortical populations. This allowed us to provide any arbitrary sound as input to the model, including those used during ourmacaque in vivo experiments, thus facilitating matching model todata.
We used evolutionary algorithms to tune the network to generate
experimentally-constrained firing rates for each of the 42 neural populations. We tuned 12 high-level connectivity parameters, including
background input and E->E, E->I, I->E, I->I weight gains, within parameter value ranges constrained biologically. Each simulated second
required approximately 1 hour on 96 supercomputer cores. For theevolutionary optimization we ran 100 simultaneous simulations (9,600
cores) every generation. To the best of our knowledge, this is the firsttime evolutionary optimization has been successfully used for largescale biophysically-detailed network models.
We will use our model to determine mechanistic origins of spatiotemporal neuronal oscillatory patterns observed in vivo using an iterativemodeling data-analysis process. At the end of the process, to confirm
model predictions, we will use targeted deep brain electrical microstimulation and pharmacological manipulations.
Acknowledgements: Funded by NIH NIDCDR01DC012947,
U24EB028998, U01EB017695, DOH01-C32250GG-3450000, Army
Research Office W911NF-19-1-0402.

﻿BMC Neurosci 2020, 21(Suppl 1):54

Page 81 of 123

P144
Closed loop parameter estimation using GPU acceleration
with GeNN
Felix B ­Kern1, Michael ­Crossley2, Rafael ­Levi3, György K­ emenes2, Thomas­Nowotny4
1
University of Tokyo, International Research Center for Neurointelligence,
Tokyo, Japan; 2University of Sussex, School of Life Sciences, Brighton,
United Kingdom; 3Universidad Autonoma de Madrid, Escuela Politechnica
Superior, Madrid, Spain; 4University of Sussex, School of Engineering
and Informatics, Brighton, United Kingdom
Correspondence: Thomas Nowotny (t.nowotny@sussex.ac.uk)
BMC Neuroscience 2020, 21(Suppl 1):P144
A common approach to understanding neuronal function is to build
accurate and predictive models of the excitable membrane. Models
are typically based on voltage clamp data where ion channels of different types are pharmacologically isolated and the stationary state
and timescale of (in)activation are estimated based on the transmembrane currents observed in response to a set of constant voltage steps.
The basic method can be extended with different stepping protocols
or input waveforms and by performing parameter fits on the full timeseries. Further improvements are achieved with parameter estimation
on additional current clamp data, an active field of research. Some
examples of employed estimation approaches include adaptive coupling to synchronise the model to data, driving neurons with chaotic
input signals, and using distributions of parameter values in a path
integral method.
Enabled by our GPU enhanced neural networks (GeNN) framework [1],we here present work that makes a different conceptual advance ofperforming parameter estimation in an online closed loop approach
while the neuron is being recorded. In doing so we can select stimulations that are highly informative for the parameter estimation process
at any given time. We can also track time dependent parameters byobserving how parameter estimates develop over time.
To demonstrate our new method we use the model system of the B1
motor cell in the buccal ganglion of the pond snail Lymnaea stagnalis. Neurons are recorded with two sharp electrodes in current clamp
mode. We have built a conductance based initial model from a published set of Hodgkin-Huxley conductances [2], using standard parameter estimation methods and data we obtained with a simple set ofcurrent steps. To perform closed loop parameter estimation, we use
a genetic algorithm (GA) in which a population of 8192 model neurons with candidate parameter values is simulated on a GPU (NVIDIA
Tesla K40c) in parallel and in real time. Models are then compared to
the response of the recorded neuron and selected for goodness of fit,
as is standard for a GA approach. The novel element of our method isthe next step, where we evaluate a pool of candidate stimuli against
the model population, selecting the stimulus with the most diverse
responses for the next epoch. The selected stimulus is then applied toboth the recorded neuron and the population of models and the normal GA procedure continues.
Figure 1 shows a representative example of online fitting to a neuron.
We first fit a set of 52 parameters to fine-tune model kinetics to the cellunder stationary conditions. Then, we restricted fitting to non-kinetic
parameters (maximum conductances, equilibrium potentials, andcapacitance) and continued to run the algorithm described above,
while at the same time manipulating sodium levels in the extracellular bath. The online fitting procedure can detect and track the change
in sodium concentration as putative changes in sodium conductanceand reversal potential.

Fig. 1 Example fitting run. Sodium-free saline is washed in (epochs
1-50), and gradually removed (51-100). Left: Voltage of the neuron(red) and best model (blue) in response to a ramp stimulus. Bottom:
Spike counts with the same stimulus. Right: Value distributions ofsome of the fitted parameters (y axis) through time (x axis). The model
recapitulates the change in sodium (top 2 plots)
Acknowledgments: This work was partially supported by the EU
under Grant Agreement 785907 (HBP SGA2).
References
1. Yavuz E, Turner JP, Nowotny T. GeNN: a code generation framework foraccelerated brain simulations. Scientific Reports. 2016; 6: 18854.
2. Vehovszky A, Szabo H, Elliott CJH. Octopamine increases the excitability ofneurons in the snail feeding system by modulation of inward sodium cur‑rent but not outward potassium currents. BMC Neuroscience. 2005; 6: 70.

P145
Possible roles of non‑synaptic interactions between olfactory
receptor neurons in insects
Mario ­Pannunzi1, Thomas ­Nowotny2
1
University of Sussex, Department of Informatics, Brighton, United
Kingdom; 2University of Sussex, School of Engineering and Informatics,
Brighton, United Kingdom
Correspondence: Mario Pannunzi (mario.pannunzi@gmail.com)
BMC Neuroscience 2020, 21(Suppl 1):P145
In insects, olfactory receptor neurons (ORNs) are grouped in hairs(sensilla) in a stereotypical way. For example, in Drosophila each sensillum houses 2 or 4 ORNs. ORNs that are co-housed in the same sensillum interact with each other via a non-synaptic mechanism (NSI, seeFig. 1a), a mechanism which is still not fully understood. The mechanism could simply be a spandrel or instead improve the function of
the insect olfactory system. A number of hypotheses have been suggested [1] trying to explain the potential role of NSIs.

﻿BMC Neurosci 2020, 21(Suppl 1):54

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When a network with LN inhibition (with NSIs) is stimulated with asynchronous whiffs of two odorants, its PN outputs in response to thesecond whiffs are strongly (mildly) altered by the response to the first
whiff.
More complex interpretations are needed when assessing the capacity
to encode the correlation between two odorants. We noted that: 1) In
terms of average PN activity, the network with LN inhibition is able toencode stimulus correlation but the network with NSI mechanism isnot, but 2) In terms of peak PN activity (response higher than a given
threshold), the network with NSI mechanism encodes correlations better than the one with LN inhibition. This improvement is bigger for
shorter whiff durations (<100 ms).
Acknowledgments: This research was funded by the Human Frontiers
Science Program, grant RGP0053/2015 (Odor Objects), the European
Union under grant agreement 785907 (HBP SGA2) and a Leverhulme
Trust Research Project Grant.
Reference
1. Todd JL, Baker TC. Function of peripheral olfactory organs. In: Insect olfac‑
tion 1999 (pp. 67-96). Springer, Berlin, Heidelberg.

P146
Larger GPU‑accelerated brain simulations with proceduralconnectivity
James ­Knight1, Thomas ­Nowotny2
1
University of Sussex, Department of Informatics, Brighton, United
Kingdom; 2University of Sussex, School of Engineering and Informatics,
Brighton, United Kingdom
Correspondence: James Knight (j.c.knight@sussex.ac.uk)
BMC Neuroscience 2020, 21(Suppl 1):P146

Fig. 1 a Non-synaptic interaction (NSI) between ORNs is probably
mediated by an electrical field interaction between closely apposed
ORNs in olfactory sensilla. b Our model: two ORN types and theirrespective PNs and LNs, in the AL. Each ORN type is tuned to a set ofodorants and converges onto its corresponding PNs. Each PN excites
its respective LNs, and is inhibited from LNs of the other type
We analyzed two hypotheses that suggest that NSIs play a role in odor
sensing in mixtures with a computational model of the first two layers of the Drosophila olfactory system - the ORNs on the antennae
and the glomeruli, with projection neurons (PNs) and local neurons(LNs), in the antennal lobe (AL, see Fig. 1b). The model is the first to
consider NSIs between ORNs in the context of the circuits of the firstand second layer of processing in the insect olfactory pathway. We
constrained the model by reproducing the responses to a set of typical odor stimuli reported in the literature. Then, we tested the feasibility of the hypotheses and compared the advantages of having NSIs
against a control network which lacked any interaction between ORNs
or PNs, and with a network without NSIs but strong lateral inhibition in
the AL, a mechanism proposed to be a valid alternative to NSIs.
The two tested hypotheses were: 1) NSIs could improve the concentration ratio identification of a mixture of odorants by increasing the
dynamic range over which it can be perceived without distortion. 2)
NSIs could help insects to distinguish mixtures of odorants emanating from a single source against those emanating from two separate
sources, by improving the capacity to encode the correlation between
olfactory stimuli.
For the first hypothesis, we observed that: 1) When comparing thecapacity to encode the ratio of the concentration of short synchronous whiffs via PN responses, both networks, the one with NSIs andthe one with AL inhibition, outperform the ‘control network’. Moreover, the NSIs help more than the LN inhibition for this task. This effectis stronger for very short stimuli (<100ms) than for longer stimuli. 2)

Large-scale brain simulations are important tools for investigating
brains’ dynamics and function. However, due to insufficient computing power and a lack of detailed connectivity data, brain models built
at the cellular level have often been limited to the scale of individual
microcircuits [1]. Larger models of multiple areas have typically been
built at a higher level of abstraction. However, recent data [2] showed
that, in the cortex, there are features of neuronal activity which can
only be reproduced by modelling multiple interconnected microcircuits at the cellular level.
Even small mammals have trillions of synapses and as each synapse
typically requires at least 32 bits of storage in a simulation, a model ofthis scale requires terabytes of memory – more than any single desktop machine has available. Therefore, until now, simulating large-scalemodels has required access to a distributed computer system. Suchsystems are costly and power-hungry, meaning that they are normallyshared resources, accessible only to a limited number of well-funded
researchers for limited runtimes. Neuromorphic systems are a potential alternative but few are currently able to simulate the density ofconnectivity found in the brain and most are still prototypes with limited availability. Alternatively, Graphical Processing Units (GPUs) have
proved useful in tasks including training deep learning systems. In ourprevious work [3] we showed that using GeNN – our GPU accelerated
spiking neural network simulator – models with around 100×103 neurons and 1×109 synapses could be simulated on a single GPU at a similar speed to supercomputers and neuromorphic systems. However,individual GPUs do not have enough memory to simulate larger brain
models and GPU clusters suffer from the same issues as any other distributed computer systems.
Here, we present extensions to GeNN that enable it to ‘procedurally’generate connectivity and synaptic weights ‘on the go’ as spikes are
triggered instead of retrieving them from memory. This approach iswell-suited to GPU architectures because their raw computational
power is often under-utilised when simulating spiking neural networks
due to memory bandwidth limitations. We demonstrate the power of
our approach with a model of the Macaque visual cortex consisting of
4×106 neurons and 24×109 synapses [4]. We find that, with our new
method, this model can be simulated correctly on a single GPU and up

﻿BMC Neurosci 2020, 21(Suppl 1):54

to 35% faster than supercomputer simulations [5]. We believe that thisis a significant step towards making large-scale brain modelling accessible to more researchers.

Fig. 1 Results of macaque visual cortex model simulation. A-C
Raster plots of activity of 3% of neurons in 3 areas. Blue: excitatory,
red: inhibitory. D-F Statistics for each population across all 32 areas
simulated using GeNN and NEST (supercomputer). D Average firing
rates. E Average pairwise correlation coefficients. F Average irregular‑
ity measured by revised local variation
Acknowledgements: This work was funded by the EPSRC (Grant
number EP/P006094/1).
References
1. Potjans T, Diesmann M. The Cell-Type Specific Cortical Microcircuit: Relating
Structure and Activity in a Full-Scale Spiking Network Model. Cerebral
Cortex. 2014; 24: 785-806.
2. Belitski A, et al. Low-frequency local field potentials and spikes in primaryvisual cortex convey independent visual information. Journal of Neurosci‑
ence. 2008; 28(22): 5696-709.
3. Knight J, Nowotny T. GPUs Outperform Current HPC and Neuromorphic
Solutions in Terms of Speed and Energy When Simulating a Highly-Con‑
nected Cortical Model. Frontiers in Neuroscience. 2018; 12: 1-19.
4. Schmidt M, et al. A multi-scale layer-resolved spiking network model ofresting-state dynamics in macaque visual cortical areas. PLoS Computa‑
tional Biology. 2018; 14(10): e1006359.
5. Knight J, Nowotny T. Larger GPU-accelerated brain simulations with proce‑dural connectivity. BioRxiv. 2020. 2020.04.27.063693

Page 83 of 123

P147
Seizure forecasting from long‑term EEG and ECG data using criticalslowing principle­ urse4
Wendy ­Xiong1, Tatiana ­Kameneva2, Elisabeth L­ ambert3, Ewan N
1
Swinburne University of Technology, Melbourne, Australia; 2Swinburne
University of Technology, Telecommunication Electrical Robotics
and Biomedical Engineering, Melbourne, Australia; 3Swinburne Universityof Technology, School of Health Sciences, Department of Healthand Medical Sciences, Melbourne, Australia; 4Seer Medical, Melbourne,
Australia
Correspondence: Wendy Xiong (wenjuan.xiong1@gmail.com)
BMC Neuroscience 2020, 21(Suppl 1):P147
Epilepsy is a neurological disorder characterized by recurrent seizuresthat are transient symptoms of synchronous neuronal activity in thebrain. Epilepsy affects more than 50 million people worldwide [1]. In
Australia, over 225,000 people live with epilepsy [2]. Seizure prediction allows patients and caregivers to deliver early interventions andprevent serious injuries. Electroencephalography (EEG) has been used
to predict seizure onset, with varying success between participants[3,4]. There is an increasing interest to use electrocardiogram (ECG) to
help with seizures detection and prediction. The aim of this study is touse long-term continuous recordings of EEG and ECG data to forecast
seizures.
EEG and ECG data from 7 patients was used for analysis. Data wasrecorded using 21 EEG electrodes and 3 ECG electrodes by Seer with
an ambulatory video-EEG-ECG system. The average period of recording was 95 hours (range 51-160 hours). Data was annotated by a clinician to indicate seizure onset and offset. On average, 4 clinical seizures
occurred per participant (range 2-10). EEG and ECG data were bandpass filtered using Butterworth filter (1-30 Hz for EEG, 3-45 Hz for ECG).
A characteristic of a system that is nearing a critical transition is criticalslowing, which refers to the tendency of the system to take longer toreturn to equilibrium after perturbations, measured by an increase insignal variance and autocorrelation [5]. The variance and autocorrelation of EEG and ECG signals were calculated for each electrode in 1 s
window for each time point. The autocorrelation value was set to thewidth of half maximum of the autocorrelation function. The instantaneous phases of variance and autocorrelation signals were calculated
at each time point using Hilbert transform. To extract long (1 day) andshort (20 s in EEG, 10 min in ECG) cycles in the variance and autocorrelation signals, a moving average filter has been applied. The relationshipbetween seizure onset times and phase of variances and autocorrelation were investigated in long and short cycles. The probability distribution for seizure occurrence was determined for each time point. The
seizure likelihood was determined at three levels: low, medium andhigh, based on two thresholds defined as functions of maximum seizure probability. Data analysis was performed in Python 3.
Results show that the variance and autocorrelation of EEG data
increased at the time of seizure onset in 66.7% and 68.3% of cases,
respectively. The variance and autocorrelation of ECG data increased
at the time of seizure onset in 60% and 50% cases, respectively. Longand short cycles of variance and autocorrelation had consistent
results. Result indicate that critical slowing may be present in a neural system during seizures and this feature could be used to forecast
seizures.
References
1. Thijs RD, Surges R, O’Brien TJ, Sander JW. Epilepsy in adults. The Lancet.
2019; 393(10172): 689-701.
2. Epilepsy Action Australia [internet]. Facts and Statistics [cited 2020 July 8].
Available from: www.epilepsy.org.au.
3. Cook MJ, et al. Prediction of seizure likelihood with a long-term, implanted
seizure advisory system in patients with drug-resistant epilepsy: a first-inman study. The Lancet Neurology. 2013; 12(6): 563-71.
4. Karoly PJ, Ung H, Grayden DB, Kuhlmann L, Leyde K, Cook MJ, Freestone DR.
The circadian profile of epilepsy improves seizure forecasting. Brain. 2017;
140(8): 2169-82.
5. Scheffer M, et al. Early-warning signals for critical transitions. Nature. 2009;
461(7260): 53-9.

﻿BMC Neurosci 2020, 21(Suppl 1):54

P148
Biophysically grounded mean‑field models of neural populations
under electrical stimulation
Caglar ­Cakan1, Klaus ­Obermayer2
1
Technische Universität Berlin, Berlin, Germany; 2Technische Universität
Berlin, Department of Software Engineering and Theoretical Computer
Science, Berlin, Germany
Correspondence: Caglar Cakan (cakan@ni.tu‑berlin.de)
BMC Neuroscience 2020, 21(Suppl 1):P148
Electrical stimulation of neural systems is a key tool for understanding neural dynamics and ultimately for developing clinical treatments.
Many applications of electrical stimulation affect large populationsof neurons. However, computational models of large networks ofspiking neurons are inherently hard to simulate and analyze. We
evaluate a reduced mean-field model of excitatory and inhibitory
adaptive exponential integrate-and-fire (AdEx) neurons which can beused to efficiently study the effects of electrical stimulation on large
neural populations. The rich dynamical properties of this basic cortical model are described in detail and validated using large network
simulations. Bifurcation diagrams (Fig. 1) reflecting the network’s state
reveal asynchronous up and down-states, bistable regimes, and oscillatory regions corresponding to fast excitation-inhibition and slow
excitation-adaptation feedback loops. The biophysical parametersof the AdEx neuron can be coupled to an electric field with realistic
field strengths which then can be propagated up to the population
description. We show how on the edge of bifurcation, direct electricalinputs cause network state transitions, such as turning on and off oscillations of the population rate. Oscillatory input can frequency-entrain
and phase-lock endogenous oscillations. Relatively weak electric fieldstrengths on the order of 1 V/m are able to produce these effects,
indicating that field effects are strongly amplified in the network. The
effects of time-varying external stimulation are well predicted by themean-field model, further underpinning the utility of low-dimensional
neural mass models.

Fig. 1 Bifurcation diagrams depict the state space of the E-I systemin terms of the mean external inputs. a-b Mean-field model and AdEx
network without adaptation with up and down-states, a bistableregion bi (green dashed contour) and an oscillatory region ­LCEI (whitesolid contour). c-d With somatic adaptation. The bistable region is
replaced by a slow oscillatory region ­LCaE

Page 84 of 123

P149
Towards large‑scale model of sleeping brain: sleep spindles
in network mass models
Nikola Jajcay, Klaus Obermayer
Technische Universitat Berlin, Neural Information Processing Group, Berlin,
Germany
Correspondence: Nikola Jajcay (jajcay@ni.tu‑berlin.de)
BMC Neuroscience 2020, 21(Suppl 1):P149
The hierarchical nesting of sleep spindles and cortical slow oscillations
is considered a precursor of successful episodic memory consolidation
where it, presumably, sets the stage for memory traces migration fromshort-term hippocampal storage to longer-lasting neocortical sites [1].
Spindles are thought to be generated in the thalamus and projected
onto cortical sites, while slow oscillations originate in the cortex andmigrate in wave-like patterns to the thalamus, hence the interplay
between the two rhythms is orchestrated within the thalamocorticalcircuitry. The state-of-the-art mass models of the thalamocortical loop,
however, only consider relevant motifs and patterns in sleep but discard network effects. Here we take the first steps towards integrating
thalamocortical projections into a large-scale brain model and modelling whole-brain cortical slow-wave activity and thalamic spindles as
seen in non-REM sleep.
We model the thalamus as a network node containing one excitatory and one inhibitory mass representing thalamocortical relay
neurons and thalamic reticular nuclei, respectively. With little deviations, our model follows the thalamic component developed in [2].
In the thalamic submodule, we investigated its spindling behaviour
upon changing, firstly, conductances of rectifying and T-type calcium current, by which the thalamus can be parametrised in threeoscillatory regimes: fast oscillations, dominated by Ca current; spindle regimes with a balanced interplay of Ca and rectifying currents,and slow delta oscillations for strong hyperpolarisation. Next, by theapplication of external excitatory firing rate drive, which simulates
excitatory source connected to the thalamus, we found dynamicallyinteresting spindle-promoting regimes in interaction with thalamic
conductances (see Fig. 1 for estimated number of spindles), and bychanging the parameters or external drive, we were also able tocontrol the inter-spindle interval, spindle duration, and shape of
spindle envelope.

Fig. 1 Various oscillatory regimes for isolated thalamus. Each panel
shows estimated number of spindles (color-coded) in 60 secondslong simulation dependent on excitatory rate drive to thalamocorti‑
cal relay mass (TCR) and thalamic reticular nuclei (TRN). Different
panels encode different conductances for potassium leak current(gLK) and rectifying current (gh)Secondly, we connected the thalamic node to one cortical node,modelled as interconnected excitatory and inhibitory adaptive exponential integrate-and-fire neuronal masses [3]. The excitatory masscontains a spike-triggered adaptation mechanism by which the node﻿BMC Neurosci 2020, 21(Suppl 1):54

Page 85 of 123

is parametrised to sit in the limit cycle generating slow oscillations by
the means of excitation-adaptation feedback loop. We investigated the
dynamical repertoire of the connected model concerning the connection
strength and network delays. Our preliminary results indeed show that,upon connection, the thalamic spindles are imprinted into cortical nodeactivity
where they modulate slow oscillation envelope, and that slow oscillation activity of the cortex, in turn, shapes spindling behaviour ofthalamocortical relay mass by affecting spindle duration and interspindle interval. Our connected model also conserves the phase-phaseand phase-amplitude couplings reported in the literature on observed
EEG data or other thalamocortical models.
Our results suggest that thalamic mass model of spindle activity can beconnected to various mass or mean-field models of cortical nodes and,after careful treatment of network connections and delays, we believethat our conclusions would carry over to the large-scale network model.
References
1. Ji D, Wilson MA. Coordinated memory replay in the visual cortex and hip‑pocampus during sleep. Nature neuroscience. 2007; 10(1): 100-7.
2. Schellenberger Costa M, et al. A thalamocortical neural mass model ofthe EEG during NREM sleep and its response to auditory stimulation. PLoS
Computational Biology. 2016; 12(9): e1005022.
3. Cakan C, Obermayer K. Biophysically grounded mean-field models of neural
populations under electrical stimulation. PLoS Computational Biology. 2020;
16(4):ce1007822.

P150
The cortical ignition is related to local and mesoscale featuresof structural connectivity in the connectome of non‑humanorganisms
Samy Castro, Patricio Orio
Universidad de Valparaiso, Centro Interdisciplinario de Neurociencia de
Valparaíso, Valparaíso, Chile
Correspondence: Samy Castro (samy.castro@cinv.cl)
BMC Neuroscience 2020, 21(Suppl 1):P150
One way to study the fluctuations of cortical activity is the ignition,i.e. the fast transition from low to high firing rate on cortical regions.
The capability of cortical regions to flexibly sustain an ‘ignited’ stateof activity has been related to conscious perception and hierarchicalinformation processing. Also, we theoretically showed that the propensity of cortical regions to be ignited is tightly linked to the coreshell structure of the human connectome, i.e. the shells of strongest
within-connected subsets of regions [1]. Moreover, the weight ofconnections (in particular the inputs) has the greatest influence in thepropensity of a region to get ignited. Now, using the connectomes of
non-human organisms (macaque [2], mouse [3], rat [4] and fruit fly [5]),we assessed whether the relationship between ignition and both local
and mesoscale structural organization is maintained in the relatedorganisms. The ignition capabilities of each connectome are obtained
from the whole-brain mean-field model, using simulations of theresting-state cortical activity. Then, the structural organization is analyzed using thes-core decomposition for the mesoscale level, and thedegree, betweenness centrality and participation index for the local
level. The order in which cortical regions are ignited is correlated toboth thes-core and the strength of the regions (i.e. hubs) of the different organisms (Fig. 1). Moreover, we found that ignition recruitment
is primarily related to weights of the inputs, rather than the outputs,of each region. The local level better explains the region propensity
to get ignited in the case of macaque and rat, whereas the mesoscale
fits better in the case of the fruit fly and mouse. We suggest that theweighted organization of non-human connectomes, as in the human,
operates as a structural principle of ignition rooted in evolution.

Fig. 1 Ignition recruitment is related to input weights in the local
and mesoscale level. A-F The scatter plots of the coupling gain of
ignition (x-axis) and the mesoscale level, A smax, B out-smax, andC in-smax (y-axis), and local level, D strength, E out-strength, and F
in-strength of each region of the cocomac (blue), rat (orange), mouse(green) and drosophila (red) dataset
Acknowledgements: This work was supported by Fondecyt Grant
1181076 (PO) and the Advanced Center for Electrical and Electronic
Engineering (FB0008 CONICYT, Chile)(PO, SC). The Centro Interdisciplinario de Neurociencia de Valparaíso (CINV) is a Millenium Institutesupported by the Millennium Scientific Initiative of the Ministerio de
Economía (Chile). SC was funded by Beca Doctorado Nacional CONICYT 21140603 and by Programa de Doctorado en Ciencias, mención
Neurociencia, Universidad de Valparaíso.
References
1. Castro S, El-Deredy W, Battaglia D, Orio P. Cortical ignition dynamics is tightly
linked to the core organisation of the human connectome. bioRxiv. 2020.
2. Bakker R, Wachtler T, Diesmann M. CoCoMac 2.0 and the future of tracttracing databases. Frontiers in Neuroinformatics. 2012 Dec; 6: 30.
3. Rubinov M, Ypma R, Watson C, and Bullmore E. Wiring Cost and Topologi‑
cal Participation of the Mouse Brain Connectome. PNAS. 2015, 112(32):10032–10037.
4. Bota M, Sporns O, and Swanson L. Architecture of the Cerebral Cortical
Association Connectome Underlying Cognition. PNAS. 2015, 112(16):
E2093–2101.
5. Shih CT, et al. Connectomics-based analysis of information flow in the
Drosophila brain. Current Biology. 2015; 25(10): 1249-58.

P151
Generalisation of stimulus representation across somatosensory
cortex areas in a cellular‑resolution photostimulus detection task
Thijs L van der Plas, James M Rowland, Robert M Lees, Adam M Packer
University of Oxford, Department of Physiology, Anatomy, and Genetics,
Oxford, United Kingdom
Correspondence: Thijs L van der Plas (thijs.vanderplas@dtc.ox.ac.uk)
BMC Neuroscience 2020, 21(Suppl 1):P151
Mice use whiskers to explore their environment. Whisker stimulation
elicits a neural response in primary (S1) and secondary (S2) somatosensory cortex, two highly interconnected and hierarchically organised
brain regions. Their interaction has been related to stimulus detection[1], although its precise functional role remains unclear [2]. Here, we
aim to assign a function to this circuit for a stimulus detection task, byassessing how S1-S2 interactions facilitate stimulus perception.
We have conditioned mice to detect 2-photon optogenetic stimulation of random ensembles of S1 cells. This allows us to control thenumber of stimulated cells on a trial by trial basis, and to separate theinitial stimulus representation from the ensuing network response.

﻿BMC Neurosci 2020, 21(Suppl 1):54

Simultaneously, we record the calcium activity of both stimulated andunstimulated cells in S1 and S2, rendering an all-optical approach to
study neural dynamics [3]. In short, we are able to directly stimulate S1neurons, hence defining the initial stimulus in S1, while recording thesubsequent S1 and S2 neural response.
Mice were conditioned to report the photostimulus by licking a water
spout. The task was divided into Go trials, where a varying number(5 - 150) of cells were stimulated, and Catch trials without stimulation. Behavioural accuracy increased as more cells were stimulated(Fig. 1A), indicating that our task operated in the regime of perception.
We observed strongly elevated, sustained neural population activity inboth S1 and S2 on successful Go trials (Hits), compared to both unsuccessful Go trials (Misses) and to licking behaviour in the absence of astimulus (False Positives). This suggests that S1 and S2 encode information during Hit trials that is different from both passive stimulusinduced activity and neural signals driven by movement and reward.
To confirm whether neurons indeed encoded stimulus information,
we performed a stimulus decoding analysis on S1 and S2 neural activity separately. We only consider trials where mice licked (i.e. Hits andFalse Positives), to avoid a behavioural bias. Here, we observe a significant difference between S1 (where the stimulus occurred) and S2(Fig. 1B): Stimulus information could only be decoded from S2 after aconsiderable time post-stimulus, while S1 could be decoded directly
post-stimulus. Hence, stimulus information has propagated (directly
or indirectly) from S1 to S2.
Furthermore, we find a striking dynamic property of information coding in the S1-S2 circuit. Directly post-stimulus at 1s, decoding accuracy
in S1 depends on the stimulus strength, the number of stimulated
cells (Fig. 1C). However, after a delay of 3s, we find that accuracy has
increased, and has become independent of the original stimulus
strength (Fig. 1C). S2 decoding accuracy increased equivalently (notshown), even though S2 decoding performs at chance level directly
post-stimulus (Fig. 1B).
The stimulus detection task design requires the animals to elicit thesame response, independent of stimulus strength. Our results showthat the S1-S2 circuit dynamically performs this computation: by
propagating stimulus information between S1 and S2, the neural code
becomes independent of the original stimulus strength. Hence, we
uncover a putative mechanism of how interregional communication
can transform stimulus information to facilitate stimulus detection.

Fig. 1 A Behavioural accuracy (fraction of correct trials) is plottedagainst the number of photostimulated (PS) neurons Nps (P-values,
Wilcoxon). B Decoding accuracy was calculated per time point withlogistic regression. Shaded areas indicate std. across 6 mice. Red box
indicates the 2-photon stimulus. C Decoded probability of PS in S1 for
2 time points (indicated by triangles in B), split by Nps

Page 86 of 123

References
1. Kwon SE, Yang H, Minamisawa G, O’Connor DH. Sensory and decisionrelated activity propagate in a cortical feedback loop during touch percep‑
tion. Nature neuroscience. 2016; 19(9): 1243-9.
2. Ni J, Chen JL. Long‐range cortical dynamics: a perspective from the mousesensorimotor whisker system. European Journal of Neuroscience. 2017;
46(8): 2315-24.
3. Packer AM, Russell LE, Dalgleish HW, Häusser M. Simultaneous all-opticalmanipulation and recording of neural circuit activity with cellular resolution
in vivo. Nature methods. 2015; 12(2): 140-6.

P152
Face‑selective neurons arise spontaneously in untrained deepneural networks
Min ­Song1, Seungdae B­ aek1, Jaeson J­ ang1, Gwangsu ­Kim2, Se‑Bum ­Paik1
1
Korea Advanced Institute of Science and Technology, Department of Bio
and Brain Engineering, Daejeon, South Korea; 2Korea Advanced Instituteof Science and Technology, Department of Physics, Daejeon, South Korea
Correspondence: Min Song (night@kaist.ac.kr)
BMC Neuroscience 2020, 21(Suppl 1):P152
In the primate brain, the neurons that selectively respond to faces areobserved and considered as the basis of face recognition [1]. Althoughsuch face-selective neurons are observed in infant animals [2], theorigin of face-selectivity is still under debate, because conflicting findings have raised questions whether this neuronal selectivity can arise
spontaneously [3], or requires training from visual experience [2].
Here, we show that face-selective neurons can spontaneously arise
in untrained deep neural networks (DNN), together with the previous
notion that DNN could be considered as a visual cortex model that canperform human-level visual function and predict neuronal responses.
Using biologically-inspired neural networks, AlexNet, we measured
responses of the last convolutional layer to the image sets of faceand 15 non-face classes. We found that face-selective neurons arise
in untrained AlexNet with randomly permuted weights, where theface-selective neuron was defined as a neuron that showed a significantly higher response to face images compared to non-face images.
To qualitatively examine the feature-selective response of these faceselective neurons, we reconstructed the preferred feature images ofindividual neurons using the reverse correlation method. We foundface-components, such as eyes, nose, and mouth, in preferred feature
images of face-selective neurons whereas no noticeable shape wasfound in neurons with no selectivity. Next, to test whether the selective response of these neurons could provide sufficient information toclassify a face from other objects, we trained a support vector machine(SVM) to classify whether the given image was a face using neural
responses of the untrained network. As a result, the SVM trained with
only face-selective neurons shows significantly better performance
than that trained with neurons with no selectivity. Next, to examine
whether the face-selective neurons show view-point invariant characteristics observed in monkeys, we measured the responses of the
permuted AlexNet while face images from five different angles were
provided to the network. Surprisingly, the face-selective neurons in
the network show viewpoint invariant responses and their level ofinvariance increased along the network hierarchy in the permuted
AlexNet, similar to that in monkey IT. Lastly, to examine the originof face-selectivity in untrained neural networks, we implemented
a randomly initialized network where values in each weight kernel
were randomly drawn from a weight distribution of the pre-trained
AlexNet. We found that the number of face-selective neurons abruptly
decreases when the weight variation is reduced to 52% of that in the
pre-trained network. These results suggest that statistical variation
present in the random feedforward projections could solely drive
the emergence of innate face-selective neurons in the visual system.

﻿BMC Neurosci 2020, 21(Suppl 1):54

Overall, our findings provide insight into the origin of cognitive functions in both artificial and biological neural networks.

Fig. 1 Spontaneous emergence of face selectivity in untrained
networks. a An untrained AlexNet. b Tuning curves for face-selec‑
tive neurons in untrained network. c Preferred feature images in
untrained network. d Face classification performance of SVM using
face-selective neurons in untrained network

Page 87 of 123

dot patterns representing numbers from 1 to 30. For stimuli, we used
three different sets to ensure invariance of the number tuning for certain geometric factors (stimulus size, density, and area). A network unit
was considered to be number-selective if its response significantly
changes across the numerosity (p < 0.01, two-way ANOVA) but there
is no significant effect for the stimulus set or interaction between two
factors (p > 0.01). Importantly, number-selective units were observedin the permuted AlexNet (9.58% of units in the last convolutional
layer), even though the network was never trained for any task after
being permuted. Observed number-selective units followed theWeber-Fechner law observed in the brain, where the width of the tuning curves increases proportionally in the numerosity. We also showedthat these units enable the network to perform a number discrimination task, by training a support vector machine (SVM) to compare
numerosities in two different images using the response of numberselective units. Next, to explain how number-selective units emerge inpermuted networks, we hypothesized that the number tuning to various numerosities can be initiated from the monotonic unit activities
in the earlier layer, the response of which monotonically decreases orincreases as the given numerosity increases. To test this idea, we performed a model simulation for the randomly weighed summation oftuning curves of increasing and decreasing activities and confirmedthat tuning to all the tested numerosities was successfully generated. Notably, the curve tuned to smaller numbers was generated by
the summation of strongly weighted decreasing activities and weakly
weighted increasing activities. As expected, in the permuted AlexNet,
we observed that number-selective units tuned to smaller numbers
receive strong inputs from the decreasing units and vice versa. These
results suggest that number-tuned neurons may spontaneously arise
from the statistical variation of feedforward projections in the visual
pathway during the early development stage. This finding provides
new insights into the origin of cognitive functions in biological brains,
as well as in artificial neural networks.

Acknowledgements: this work was supported by Grant Number:(2019M3E5D2A01058328, 2019R1A2C4069863).
References
1. Tsao DY, Freiwald WA, Tootell RB, Livingstone MS. A cortical region consist‑ing entirely of face-selective cells. Science. 2006; 311(5761): 670-4.
2. Livingstone MS, et al. Development of the macaque face-patch system.
Nature Communications. 2017; 8(1): 1-2.
3. Deen B, et al. Organization of high-level visual cortex in human infants.
Nature communications. 2017; 8(1): 1-0.

P153
Number‑selective units can spontaneously arise in untrained deepneural networks
Jaeson ­Jang1, Gwangsu ­Kim2, Seungdae B­ aek1, Min ­Song1, Se‑Bum ­Paik1
1
Korea Advanced Institute of Science and Technology, Department of Bio
and Brain Engineering, Daejeon, South Korea; 2Korea Advanced Instituteof Science and Technology, Department of Physics, Daejeon, South Korea
Correspondence: Jaeson Jang (jaesonjang@kaist.ac.kr)
BMC Neuroscience 2020, 21(Suppl 1):P153
Number sense is an ability to estimate number of visual items (numerosity) without counting, which is observed in newborn animals of
various species. In single-neuron recordings in numerically naïve
monkeys, it was observed that individual neurons can respond selectively to the numerosity [1]. This suggests that number-selective neurons spontaneously arise for a foundation of innate number sense,
but it remains unclear how these neurons originate in the absence oflearning. Here, using a deep neural network (DNN) designed from thestructure of a visual pathway (AlexNet), we show that number tuningof network units can spontaneously arise in untrained networks, evenin the absence of any learning. To devise an untrained network, we
randomly permuted the weights of filters in each convolutional layerof the pre-trained AlexNet and examined the response to images ofFig. 1 Spontaneous emergence of number-selective units in
untrained deep neural networks. a Stimuli encoding different numer‑
osities and geometric factors. b An untrained AlexNet was devised byrandomly permuting the weights of the pre-trained AlexNet. c Tuning
curves of individual number-selective network units﻿BMC Neurosci 2020, 21(Suppl 1):54

Page 88 of 123

Acknowledgments: We thank the National Research Foundation of
Korea (NRF) for supporting Se-Bum Paik with 2019R1A2C4069863 and2019M3E5D2A01058328.
Reference
1. Viswanathan P, Nieder A. Neuronal correlates of a visual “sense of number”
in primate parietal and prefrontal cortices. Proceedings of the National
Academy of Sciences. 2013; 110(27): 11187-92.

P154
Rhythmic eye movement predicts active perception of ambiguous
visual stimulus
Woochul ­Choi1, Hyeonsu ­Lee2, Se‑Bum ­Paik2
1
Korea Advanced Institute of Science and Technology, Daejeon, South
Korea; 2Korea Advanced Institute of Science and Technology, Departmentof Bio and Brain Engineering, Daejeon, South Korea
Correspondence: Woochul Choi (choiwc1128@kaist.ac.kr)
BMC Neuroscience 2020, 21(Suppl 1):P154
When an ambiguous sensory stimulus is given, our brain often actively
interprets the given stimulus to dissolve ambiguity. A particular example is the condition of ‘bistable perception’, where a given stimulus
can be interpreted as two different states. Under this ambiguity, ourperception alternates between two possible interpretations quasiperiodically with switching frequency varying across individuals. This
characteristic dynamics of bistable perception is thought to reveal
how the brain recognizes incomplete visual signals to lead to a perceptual decision, and a number of studies have been performed toinvestigate the mechanism of its rhythmic perceptual alternation.
However, understanding the dynamics of bistable perception has
proved elusive, as it is a complicated process involving interrelated
cognitive and motor processes even including top-down intention
and eye movements. Recent studies reported that specific eye movement occurs during bistable perception [1], but it is still not known
whether eye movements can actively induce perceptual decision, or
they are just accompanied after the decision. Here, we show that eye
movement may not solely induce perceptual behavior, but the eye
movement patterns reflect the perceptual decisions for interpretationof ambiguous stimuli. We performed a human psychophysics experiment with simultaneous eye-tracking, using three bistable stimuli –racetrack, rotating cylinder, and Necker cube. We found that eye gazeslowly oscillates with 5-10s intervals, the period of which was positively correlated to the frequency of perceptual switch. In addition, we
found that eye gaze movements were observed in the opposite directions before two different perceptual decisions are made. The preceding eye gaze can thus predict the perceptual decision with ~90%
accuracy. We also found that the frequency of the saccadic eye movement during free viewing, which does not require any active interpretation, was correlated with the period of perceptual switch, implyingthat dynamics of eye movement reflects the characteristic of bistable
perception. Next, to isolate the effect of eye movement from intention, we first asked the subjects to have a strong intention to switch(or stay) their perceived state during experiments. With such manipulations, we found that both perceptual decision and eye movements
were significantly altered, compared to the case of non-intended trials. We then controlled visual stimuli so that the subject’s eye movement follows the traces of intention-controlled trials, without actual
intention to change their behavior. Under this condition, even thoughsubjects’ eye movements mimic those of the intended trial, perceptual
decisions were not significantly biased. This suggests that eye movements alone cannot bias perceptual behavior in bistable perception.
Taken together, the results suggest that 1) rhythmic eye movement
correlates with active visual perception, 2) preceding eye gaze trajectory predicts individual decision but 3) eye movement may not solely
induce perceptual decision. These results collectively suggest a relationship between eye movement control, top-down intention, andactive perception.

Fig. 1 Eye gaze reflecting perceptual decisions A Three bistable
perception tasks with eye-tracking B Gaze and perceptual response C
Gaze trajectories before decision D Correlation between the period ofgaze oscillation and perceptual alternation. E Gaze position can pre‑dict perception F Intention alters perceptual behavior but replicatedeye gaze without intention does not alter perception
Acknowledgements:
This
researchwas
National Research Foundation of Korea,
2019M3E5D2A01058328, 2019R1A2C4069863.

supportedbyGrant Number:

Reference
1. Polgári P, Causin JB, Weiner L, Bertschy G, Giersch A. Novel method tomeasure temporal windows based on eye movements during viewing of
the Necker cube. PLOS One. 2020; 15(1): e0227506.

P155
A model for unsupervised object categorization in infants
Sunho ­Lee1, Youngjin ­Park2, Se‑Bum ­Paik2
1
Korea Advanced Institute of Science and Technology, Daejeon, South
Korea; 2Korea Advanced Institute of Science and Technology, Departmentof Bio and Brain Engineering, Daejeon, South Korea
Correspondence: Youngjin Park (yodamaster@kaist.ac.kr)
BMC Neuroscience 2020, 21(Suppl 1):P155
Both the brain and recent deep neural networks (DNNs) can successfully perform visual object recognition at similar levels. However, toacquire this function, DNNs generally require a large amount of training with a huge number of labeled data, whereas the brain does notappear to need such artificially labeled images to learn. Moreover,human infants, who certainly never experienced any training, arestill able to classify unfamiliar object categories [1]. The mechanism
by which the immature brain can categorize visual objects without
any supervisory feedback remains elusive. Here, we suggest a biologically plausible circuit model that can correctly categorize natural images without any supervision. Instead of supervised signals,
which are believed to be essential to train the system, we focused on
the temporal continuity of the natural scene. Natural visual stimuli
to which infants are exposed repeatedly have temporal continuity[2], unlike the dataset of images used to train artificial DNNs. In thisregard, to detect the discontinuity in a natural scene that is potentiallyequivalent to the border of the image cluster of the same object, we
designed a ‘differential unit’ (Fig. 1, DU). The DU estimates the difference between the current input and delayed input before seconds,
and thereby can detect the temporal difference of visual input in realtime. In addition to the DU, to memorize the representation of visualobjects, we also designed a ‘readout network’ (Fig. 1, k-Winners-TakeAll network and readout), which is linked to the filtered pool5 unitsof randomized AlexNet. The randomized AlexNet corresponds to theearly visual pathway of infants and functions as an image abstractor,

﻿BMC Neurosci 2020, 21(Suppl 1):54

where its weights are randomly initialized and fixed. The connection
weights between the readout and pool5 units can be updated by Hebbian plasticity, but because the DU continuously inhibits the readout,
the plasticity was blocked initially. However, when the temporal difference of response becomes below a certain threshold (which meansthat the same object was consistently detected), the DU stops the inhibition, and connections between the ensemble of pool5 units (highly
activated for that object) and the readout are strengthened. During
the test session, we can identify the category of the given test images
by simply choosing the readout that shows the highest response. To
validate the model performance, we made a sequence of images by
sorting the CIFAR-10 dataset by categories, which mimics the temporal continuity of the natural scene. The model was trained by thedesigned image sequence, and tested by a separate validation set. As
a result, we achieved 35% classification accuracy, which is significantly
higher than the chance level of 10%. Based on the present findings,
we suggest a biologically-plausible mechanism of object categorization with no supervision, and we believe that our model can explain
how the visual function arises in the early stages of the brain without
supervised learning.

Fig. 1 The overall structure of the model. The model consists of therandomly initialized AlexNet (image abstractor), temporal kernel, dif‑ferential unit (DU), k-Winner-Take-All network and readout
Acknowledgements: This work was supported byNational Research
Foundation of Korea (No.2019M3E5D2A01058328,2019R1A2C406986
3).
References
1. Rakison DH, Yermolayeva Y. Infant categorization. Wiley Interdisciplinary
Reviews: Cognitive Science. 2010; 1(6): 894-905.
2. Wiskott L, Sejnowski TJ. Slow feature analysis: Unsupervised learning ofinvariances. Neural computation. 2002; 14(4): 715-70.

P156
Analytical solution of linearized equations of the Morris‑Lecar
neuron model at large constant stimulation
Tatiana ­Zemskova1, Alexander ­Paraskevov2
1
Ecole Polytechnique, Palaiseau, France; 2National Research Centre“Kurchatov Institute”, Institute for Information Transmission Problems,
Moscow, Russia
Correspondence: Tatiana Zemskova (zemskova.ts@phystech.edu)
BMC Neuroscience 2020, 21(Suppl 1):P156
The Morris-Lecar model (MLM) [1] is a classical biophysical model of
spike generation by the neuron, which takes into account dynamics of voltage-dependent ion channels and realistically describes
the spike waveform. MLM predict that upon stimulation of the neuron with sufficiently large constant depolarizing current Istim, there
exists a finite interval of Istim values where periodic spike generation
occurs [2-4]. Numerical simulations show that the cessation of periodic generation of spikes above the upper boundary of this interval occurs through damping of the spike amplitude, arising with adelay inversely proportional to Istim value. In particular, the damped
dynamics can be divided into four successive stages: 1) minor primarydamping, which reflects a typical transient to stationary state, 2) plateau of nearly undamped periodic oscillations, which determines the

Page 89 of 123

aforementioned delay, 3) strong damping, and 4) reaching a constant
asymptotic value. As the last two stages resemble the well-known
exponentially-damped harmonic oscillations, we tackled to find ananalytical description for these stages [5].
First, we have linearized the MLM equations at the vicinity of thestationary asymptotic value of the neuronal potential. The resulting
equations have been then reduced to an inhomogeneous Volterra
integral equation of the 2nd kind. In turn, the latter has been transformed into an ordinary differential equation of the second order witha time-dependent coefficient at the first-order derivative. As this timedependence was just an exponential decay, we considered its asymptotic value and analytically solved the final equation. In order to verify
the analytical solution found, we have compared it with the numerical solution obtained using the standard MATLAB tools for systems ofordinary differential equations.
We have accurately shown that the linearized system of equations of
the MLM can be reduced to a standard equation of damped harmonic
oscillations for the neuron potential. Since all coefficients of this equation are explicitly expressed through the parameters of the original
MLM, one can directly (i.e. without any fitting) compare the numerical and analytical solutions for dynamics of the neuron potential at thestages of strong damping and reaching a constant asymptotic value.
The results allow a quantitative study of the applicability boundary of
linear stability analysis that implies exponential damping.
References
1. Morris C, Lecar H. Voltage oscillations in the barnacle giant muscle fiber.
Biophysical Journal 1981.; 35(1): 193-213.
2. Tateno T, Harsch A, Robinson HP. Threshold firing frequency–current
relationships of neurons in rat somatosensory cortex: type 1 and type 2
dynamics. Journal of Neurophysiology. 2004; 92(4): 2283-94.
3. Tsumoto K, Kitajima H, Yoshinaga T, Aihara K, Kawakami H. Bifurcations inMorris–Lecar neuron model. Neurocomputing. 2006; 69(4-6): 293-316.
4. Nguyen LH, Hong KS, Park S. Bifurcation control of the Morris-Lecar neuronmodel via a dynamic state-feedback control. Biological Cybernetics. 2012;
106(10): 587-594.
5. Paraskevov AV, Zemskova TS. Analytical solution of linearized equations of
the Morris-Lecar neuron model at large constant stimulation, bioRxiv. 2019.

P157
Mesoscopic spiking neuronal network model capturing the remote
activation of “epilepsy” focus
Dmitrii ­Zendrikov1, Alexander P­ araskevov2
1
University of Zurich, Institute of Neuroinformatics, Zurich, Switzerland;2
National Research Centre “Kurchatov Institute”, Institute for Information
Transmission Problems, Moscow, Russia
Correspondence: Dmitrii Zendrikov (dmitrii@ini.uzh.ch)
BMC Neuroscience 2020, 21(Suppl 1):P157
Spontaneous focal synchronization of collective spiking followed byinduced traveling waves can occur in the cortical sheet and in cultured planar neuronal networks. In the first case, it is focal epilepsy
leading to a seizure and, in the second, it is synchronization that originates from one of a few steady nucleation sites resulting in a so-called
population spike. Assuming functional similarity between the nucleation sites and non-lesional epileptic foci, the major unsolved issue inboth cases is that whether activation of the focus occurs inside it (i.e.,autonomously relative to the interaction with surrounding neuronaltissue) or from the outside. The “internal” scenario implies that thefocus spatially contains some pacemakers. In turn, several experimental findings indicate a complex spatially non-local activation of epileptic focus [1-4]. In modeling studies, we address the issue in order toverify the validity of this conclusion.
We use generative mechanistic model of planar neuronal network
exhibiting irregular spontaneous population spikes, which emerge
from a few spontaneously-formed stationary nucleation sites. The
model consists of leaky integrate-and-fire neurons connected by synapses with short-term plasticity, forming spatially-dependent “smallworld” network topology, where synaptic connection probability
decreases exponentially with the distance between neurons. Spiking

﻿BMC Neurosci 2020, 21(Suppl 1):54

activity in the network occurs due to some fraction of pacemaker neurons. Importantly, the spatial configuration of pacemaker neurons
was artificially engineered in order to resolve the above-mentioned
problem: all pacemakers were placed within a circular central spot
so that their spatial density was equal to the average density of neurons. Leaving the global dynamic regime unaffected, this spatial configuration crucially helps to clarify the activation process, visualizing of
which is hindered at spatially-uniform pacemaker distribution.
Extensive simulations [5] have shown that steady and spontaneous
nucleation sites of population spikes (i) can emerge in spatial regions,which are far away from the spot with pacemakers and (ii) can be activated even without direct links from pacemakers. The results demonstrate the principle possibility of external, or remote, activation ofa focal source of epileptic activity in the brain and favor the interpretation in the above-mentioned experimental findings. The suggested
deterministic model provides the means to study this network phenomenon systematically and reproducibly.
References
1. Schevon CA, et al. Cortical abnormalities in epilepsy revealed by local EEG
synchrony. Neuroimage. 2007; 35(1): 140-8.
2. Zaveri HP, et al. Localization-related epilepsy exhibits significant connectiv‑
ity away from the seizure-onset area. Neuroreport. 2009; 20(9): 891-5.
3. Bower MR, Stead M, Meyer FB, Marsh WR, Worrell GA. Spatiotemporal neu‑
ronal correlates of seizure generation in focal epilepsy. Epilepsia. 2012; 53(5):807-16.
4. Paz JT, Huguenard JR. Microcircuits and their interactions in epilepsy: is thefocus out of focus?. Nature Neuroscience. 2015; 18(3): 351.
5. Paraskevov AV, Zendrikov DK. Capturing remote activation of epilepsy
source?. bioRxiv. 2018; 355255.

P158
Controllability for nonlinear dynamic brain system under biologicalconstrains
Jiyoung ­Kang1, Hae‑Jeong ­Park2
1
Yonsei University, Center for Systems and Translational Brain Sciences,
Seoul, South Korea; 2Yonsei University College of Medicine, Departmentof Nuclear Medicine, Seoul, South Korea
Correspondence: Jiyoung Kang (jiyoungkang01@gmail.com)
BMC Neuroscience 2020, 21(Suppl 1):P158
Controllability of the brain system has been studied in the domain of
network science. However, most of studies on the controllability ofthe brain have not considered the nonlinear nature of the brain. In thepresent study, we suggest a computational framework to control thebrain system with a consideration of nonlinear brain dynamics. Our
framework is based on a hypothesis that a brain with a disease has
specific brain dynamics different from that of a normal brain and canbe analyzed by using an energy landscape analysis. For both normal
and abnormal brain systems, multistable activation states (attractors)
and transition rates were investigated by performing an energy landscape analysis based on a pairwise maximum entropy model. In thecurrent virtual framework, we simulated how dynamics of a disease
brain can be changed to that of the normal brain by external treatments under biological constrains. By doing this, we tried to find astrategy for optimal treatments that control the target brain to generate brain state dynamics similar to that of the healthy brain. We
assumed that the target brain changes not only at a treated region ortreated connectivity, but also it induces changes in the neighbors thatthe treated region interacts. By allowing changes in the neighborhood
in response to the treatment to a target region, we showed an optimal controllability that takes into account of the nonlinear responsesof the brain after treatment. We applied the current framework tofind a virtually optimal way of perturbing the network system of theschizophrenia to induced healthy state dynamics. We expect that thiscomputational framework for controllability would help treatment
planning for the nonlinear brain system, after empirical evaluation andvalidation.
Acknowledgments: This research was supported by Brain
Research Program and the Korea Research Fellowship Program

Page 90 of 123

through the National Research Foundation of Korea (NRF) fundedby the Ministry of Science and ICT (NRF-2017M3C7A1049051 andNRF-2017H1D3A1A01053094).
P159
Effective connectivity between the forebrain and tectumin the lavar zebra fish for opto‑motor behaviors
Dongmyeong ­Lee1, Junho S­ on1, Jinseok ­Eo2, Jiyoung K­ ang3, Hae‑Jeong­Park41
Yonsei University College of Medicine, Seoul, South Korea; 2Yonsei
University College of Medicine, Department of Radiology, Departmentof Psychiatry, Seoul, South Korea; 3Yonsei University, Center for Systems
and Translational Brain Sciences, Seoul, South Korea; 4Yonsei University
College of Medicine, Department of Nuclear Medicine, Seoul, South Korea
Correspondence: Dongmyeong Lee (dmyeong@inu.ac.kr)
BMC Neuroscience 2020, 21(Suppl 1):P159
The role of the forebrain of the zebra fish has not been clearly understood particularly during the opto-motor behaviors. Most studies
on the opto-motor behavior have been researched in terms of interactions between the retina and the tectum. However, the role ofthe forebrain has not been fully explored. In the current study, we
explored how the forebrain works during the zebra’s opto-moto
behavior by estimating the context-dependent effective connectivity
between the tectum and the forebrain using dynamic causal modelling (DCM) of the calcium signals. We hypothesized that the forebrain
plays a role in modulating the tectum as top-down process. For the
six lavar zebra fish (elavl3: H2B-GCaMP6f, 5~7dpf) during opto-motor
behavior [1], we applied principal component analysis to the calcium
imaging time series to identify neural modes that respond synchronously to the optic stimulus. As a result, three neural modes at theforebrain and two (left/right) neural modes at the tectum were identified and used to construct a functional circuitry for the zebra fish.
A DCM with a convolution-based dynamic neural state model and a
dynamic calcium ion concentration model for calcium signals [2] wasapplied to fit the time series of the five modes. Onsets of both stimuli
were assigned to the bilateral tectum to derive the system. The left
and right stimuli were used independently as two modulation inputs
to effective connectivity. Using DCM, we inverted effective connectivity among the fully connected nodes. For a group level analysis of
the direction-dependent effective connectivity, we used parametricempirical Bayesian analysis of the individual effective connectivity.
The neurons related to opto-moto behaviors in the forebrain were
not highly lateralized compared to those at the tectum. The left andright optic stimulation modulated the effective connectivity from theforebrain to the tectum in a direction dependent way. When responding to a directional stimulus, the mode in the tectum suppresses thecontra-lateral neural mode. Our computational modelling results suggest the active involvement of the forebrain in modulating top-down
effective connectivity for opto-motor behaviors.
References
1. Chen X, et al. Brain-wide organization of neuronal activity and convergent
sensorimotor transformations in larval zebrafish. Neuron. 2018; 100(4):876-90.
2. Jung K, Kang J, Chung S, Park HJ. Dynamic causal modeling for calcium
imaging: Exploration of differential effective connectivity for sensory pro‑cessing in a barrel cortical column. NeuroImage. 2019; 201: 116008.

P160
Dynamic causal modeling of single cell level activities observedin the calcium imaging
Jinseok ­Eo1, Jiyoung ­Kang2, Dongmyeong L­ ee3, Junho S­ on3, Hae‑Jeong­Park41
Yonsei University, Seoul, South Korea; 2Yonsei University, Center
for Systems and Translational Brain Sciences, Seoul, South Korea; 3Yonsei
University College of Medicine, Seoul, South Korea; 4Yonsei University
College of Medicine, Department of Nuclear Medicine, Seoul, South Korea
Correspondence: Jinseok Eo (jinseok007@naver.com)
BMC Neuroscience 2020, 21(Suppl 1):P160

﻿BMC Neurosci 2020, 21(Suppl 1):54

Multi-photon calcium imaging (CaI) makes it possible to analyze distributed neural activity in the neuronal level. Most studies using CaI
have analyzed the neural system in terms of functional connectivity,
i.e., temporal synchrony among nodes, which lacks information on theasymmetric interactions, called effective connectivity. Recent computational modelling techniques have been introduced to infer effective
connectivity among neural populations using dynamic causal modelling (DCM) [1,2]. These studies have used a firing-rate based neuralstate dynamic model, combined with a calcium ion kinetic model in
the neural population level, which is not appropriate to model neural interactions among individual neurons. In the single neuron level,a quadratic gaussian integrate-and-fire neural state model (QGIF) in
combination with a calcium kinetic equation was proposed to fit CaI
at a single neuron [3]. To make it applicable to exploring interactions
among multiple neurons, we extended the previous model of [3] to a
general circuit with multi-nodes in the DCM framework. We utilizeda QGIF model for a single neuronal activity, with conductance basedneural connectivity (synaptic conductances are approximated withalpha function), and a CaI state dynamic equation. Bayesian model
optimization is applied to find optimal model parameters (e.g., effective connectivity) using a variational expectation maximization
scheme implemented in the DCM. We confirmed the reliability of the
proposed modeling and model inversion process using simulation
experiments. We applied the proposed method to explore effective
connectivity among neural cells from the neural activity observed in
CaI at the rodent’s barrel cortex during successful and failed whisking. The results suggest the plausibility of the proposed method in theanalysis of neural interactions observed in the CaI in the neuron level.
References
1. Jung K, Kang J, Chung S, Park HJ. Dynamic causal modeling for calcium
imaging: Exploration of differential effective connectivity for sensory pro‑cessing in a barrel cortical column. NeuroImage. 2019; 201: 116008.
2. Rosch RE, Hunter PR, Baldeweg T, Friston KJ, Meyer MP. Calcium imaging
and dynamic causal modelling reveal brain-wide changes in effective con‑nectivity and synaptic dynamics during epileptic seizures. PLoS Computa‑
tional Biology. 2018; 14(8): e1006375.
3. Rahmati V, Kirmse K, Marković D, Holthoff K, Kiebel SJ. Inferring neuronal
dynamics from calcium imaging data using biophysical models and Bayes‑
ian inference. PLoS Computational Biology. 2016; 12(2): e1004736.

P161
Less is more: a new computational approach for analyzing EEG
for brain computer interface applications
Andi ­Partovi1, Mehrshad H­ osseini2, Milad ­Soleimani2, Sahand­Sadeghpoor2, Erfan Habibi Panah ­Fard2, Kiana L­ iaghat2, Soroush ­Ziaee2,
Farhad ­Goodarzy3
1
The University of Melbourne, Biomedical Engineering, Melbourne,
Australia; 2KNT University of Technology, Tehran, Iran; 3The Universityof Melbourne, Medicine, Dentistry and Health Sciences, Melbourne,
Australia
Correspondence: Farhad Goodarzy (goodarzy@unimelb.edu.au)
BMC Neuroscience 2020, 21(Suppl 1):P161
Brain computer interfaces (BCI) are amongst the exhilarating applications of computational neuroscience and have been increasingly the
focus of research around the world. Currently, abundant effort in BCI
research is devoted to offline analysis of recorded data to achieve
higher accuracies in decoding [1]. Although this has led to the development of new methods and algorithms, the problem of online
decoding of subject intentions remains a challenging one [2]. One of
the restrictive steps of BCI design is the need for complex preprocessing steps required to extract features from the recorded signals to be
used by the classifiers to distinguish intentions of the subject [1]. The
other hindering factor is the variability of the recorded signals. EEG
recordings use between 20-128 electrodes at sampling rates of 250 up
to 1KHz for BCI applications. This data is recorded from the whole brain
and in/between subject(s) variability intensifies the problem even further. This problem is currently mitigated through manual and carefulfeature engineering steps and tweaking of classifier parameters.

Page 91 of 123

We are proposing to reduce the complexity of the architecture by 1)
using only raw recorded signals with no preprocessing, 2) reducing
the number of channels used for classification and 3) a single convolutional neural network (CNN) to be used for classification amongst all
subjects. We have limited our preliminary results to EEG signal analysis of a left/right/rest motor imagery task as this is the most popularsignal used in BCI applications. We have previously shown [3] that our
proposed CNN can reliably decode intentions utilizing same architecture for multiple subjects. Here, we are extending our method to
4 new subjects and show that drastically reducing channels has insignificant effect on decoding results. We have also expanded our decoding results to 3-class classification and obtained the same decoding
accuracies by only using a few channels for classification. Our results
are summarized in Table 1.
Table 1 Summary of results
2-Class

3-Class# of electrodes

all

10

2

all

10

3

S1 (64)

84

84

72

60

60

58

S2 (64)

89

90

81

68

66

60

S3 (38)

90

88

80

73

74

70

S4 (22)

86

86

78

82

80

75

Our results show that drastically reducing the complexity of data,
can still yield comparable performance while using a single decoder
for multiple subjects. Since the choice of the channels for decoding isbased on the mental task, one can envision the use of these methods
to create practical, reliable online BCI solutions. Also using raw data
for analysis and the use of a single architecture to classify all subjects
allows for a hardware to be designed to even further improves efficiency of the system.
Acknowledgments: We thank NHMRC for funding Goodarzy F. withthe project grant.
References
1. Lotte F, et al. A review of classification algorithms for EEG-based brain–com‑
puter interfaces: a 10 year update. Journal of Neural Engineering. 2018;15(3): 031005.
2. Movahedi F, Coyle JL, Sejdić E. Deep belief networks for electroencephalog‑
raphy: A review of recent contributions and future outlooks. IEEE Journal of
Biomedical and Health Informatics. 2017; 22(3): 642-52.
3. Partovi A, et al. A Convolutional Neural Network Model for Decoding EEG
signals in a Hand-Squeeze Task. In 8th International Winter Conference on
Brain-Computer Interface. 2020; 1-6.

P162
Approximative models for enabling multi‑scale simulations
Mikko Lehtimäki1, Marja‑Leena ­Linne2, Lassi ­Paunonen3
1
Tampere University, Faculty of Medicine and Health Technology,
Tampere, Finland; 2Tampere University, Medicine and Health Sciences,
Tampere, Finland; 3Tampere University, Faculty of Information Technology
and Communication Sciences, Tampere, Finland
Correspondence: Mikko Lehtimäki (lehtimaki.mikko@gmail.com)
BMC Neuroscience 2020, 21(Suppl 1):P162
In computational neuroscience there is a great demand to incorporate
more molecular and cellular level detail into mathematical models ofneuronal networks. This is deemed necessary in order to recreate phenomena such as learning, memory and behavior in silico. However,numerical simulation of such multi-scale models is resource intensive, if not impossible. This problem has been partially overcome byusing simplified synapse, neuron and population models that replacebiological variables and mechanisms from the models with phenomenological descriptions. While useful, this approach causes information loss that might diminish the value of such models, as the variables
might lack biological meaning.

﻿BMC Neurosci 2020, 21(Suppl 1):54

Page 92 of 123

In this study we present approximation as an alternative to simplification. By using mathematical model order reduction (MOR) methods
approximations can be derived algorithmically. Here we compute
reduced models with the Discrete Empirical Interpolation Method(DEIM) [1] algorithm along with its advanced variants. The appeal ofthese methods is that there is no need to linearize the model, makeassumptions of the system behavior or discard any variables. A
reduced model can be simulated efficiently in a low-dimensional subspace where a smaller number of equations needs to be solved. An
approximation of the original high-dimensional model can be reconstructed at any time. The acceleration in simulation time gained thisway requires no special hardware and can be readily implemented inany programming language.
We discuss results from approximating three nonlinear systems;
chemical reactions in the synapse, a compartmental neuronal network and a multi-dimensional mean-field model [2-4]. We have madethe code to approximate the mean-field model open source [https://github.com/Mikkolehtimaki/neuro-mor]. We demonstrate the valueof reduced models in computational neuroscience and explain thepros and cons of several different reduction methods with regards tothe above models. Especially implementation of mathematical modelorder reduction algorithms in neuronal simulators and using reduced
models in neuromorphic hardware are potential applications of thesemethods for enabling multi-scale simulations of brain activity.
Acknowledgements: M.L. is supported by TUNI Graduate School, M.L.L by Academy of Finland grant 297893 and L.P. by grants 298182 and
310489. This research has received funding from the European Union’s
Horizon 2020 Framework Programme for Research and Innovation
under the Specific Grant Agreement No. 785907 (Human Brain ProjectSGA2).

The presence of presynaptic-dependent postsynaptic signatures may
serve to differentiate between correlation and causation.
We address these issues by combining biophysical modeling, numerical simulations and dynamical systems tools. We extend the framework developed in [5] to describe an ultra-precise monosynaptic
connection by including ionic currents with representative dynamics.
The model consists of two neurons receiving uncorrelated noise where
the presynaptic neuron sends a fixed number of synaptic events to thepostsynaptic neuron. CCF is computed as an average over a numberof trials. We consider a number of scenarios corresponding to different levels of the ionic currents, their nonlinearities and effective time
constants.
Our results show the emergence of an additional slower and wider
temporal relationship, after the sharp peak in the CCF. This relationship depends on the dynamic properties present in the postsynaptic
neuron model (ionic currents) in the subthreshold regime. Upon a synaptic event, if the neuron is not on the verge of a spike, it will increase
its voltage following some dynamics, which depends particularly on
the effective time constant, and which will be reflected in the CCF.
This temporal relationship may not be clearly observed in experiments due to a high signal-to-noise ratio and is not capturing external
modulation effects. We explain this effect using a phase-plane description where we capture the spike-initiation nonlinearity in terms ofnullclines and connect it to the CCF.
We expect that these results will help the identification of monosynaptic connections between different neuron types, in particular, thoseconnections among neurons from different classes.

References
1. Chaturantabut S, Sorensen DC. Nonlinear model reduction via discrete
empirical interpolation. SIAM Journal on Scientific Computing. 2010; 32(5):2737-64.
2. Lehtimäki M, Paunonen L, Pohjolainen S, Linne ML. Order reduction for a
signaling pathway model of neuronal synaptic plasticity. IFAC-PapersOn‑
Line. 2017; 50(1): 7687-92.
3. Lehtimäki M, Paunonen L, Linne ML. Projection-based order reduction of anonlinear biophysical neuronal network model. In IEEE 58th Conference on
Decision and Control. 2019; 1-6.
4. Lehtimäki M, Seppälä I, Paunonen L, Linne ML. Accelerated Simulation of
a Neuronal Population via Mathematical Model Order Reduction. In 2ndIEEE International Conference on Artificial Intelligence Circuits and Systems.
2020; 118-122.

References
1. English DF, et al. Pyramidal cell-interneuron circuit architecture and dynam‑
ics in hippocampal networks. Neuron. 2017; 96(2): 505-20.
2. Constantinidis C, Goldman-Rakic PS. Correlated discharges among puta‑tive pyramidal neurons and interneurons in the primate prefrontal cortex.
Journal of Neurophysiology. 2002; 88(6): 3487-97.
3. Yu J, Ferster D. Functional coupling from simple to complex cells in the visu‑
ally driven cortical circuit. Journal of Neuroscience. 2013; 33(48): 18855-66.
4. Ostojic S, Brunel N, Hakim V. How connectivity, background activity, andsynaptic properties shape the cross-correlation between spike trains. Jour‑nal of Neuroscience. 2009; 29(33): 10234-53.
5. Platkiewicz J, Saccomano Z, McKenzie S, English D, Amarasingham A. Mon‑osynaptic inference via finely-timed spikes. arXiv preprint arXiv:1909.08553.
2019.

P163
Biophysics and dynamics shape the cross‑correlation propertiesof monosynaptic connections
Horacio ­Rotstein1, Rodrigo ­Pena21
New Jersey Institute of Technology, Federated Department of Biological
Sciences, NJIT / Rutgers University, Newark, New Jersey, United Statesof America; 2New Jersey Institute of Technology, Federated Departmentof Biological Sciences, Newark, New Jersey, United States of America
Correspondence: Horacio Rotstein (horacio@njit.edu)
BMC Neuroscience 2020, 21(Suppl 1):P163

P164
Using entropy to compute phase transitions of large networksof neurons
Wei Qin, Andre Peterson
The University of Melbourne, Biomedical Engineering, Melbourne,
Australia
Correspondence: Wei Qin (wqin1@student.unimelb.edu.au)
BMC Neuroscience 2020, 21(Suppl 1):P164

Finely-timed spike relationships provide knowledge of putative monosynaptic connections in populations of neurons. Recent experiments
involving hippocampal in vivo recordings were able to demonstratesuch a relationship by means of the cross-correlation function (CCF)[1,2]. A sharp peak within a few milliseconds in the CCF indicates thepresence of a connection. Yet, neurons that are not monosynapticallyconnected can emit spikes within some short temporal distance as aresult of network co-modulation [3], usually in the form of backgroundnoise. In general, there is an agreement that CCFs are shaped by either
the connectivity, synaptic properties, or background activity [4]. However, it remains unclear whether and how the postsynaptic intrinsic
neuronal properties such as the ionic currents’ nonlinearities and time
constants shape the CCFs between pre- and postsynaptic neurons.

Acknowledgments: This work was supported by the National Science
Foundation grant DMS-1608077 (HGR).

Phase transitions are often used to describe pathological brain statetransitions observed in neurological diseases such as epilepsy. Typically, the study of the dynamics of neurons that are nonlinearly coupled and have complex network structures is done via large scale
numerical simulations, which are mathematically intractable. Otherwise, analysis is performed where the network structure is averaged
over and made spatially homogeneous. For a networked nonlinear
dynamical system, phase transitions or bifurcations are computed via
changes in the local stability around the fixed points. However, in sucha system it is very difficult to compute the fixed points as the dimensionality of the system becomes large due to nested nonlinearities.
We know from numerical simulations that the system becomes ‘chaotic’ [1] as the order parameter (variance of the connectivity matrix) is
increased and that microscopically this phase transition corresponds
to an exponential increase in the number of fixed points [2]. This
phase transition has also been computed for heterogeneous network

﻿BMC Neurosci 2020, 21(Suppl 1):54

structures such as Dale’s law [3]. However, it is very difficult to numerically verify these results. To quantify the change in network dynamics, we compute the entropy, a quantity which describes the numberof states or information in a system. We show in this paper that the
Network entropy (NE), a term derived from Shannon Entropy, can beused as a numerical indicator of a change in the number of equilibria. Hence, it is also a numerical method to estimate the change in stability of a network. It is developed via a Symbolic Dynamic approachbased on probability distributions of the system state, which provides
a measure of the number of states of the system.
In this paper, a first order neural model with a time-constant andinstantaneous synapses is networked. The network connectivities aredescribed by a random matrix with mean and variance. Dale’s law can be
integrated into the model by changing the connectivity matrix. We estimate the stability of a network via measuring the entropy of the network
states using numerical simulations with different realisations of the connectivity matrix. The result demonstrates that the transition points fromthe analytical results for each case coincided with the measured NEs. It
suggests the NEs can be used in numerical simulations to estimate thechanges in the number of the fixed points, a.k.a. the phase transitions.
This work provides a novel approach to estimate the network states andphase transitions via numerical simulations. Future works are needed
to discover the mathematical relationship between the fixed points
and entropy. Furthermore, it is interesting to use entropy to predict the
dynamical behaviours of a system in an early stage. The discovery can beused to understand the brain state transitions and for the early diagnosisof neurological diseases, such as epilepsy.
References
1. Stern M, Sompolinsky H, Abbott LF. Dynamics of random neural networks
with bistable units. Physical Review E. 2014; 90(6): 062710.
2. Wainrib G, Touboul J. Topological and dynamical complexity of randomneural networks. Physical Review Letters. 2013;110(11): 118101.
3. Ipsen JR, Peterson AD. Consequences of Dale’s law on the stability-com‑
plexity relationship of random neural networks. 2019; arXiv: 1907.07293.

P165
Using dynamical mean field theory to study synchronizationin the brain.
Isabelle ­Harris1, Anthony ­Burkitt2, Hamish M­ effin3, Andre P­ eterson2
1
The University of Melbourne, Department of Medicine, Melbourne,
Australia; 2The University of Melbourne, Department of Biomedical
Engineering, Melbourne, Australia; 3National Vision Research Institute,
Melbourne, Australia
Correspondence: Isabelle Harris (iharris@student.unimelb.edu.au)
BMC Neuroscience 2020, 21(Suppl 1):P165
This work focuses on the dynamics of large networks of neurons, andparticularly aims to study the effects of brain structures and functions on state transitions, such as those found in epilepsy. We derivea measure of synchronization based on network structures to identify
a state change in the system, specifically, the state change between a
non-seizure state and a seizure state.
Currently, the modelling framework in theoretical neuroscience focuses
on using dynamical systems analysis of neural field models, and numerical simulations to disentangle the influences of structure and function
on brain dynamics. However, these models and methods use continuous spatial averages of network connectivity. In this work, we are particularly interested in the spatial structures and functions that inducea state transition from a state of intrinsically fluctuating and complex
activity (non-seizure state), to a state of synchronisation and simplistic
activity (seizure state). Using a first order neural network model with adiscrete spatial field given by a coupling matrix, a set of self-consistentequations that describe the nature of the activity of network structureswith populations of neurons, can be derived using dynamical mean field
theory [1]. This set of self-consistent equations can be solved semi-analytically, and we use these solutions to derive a measure of synchronisation and hence, excitability: the coefficient of variation (CV). The CV isa common measure of synchrony used in theoretical neuroscience [2],but it has also been recently used in the analysis of animal model data[3]. We use the derived expression of CV to show that under certain

Page 93 of 123

network structure and function conditions there exists a transition froma state of intrinsic fluctuating activity to a state of synchronization andhyper-excitation. This state transition is analogous to the transition froma non-seizure to a seizure state. Furthermore, we calculate the CV fornumerically simulated time series outputs of the model used, to compare and verify our analytical expression of CV.
This measure of CV defined in this work is dependent on the network
structures that are thought to be instrumental in initiating seizuretransitions in the brain. As seizure events are typically due to pathological brain structures and function, we have used the CV as a theoretical measure of whether particular brain structures are susceptible
to epileptic state transitions in the brain.
References
1. Mastrogiuseppe F, Ostojic S. Intrinsically-generated fluctuating activity inexcitatory-inhibitory networks. PLoS Computational Biology. 2017; 13(4):e1005498.
2. Meffin H, Burkitt AN, Grayden DB. (2004). An analytical model for the ‘large,
fluctuating synaptic conductance state’ typical of neocortical neurons
in vivo. Journal of Computational Neuroscience. 2004; 16(2): 159–175.
3. Fontenele AJ, et al. Criticality between cortical states. Physical Review Let‑ters. 2019; 122(20): 208101.

P166
Compensating method for the lack of connection on topographic
neuron network edge
Cecilia ­Romaro1, Antonio C R­ oque1, Jose R C P­ iqueira2
1
University of Sao Paulo, Physics Department, Sao Paulo, Brazil; 2Escola
Politecnica, Department of Telecommunications and Control Engineering,
Sao Paulo, Brazil
Correspondence: Cecilia Romaro (cecilia.romaro@usp.br)
BMC Neuroscience 2020, 21(Suppl 1):P166
Studying the dynamics of a neuron network has been a challenge tocomputational Neuroscience [1,2]. Doing so in a neuron network with
topographic organization is even more demanding due to the boundary condition, i.e. the interruption of the topographic pattern of connection in network edges, which changes network boundary activity.
The neurons on the edge of the network present underside behavior due to a lack (or excess) of connections and a torus solution may
introduce undesired oscillations. Facing such strain, this work presents
a method based on mean field potential (i.e. first and second-order
statistics of neuron network dynamics) to sustain neuron boundary
activity – such as neurons on the core of the layer network activity –without introducing an oscillation component.
This method is based on the rescaling presented on CNS previous
works and consists of:
Step 1: Calculating the scale factor k_i for any neuron i in network as
follows: for a neuron i, k_i is given by the average of total number ofconnections received divided by the average of total number of connections that would be received IF the network had no boundaries –was a set of infinity neurons;
Step 2: Increasing the synaptic weights by dividing them by the squareroot of the scale factor;
Step 3: Providing each cell with a DC input current with a value corresponding to the total input lost due to network edge (boundary cut).
In essence, the boundary correction method numerically estimates
the normalized density function of connection on the first step, thenweights each neuron connection based on this density, and finally balances the threshold to grant the neuron/layer activity. This method
was successfully applied on consolidated models such as Brunel [1]and PD [2], among others.
Firstly the models were reimplemented and the results were reproduced. Secondly, a topographic patter of connection was introduced
to the models including the consideration that neurons near eachother have a higher probability of connection then those further fromeach other. A different activity rises on both network boundary neurons and sometimes on core neurons. This method was applied andthe activities were driven back to the original ones.
The algorithmic of rescaling method can be found in any one of example-application available in GitHub (https://github.com/ceciliaromaro/

﻿BMC Neurosci 2020, 21(Suppl 1):54

Page 94 of 123

recoup-the-first-and-second-order-statistics-of-neuron-networkdynamics)
Acknowledgements: This work was produced as part of the activities
of FAPESP Research, Disseminations and Innovation Center for Neuromathematics (Grant 2013/07699-0, S. Paulo Research Foundation).

and Mental Health, Toronto, Canada; 3University of Toronto, Instituteof Biomaterials and Biomedical Engineering, Neurosciences and Mental
Health & Department of Physiology, Toronto, Canada
Correspondence: Nooshin Abdollahi (nooshin.abdollahi@mail.utoronto.ca)
BMC Neuroscience 2020, 21(Suppl 1):P168

References
1. Brunel N. Dynamics of sparsely connected networks of excitatory andinhibitory spiking neurons. Journal of Computational Neuroscience. 2000;
8(3): 183–208
2. Potjans TC and Diesmann M. The cell-type specific cortical microcircuit:relating structure and activity in a full-scale spiking network model. Cerebral
Cortex. 2014; 24: 785-806.

Spikes are usually initiated at the axon initial segment (AIS), the most
excitable site of a neuron. Yet other regions of the neuron are also
excitable; indeed, axonal excitability is critical for spike propagation.
While there are many studies on somatic and dendritic excitability,
axon excitability has yet to be thoroughly investigated in most neurons
because the small size of the axon precludes most experiments. There
are some recordings from the cut end of axons (i.e. blebs) suggestingthat axons do not spike repetitively during sustained depolarization
but, instead, spike only at the onset of abrupt depolarization, consistent with class 3 excitability. However, it remains unclear whether transient spiking accurately reflects axon excitability or is an artifact of axon
damage. Using a novel optogenetic approach, recent experimentsfrom our lab have shown that axon does indeed have class 3 excitability. Although the optogenetic method is less invasive than bleb recordings, it still has some limitations that necessitated simulations in orderto definitively interpret the experimental results. I have built a multicompartment model of a pyramidal neuron with a detailed myelinatedaxon that reproduces the observed experimental data collected in ourlab. The model has helped us confirm the site of spike initiation basedon the shape (kinkiness) of spikes recorded in the soma. Simulations
also confirmed that even when targeting the axon for photostimulation, a small degree of stray light can hit the dendrites and evoked
spikes in the AIS. The results ultimately confirm that unlike spike initiation in the AIS, which relies on class 1 excitability, spike propagation in
the axon occurs on the basis of class 3 excitability (Fig. 1).

P167
Modelling ipRGC‑influenced light response on circadian phase,
melatonin suppression and subjective sleepiness.
Tahereh ­Tekieh1, Peter R­ obinson2, Steven L­ ockley3, Stephan M­ cCloskey2,
M S ­Zobaer2, Svetlana ­Postnova2
1
The University of Sydney, Sydney, Australia; 2The University of Sydney,
School of Physics, Sydney, Australia; 3Monash University, Turner Institutefor Brain and Mental Health, School of Psychological Sciences, Melbourne,
Australia
Correspondence: Tahereh Tekieh (tahereh.tekieh@sydney.edu.au)
BMC Neuroscience 2020, 21(Suppl 1):P32
A physiologically-based model of arousal dynamics is extended toincorporate the spectral effects of light (as an input to the model) on
the circadian rhythms, melatonin dynamics and subjective sleepiness. Doing this, photopic illuminance in the model is replaced withmelanopic irradiance which, reflects the role of melanopsin, a photopigment expressed in ipRGCs (intrinsically photosensitive retinal
ganglion cells). Melanopsin-expressing ipRGCs are the primary cells
in the retina mediating the effect of light to different non-visual
related brain regions. Melanopsins are short wavelength sensitive
and their main target is the circadian clock located in suprachiasmatic
nuclei (SCN), with output signals regulating sleep/wake cycles, alertness, and hormone secretion. The melanopic irradiance is thus usedas the light input to the model, which affects the dynamic circadianoscillator, melatonin (hormone produced in pineal gland) profileand sleepiness. The dynamic circadian oscillator is extended according to the melanopic irradiance definition and tested against experimental circadian phase dose- and phase-response data. The function
which demonstrates melatonin suppression in presence of light is
re-calibrated against melatonin dose-response data for monochromatic and polychromatic light sources. A new light-dependent termis then introduced into the homeostatic weight component of subjective sleepiness to represent the direct effect of light. The new term
responds dynamically to light and is calibrated against experimental
data with different light spectrums. The model predictions are compared to a total of 14 experimental studies containing 26 data sets for
14 different spectral light profiles. The extended melanopic modelshows an average reduction in prediction error relative to the model
used prior. Overall, incorporating melanopic irradiance allows simulation of wavelength-dependent responses to light observed in experiments and explains most of the observations. Models demonstrating
the effect of light on circadian dynamics, sleep, and sleepiness need touse ipRGC-influenced responses as a non-visual measure of light; e.g.,melanopic irradiance, instead of the traditionally used illuminancebased on the visual system.
P168
Spike initiation properties in the axon: simulations
in a biophysically detailed model
Nooshin ­Abdollahi1, Amin Kamaleddin ­Ezabadi2, Stephanie R­ atte2, Steve­Prescott3
1
Univeristy of Toronto, Institute of Biomaterials and Biomedical Engineering,
Toronto, Canada; 2The Hospital for Sick Children, University of Toronto,
Institute of Biomaterials and Biomedical Engineering, NeuroscienceFig. 1 A Experimental results. B Simulation results. Current injec‑tion and photostimulation of the soma evoked repetitive spiking,consistent with class 1 excitability. However, current injection andphotostimulation of the axon evoked a single spike at stimulus onset,
consistent with class 3 excitability

﻿BMC Neurosci 2020, 21(Suppl 1):54

P169
Entrainment of competitive threshold‑linear networks
Andrea ­Bel1, Horacio ­Rotstein2, Walter ­Reartes1
1
Universidad Nacional del Sur, Departamento de Matemática, Bahía Blanca,
Argentina; 2New Jersey Institute of Technology, Federated Departmentof Biological Sciences, NJIT / Rutgers University, Newark, New Jersey,
United States of America
Correspondence: Andrea Bel (andrea.bel@uns.edu.ar)
BMC Neuroscience 2020, 21(Suppl 1):P169
Neuronal oscillations are ubiquitous in the brain and emerge from the
combined activity of the participating neurons (or nodes), the connectivity and the network topology. Recent neurotechnological advances
have made it possible to interrogate neuronal circuits by perturbing
one or more of its nodes. The response to periodic inputs has beenused as a tool to identify the oscillatory properties of circuits andthe flow of information in networks. However, a general theory that
explains the underlying mechanisms and allows to make predictions islacking beyond the single neuron level.
Threshold-linear network (TLN) models describe the activity of connected nodes where the contribution of the connectivity terms is linear above some threshold value (typically zero), while the network is
disconnected below it. In their simplest description, the dynamics of
the individual nodes are one-dimensional and linear. When the nodes
in the network are neurons or neuronal populations, their activity canbe interpreted as the firing rate, and therefore the TLNs represent firing rate models [1].
Competitive threshold-linear networks (CTLNs) are a class of TLNs
where the connectivity weights are all negative and there are no selfconnections [2,3]. Inhibitory networks arise in many neuronal systems
and have been shown to underlie the generation of rhythmic activity
in cognition and motor behavior [4,5]. Despite their simplicity, TLNs
and CTLNs produce complex behavior including multistability, periodic, quasi-periodic and chaotic solutions [2,3,6].
In this work, we consider CTLNs with three or more nodes and cyclic
symmetry in which oscillatory solutions are observed. We first assume
that an external oscillatory input is added to one of the nodes and, by
defining a Poincaré map, we numerically study the response properties of the CTLN networks. We determine the ranges of input amplitude and frequency in which the CTLN is able to follow the input(1:1 entrainment). For this we define local and global entrainment
measures that convey different information. We then study how theentrainment properties of the CTLNs is affected by changes in (i) thetime scale of each node, (ii) the number of nodes in the network, and(iii) the strength of the inhibitory connections. Finally, we extend our
results to include other entrainment scenarios (e.g., 2:1) and other network topologies.
Acknowledgments: This work was supported by the Universidad
Nacional del Sur grant PGI 24/L113-2019 (AB, WR) and the National
Science Foundation grant DMS-1608077 (HGR).
References
1. Dayan P, Abbott LF. Theoretical neuroscience: computational and math‑ematical modeling of neural systems. The MIT Press. 2001.
2. Curto C, Morrison K. Pattern completion in symmetric threshold-linear
networks. Neural Computation. 2016; 28(12): 2825-52.
3. Morrison K, Degeratu A, Itskov V, Curto C. Diversity of emergent dynamics incompetitive threshold-linear networks: A preliminary report. arXiv preprint.
2016.
4. Arriaga M, Han EB. Dedicated hippocampal inhibitory networks for locomo‑tion and immobility. Journal of Neuroscience. 2017; 37(38): 9222-38.
5. Burke DA, Rotstein HG, Alvarez VA. Striatal local circuitry: a new framework
for lateral inhibition. Neuron. 2017; 96(2): 267-84.
6. Hahnloser RL. On the piecewise analysis of networks of linear thresholdneurons. Neural Networks. 1998; 11(4): 691-7.

Page 95 of 123

P170
Optically imaged map of orientation preferences in visual cortexof an Australian marsupial, the Tammar Wallaby Macropus eugenii.
­ lmasi2, Shi S­ un1, Shaun C­ loherty3, Hamish M­ effin4,
Young Jun J­ ung1, Ali A
Michael ­Ibbotson5, Molis ­Yunzab2, Sebastien ­Bauquier6, Marilyn R­ enfree7
1
The University of Melbourne, Department of Optometry and Vision
Sciences, Melbourne, Australia; 2National Vision Research Institute,
Melbourne, Australia; 3Monash University, Department of Physiology,
Melbourne, Australia; 4University of Melbourne, Biomedical Engineering,
Melbourne, Australia; 5University of Melbourne, Melbourne, Australia; 6The
University of Melbourne, Veterinary, Melbourne, Australia; 7The Universityof Melbourne, Biosciences, Melboune, Australia
Correspondence: Young Jun Jung (95jungy@gmail.com)
BMC Neuroscience 2020, 21(Suppl 1):P170
Orientation selectivity (OS) is a key feature of neurons in the mammalian primary visual cortex. In rodents and rabbits, these neurons
are randomly distributed across V1 while in cats and all primates, cellswith similar OS preferences cluster together into cortical columns.
Could it be that mammals with smaller primary visual cortices, relatively undifferentiated cortices or poor-resolution vision are restricted
to having salt-and-pepper OS maps? This is not true, because in graysquirrel, a highly visual rodent with good spatial resolution and a V1
that is highly differentiated, no clear functional organisation of OS
preferences exists in V1. We do not know yet why the maps coding OS
preferences are so radically different in rodents/rabbits compared tothe clear similarities across other mammalian visual systems.
Several models of cortical OS maps have been created incorporating Hebbian plasticity, intracortical interactions and the properties ofgrowing axons. But these models mainly focus on maps arising fromintracortical interactions. Here we focus on two factors contributing to
map formation: the topography of retina and phylogeny. One promising method of predicting whether or not a species has pinwheel maps
is to look at the central-to-peripheral ratio (CP ratio) of retinal cell density. We have found that animals with high CP ratios (>7) have orientation columns while those with low CP ratios (<4) have random OS
maps. We also investigated whether the development of OS maps is
influenced by a genetic factor related to phylogeny. A problem withthe existing literature is that OS maps have been investigated in onlya small subset of mammals. We suggest that the rodents and rabbits
might have lost the genetic capacity to develop OS maps, but that the
mammalian line may have originally evolved with the genetic capacity
to create orientation columns.
We studied a highly visual marsupial, the Tammar wallaby (Macropus
Eugenii), which represents a phylogenetically distinct branch of mammals for which the orientation map structure is unknown. The topography of RCC’s in wallabies is very similar to cats and primates. They
have a high density of RGC in the retinal specialization, indicated bya high CP ratio of 20. If orientation columns are the mammalian norm
and if species with high CP ratios have OS maps, we would predict theexistence of orientation columns in wallaby cortex. We used intrinsic optical imaging and multi-channel electrophysiology methods toexamine the functional organization of the wallaby cortex. We foundrobust OS in a high proportion of cells in the primary visual cortex andclear orientation columns similar to those found in cats and primates
but with bias towards vertical and horizontal preferences, suggesting
lifestyle-driven variations. The findings suggest that orientation columns are the norm and it might be that the rodents and rabbits are
unusual in terms of mammalian cortical architecture.
P171
Neural routing: determination of the fastest flows and fastest routes
in brain networks
Paula Sanz‑Leon1, Pierpaolo S­ orrentino2, Fabio B­ aselice3, Rosaria ­Rucco4,
Leonardo L ­Gollo5, James A ­Roberts6
1
University of Sydney, QIMR Berghofer, Brisbane, Australia; 2Aix‑Marseille/
Parthenope University of Naples, Marseille, France; 3Parthenope Universityof Naples, Biomedical Engineering, Naples, Italy; 4Parthenope Universityof Naples, Motor Sciences and Wellness, Naples, Italy; 5QIMR Berghofer/

﻿BMC Neurosci 2020, 21(Suppl 1):54

Monash University, Melbourne, Australia; 6QIMR Berghofer, Computational
Biology, Brisbane, Australia
Correspondence: Paula Sanz‑Leon (pmsl.academic@gmail.com)
BMC Neuroscience 2020, 21(Suppl 1):P171
Background: Large-scale brain networks [1] are characterized by
global and local functional and structural metrics [2,3] that have furthered our understanding of brain function [4]. These metrics are
based on the idea that information in a network flows along the shortest paths, either topological [5] or geometrical [6]. In this work, we
propose two functional network connectivity measures based on thephysical concept of flow [7,8], encompassing both geometrical andtemporal aspects of neural activity. We term the first measure modal
fastest flows, a time-averaged representation of the (fastest) flow lines
revealing portions of physical space along which a particle (e.g., wave
packet, information, spike) would travel at the maximal speed possible. The second measure, fastest neural routes, refers to a dense matrix
where the weights are the average transit time a packet of information
would take to travel from region ‘j’ to region ‘i’.
Method: (generation of fastest flow lines): We use our neural-flows
toolbox [8,9] to derive flow fields from source-reconstructed MEG
data. Fastest flow lines are then generated in 3 steps. First, we estimate flow vectors halfway between pairs of regions, transforming flow
vectors into an edge property rather than a nodal property. Second,
we trace a flow line starting from j, following the fastest flow to one
of its nearest neighbours within a small spherical region. This process
is done iteratively until reaching region i, and repeated for every possible region-pairwise combination. Flow lines are the sequences ofmaximal instantaneous speeds. Third, we average the values of eachflow line to produce a matrix of fastest flows between pairs of regions.
Results: (modal fastest flows and fastest neural routes): We time-averaged the modal fastest flows (MFF), into a single matrix of conduction
speeds. A comparison between functional connectivity derived from
MEG timeseries and our MFF, indicates high similarity, quantified withthe correlation matrix distance (cmd) [10] – 0 if matrices are equal, and1 if completely different – and in this case cmd = 0.18. Paths highlighted by flow lines are not necessarily the shortest (in physical distance). Thus, we combine MFF with pairwise distance metrics to derive
the fastest neural routes of information flow: the euclidean distance
between pairs of regions, and the flow line lengths. Distributionsof transit times are presented in Figure 1. Our MFF matrix combinedwith the fibre length of structural connectome, can be used as a first
approximation of heterogeneous time delays (s) in brain networks.

Fig. 1 Flows are estimated from source-reconstructed MEG and
defined at the sources. We then translate flows into edge propeties.
Then, we derive the matrix of fastest flows (m/s), which strongly
resembles traditional functional connectivity (Pearson’s correlation
coefficients). To estimate the fastest routes (ms), we use different
distance metrics

Page 96 of 123

References
1. Bullmore E, Sporns O. Complex brain networks: graph theoretical analysisof structural and functional systems. Nature Reviews Neuroscience. 2009;10(3): 186-98.
2. Rubinov M, Sporns O. Complex network measures of brain connectivity:
uses and interpretations. Neuroimage. 2010; 52(3): 1059-69.
3. Zalesky A, Fornito A, Bullmore ET. Network-based statistic: identifying differ‑ences in brain networks. Neuroimage. 2010; 53(4): 1197-207.
4. Fornito A, Zalesky A, Breakspear M. The connectomics of brain disorders.
Nature Reviews Neuroscience. 2015; 16(3): 159-72.
5. Fornito A, Zalesky A, Breakspear M. Graph analysis of the human connec‑
tome: promise, progress, and pitfalls. Neuroimage. 2013; 80: 426-44.
6. Seguin C, Van Den Heuvel MP, Zalesky A. Navigation of brain networks.
Proceedings of the National Academy of Sciences. 2018; 115(24): 6297-302.
7. Townsend RG, Gong P. Detection and analysis of spatiotemporal patterns inbrain activity. PLoS Computational Biology. 2018; 14(12): e1006643.
8. Sanz-Leon P, et al. Neuroimage toolbox [in prep].
9. Roberts JA, Gollo LL, Abeysuriya RG, Roberts G, Mitchell PB, Woolrich MW,
Breakspear M. Metastable brain waves. Nature Communications. 2019;10(1): 1-7.
10.
Herdin M, Czink N, Ozcelik H, Bonek E. Correlation matrix distance,a meaningful measure for evaluation of non-stationary MIMO channels. In
IEEE 61st Vehicular Technology Conference 2005; 1: 136-140.

P172
Spatiotemporal brain waves on resting‑state MEG data
James ­Pang1, Paula Sanz‑Leon2, Jonathan H­ adida3, Leonardo G­ ollo4, Mark
­Woolrich3, James A ­Roberts5
1
QIMR Berghofer Medical Research Institute, Genetics & Computational
Biology, Brisbane, Australia; 2QIMR Berghofer Medical Research Institute,
Brain Modeling Group, Brisbane, Australia; 3University of Oxford, Oxford
Centre for Human Brain Activity, Oxford, United Kingdom; 4Monash
University, Melbourne, Australia; 5QIMR Berghofer, Computational Biology,
Brisbane, Australia
Correspondence: James Pang (james.pang@qimrberghofer.edu.au)
BMC Neuroscience 2020, 21(Suppl 1):P172
Human brain function relies on the integration and coordination ofneuronal activity on multiple scales. Several works have revealed that
this is possible through spontaneous or evoked synchronization ofactivities of neural circuits in the brain, allowing spatially correlated
patterns that propagate in time to emerge, known as brain waves [1].
These brain waves have been observed in empirical macroscopic andmesoscopic measurements [2,3] and computational brain networkmodels [4], and have been shown to support various brain functions
such as visual perception [5]. However, brain waves are rarely investigated in resting-state experimental settings (i.e., without performing
an explicit task).
Here, we investigate large-scale spatiotemporal brain waves in resting-state human magnetoencephalography (MEG), which is becoming
a popular imaging modality due to its high spatial and temporal resolution, enabling more accurate analysis of macroscopic brain waves.
We use source reconstructed single-subject MEG data projected onto
the cortical surface and then decompose the signal into various typical frequency bands from delta to gamma. We find that organizedpatterns of waves traveling in space and time exist in the resting-statedata at the different frequency bands; an example of which is shownin the time snapshots of the alpha-filtered MEG signal in Figure 1A
and the corresponding phase maps in Figure 1B. Using the methodsin [3] for estimating instantaneous phase speeds, we find that, in general, waves with higher temporal frequencies tend to propagate more
rapidly (Fig. 1C). In addition, the speeds match those in the literatureusing other modalities (e.g., electrocorticography in [2]), suggesting
the reliability of our analyses. In summary, our work shows that macroscopic brain waves can be observed in resting-state MEG data evenfor a single subject, enabling the use of MEG alongside computationalmodels in future investigations on how brain waves affect and relate﻿BMC Neurosci 2020, 21(Suppl 1):54

to large-scale brain networks and the emergence of cognition andbehavior.

Page 97 of 123

P173
How energy constraints shape brain dynamics during hypoxia
and epileptic seizures
Shrey ­Dutta1, James A ­Roberts2
1
QIMR Berghofer Medical Research Institute, Faculty of Medicine, Brisbane,
Australia; 2QIMR Berghofer, Computational Biology, Brisbane, Australia
Correspondence: Shrey Dutta (shrey.dutta@qimrberghofer.edu.au)
BMC Neuroscience 2020, 21(Suppl 1):P173
The brain consumes 20% of the body’s energy, 10 times more than
predicted by its mass, which makes it highly sensitive to metabolic
disturbances [1]. Asphyxia and epileptic seizures disrupt energy andoxygen availability in the brain, leading to pathological activity inthe electroencephalogram (EEG) [2-4]. Modelling the bidirectional
relationship between brain activity and energy resources is crucial tounderstand brain disorders where metabolic disturbances are implicated. Most models of brain activity do not explicitly include metabolic variables and so are unable to address dynamical constraints onenergy resources. Here, we explore the roles of energy demand andenergy supply in Hodgkin-Huxley neurons augmented with the energy
resource dynamics of Na+/K+ pumps [4]. Using a small-scale networkof excitatory and inhibitory neurons, we show that during high energy
demand and low energy supply (extreme hypoxia) the model simulates scale-free burst suppression with asymmetric longer-duration
bursts (Fig. 1) – similar to empirical EEG from infants recovering fromhypoxia. During normal energy demand and low-to-moderate energy
supply the model generates several types of epileptic seizures (Fig. 1).
We also show multiple mechanisms for seizure terminations depending on the magnitude of hypoxia. Seizure termination during low
energy supply is due to depletion of local energy resources, while during moderate energy supply ion (Na+& K+) imbalances terminate theseizure. This suggests that seizure termination due to lack of energy isa potential mechanism for postictal generalised EEG suppression. Our
results unify burst suppression during hypoxia and epileptic seizures,and our modelling provides a general platform to study brain pathologies linked with metabolic disturbances.

Fig. 1 A Spatiotemporal variation of MEG signal filtered at the alphafrequency band. B Spatiotemporal phase maps of the signal in panel
A. C Kernel density distribution of wave propagation speeds at differ‑ent frequency bands
References
1. Muller L, Chavane F, Reynolds J, and Sejnowski TJ. Cortical travelling waves:
Mechanisms and computational principles. Nature Reviews Neuroscience.
2018; 19(5): 255–268.
2. Zhang H, Watrous AJ, Patel A, and Jacobs J. Theta and alpha oscillations aretraveling waves in the human neocortex. Neuron. 2018; 98: 1269–1281.
3. Rubino D, Robbins KA, and Hatsopoulos. Propagating waves mediate
information transfer in the motor cortex. Nature Neuroscience. 2006; 9:
1549–1557.
4. Roberts JA, et al. Metastable brain waves. Nature Communications. 2019;1056.
5. Zanos TP, Mineault PJ, Nasiotis KT, Guitton D, and Pack CC. A sensorimo‑
tor role for traveling waves in primate visual cortex. Neuron. 2015; 85(3):615–627.

Fig. 1 Unification in a single model of pathological patterns seen
during baby hypoxia and epileptic seizures. Middle: Phase transitions
between the regimes of hypoxia, healthy continuous activity andepilepsy in the energy demand-supply plane. Left: Model generated
scale-free burst suppression in hypoxia showing power law regimes
with asymmetric burst shape. Right: Model generated epileptic
seizures
References
1. Raichle ME. The brain’s dark energy. Science-New York Then Washington-.
2006; 314(5803): 1249.

﻿BMC Neurosci 2020, 21(Suppl 1):54

2. Roberts JA, Iyer KK, Finnigan S, Vanhatalo S, Breakspear M. Scale-free burst‑
ing in human cortex following hypoxia at birth. Journal of Neuroscience.
2014; 34(19): 6557-72.
3. Jirsa VK, Stacey WC, Quilichini PP, Ivanov AI, Bernard C. On the nature of
seizure dynamics. Brain. 2014; 137(8): 2210-30.
4. Wei Y, Ullah G, Ingram J, Schiff SJ. Oxygen and seizure dynamics: II. Compu‑
tational modeling. Journal of Neurophysiology. 2014; 112(2): 213-23.

P174
Modal‑polar representation of evoked response potentials
Rawan El‑Zghir1, Natasha G­ abay2, Peter R­ obinson3
1
The University of Sydney, Sydney, Australia; 2University of Sydney, Schoolof Physics/Complex Systems, Sydney, Australia; 3The University of Sydney,
School of Physics, Sydney, Australia
Correspondence: Rawan El‑Zghir (relz2030@uni.sydney.edu.au)
BMC Neuroscience 2020, 21(Suppl 1):P174
Event related potentials (ERPs) have grabbed the attention of neuroscientists as significant voltage fluctuations of the brain after a visual,auditory, or sensory stimulation to the nervous system. ERPs and correlation functions between signals at different points are key elements
for investigating cognitive features and signal processing of the brain.
To predict ERPs and correlation functions corresponding to distinct
arousal states, we use a corticothalamic neural field theory whichcontains physiologically based parameters corresponding to different
physical quantities. Within this framework, ERPs and correlation functions depend on transcendental equations which are not analyticallytractable. We approximate the temporal transfer function in termsof poles or resonances to derive formulas for the ERP and correlation
functions which greatly simplify their analytic forms. The dominantresonances of the system correspond to slow frequency, alpha, andbeta frequencies. Our calculations are based on contour integration
via the Cauchy-residue theorem that allows us to find explicit expressions for the ERP and correlation functions in terms of real and imaginary parts of the residues and poles. For each arousal state, we isolate
the different resonances of the system and find that the eyes-closed
wake state is distinguished by a more prominent alpha resonancecompared to the eyes-open wake state, as expected. We found that8 poles are sufficient to study the main dynamics of the system in theawake eyes-closed case (with around 3 % accuracy at the alpha peak)and 10 poles for the awake eyes-opened case (with around 2 % accuracy at the alpha peak). Similarly, we found that 8 poles are sufficient
to reproduce ERPs corresponding to REM and S1 sleep stages, whereas
only 6 poles are sufficient to study the dynamics of deeper sleep
stages (slow wave sleep). This framework provides a physiologicallybased tool which predicts ERPs and correlations corresponding to agiven transfer function.
P175
Analytic model for feature maps in the primary visual cortex
Xiaochen Li, Peter Robinson
University of Sydney, School of Physics, Sydney, Australia
Correspondence: Xiaochen Li (morninglxc@hotmail.com)
BMC Neuroscience 2020, 21(Suppl 1):P175
This study proposes a compact analytic model that describes the orientation preference (OP) and ocular dominance (OD) maps of the primary visual cortex (V1) in hypercolumns, within which OP and OD arearranged as pinwheels and stripes. This model consists of two parts:(i) an OP operator, which uses a linear combination of weighted partial derivatives to incorporate the small-scale local neuron sensitivity
to the preferred orientation of the visual inputs; and (ii) a receptive
field (RF) operator, which models the spatial RF structure of V1 simple cell, and it is derived from finding the neural activities at arbitrary
location with a directional anisotropic modulation of projections fromneighboring neurons at scales of a few tenths of a millimetre. The
parameters of the proposed OP-OD map model are tuned to maximize
the neural response at the desired OP, by matching the width of OP

Page 98 of 123

tuning curves with experimental results. Moreover, we find that theweights of the partial derivatives in OP operator do not significantly
affect the OP selectivity of the neuron, whereas the overall envelopeof the RF operator does. This agrees with Hubel and Wiesel’s prediction [1], that orientation tuning width of V1 simple cell is related to theelongation of its RF.
The simplified OP-OD map is used to provide inputs to neural field
theory (NFT) analysis of the approximate periodic OP-OD structure of
V1. This is done by decomposing the OP-OD map representation in
Fourier domain to generate a sparse set of Fourier coefficients. In addition, only the least number of coefficients, which are enough to preserves the basic spatial arrangement of OP-OD map, are passed to NFT
for investigating OP map related neural activities. The decompositionis also applied on more realistic OP maps generated from publishedmodels and its properties are discussed.
Reference
1. Hubel DH, Wiesel TN. Receptive fields, binocular interaction and functionalarchitecture in the cat’s visual cortex. The Journal of Physiology. 1962;
160(1): 106.

P176
Using deep convolutional neural networks to visualise the receptive
fields of high level visual cortical neurons
Brett ­Schmerl1, Declan R­ owley2, Elizabeth Z­ avitz2, Hsin‑Hao ­Yu2, Nicholas­Price2, Marcello R­ osa2
1
University of South Australia, School of Information Technology
and Mathematical Sciences, Adelaide, Australia; 2Monash University,
Department of Physiology; Australian Centre of Excellence for Integrative
Brain Function, Melbourne, Australia
Correspondence: Brett Schmerl (brett.schmerl@unisa.edu.au)
BMC Neuroscience 2020, 21(Suppl 1):P176
Understanding the image features that are encoded by neurons
throughout the hierarchy of visual cortical areas, particularly in areas
higher in the hierarchy that have more complex response properties
than in V1, is a challenging yet fundamental goal in visual neurosciencethat is often achieved by visualising their pattern of responses [1]. Visualising image features responsible for driving activity of individual units
in a hierarchical system used for visual processing for the purposes ofunderstanding the system’s functioning and information representationis also encountered in the study of deep convolutional neural networks.
In this study we train deep convolutional neural networks on spiking data recorded from individual neurons in a mid-tier visual area(the dorsomedial area, DM) of the anaesthetised marmoset monkey
whilst the animal is presented with changing patterns of spatiotemporally white noise [2]. We show that convolutional neural networks
are capable of learning statistically significant input-output relationships of these neurons and are thus able to perform classification of
the spiking behaviour of the neuron given the stimuli. Furthermore,
we applied deconvolutional techniques [3] used to visualise image
features encoded by the convolutional model, thus allowing visualisation of input image features that are significant to determining spiking behaviour, by proxy, of the neuron. A comparison between thefeatures recovered using this technique and those recovered by traditional methods of analysis is presented.
References
1. Jones JP, Palmer LA. The two-dimensional spatial structure of simple recep‑
tive fields in cat striate cortex. Journal of neurophysiology. 1987; 58(6):1187-211.
2. Lui LL, Bourne JA, Rosa MG. Functional response properties of neurons in
the dorsomedial visual area of New World monkeys (Callithrix jacchus).
Cerebral Cortex. 2006; 16(2): 162-77.
3. Zeiler MD, Fergus R. Visualizing and understanding convolutional networks.
In European conference on computer vision 2014; 818-833. Springer, Cham.

﻿BMC Neurosci 2020, 21(Suppl 1):54

P177
Modeling theta‑band resonance in a neocortical circuit
Rodrigo ­Pena1, Horacio ­Rotstein2
1
New Jersey Institute of Technology, Federated Department of Biological
Sciences, Newark, New Jersey, United States of America; 2New Jersey
Institute of Technology, Federated Department of Biological Sciences,
NJIT/ Rutgers University, Newark, New Jersey, United States of America
Correspondence: Rodrigo Pena (pena@njit.edu)
BMC Neuroscience 2020, 21(Suppl 1):P177
The neocortex is a brain region responsible for many higher-orderfunctions. Sensory signals arriving from different areas are integrated
into the neocortex. Oscillations at certain frequency bands are believed
to coordinate activity in many areas [1]. Resonance refers to the ability of a system to generate an amplified response if the input oscillation is tuned in a specific frequency band. Recent work showed therole of inhibition on the control of theta (4-11 Hz) oscillations throughresonance [2]. By using optogenetic activation interneurons (inhibitory)
induced theta-band-limited spiking in pyramidal (excitatory) neurons.
On the other side, direct optogenetic activation of pyramidal cells did
not generate any resonance pattern. Although it is clear that this phenomenon is neuron-specific, the network architecture responsible forthe observed resonance and how this is related to the correct gating of
the signals in such a network is currently unknown.
We address these issues by constructing a microcircuit biophysical minimal model of the neocortex using the Hodgkin-Huxley formalism [3].
We consider two pyramidal cells (PYR), one parvalbumin-positive (PV)interneuron, and one somatostatin-expressing (SOM) interneuron. Thesecells are interconnected with exponential decaying event-driven synapses where short-term depression/facilitation is present when appropriate [4]. Every cell spontaneously fires while receiving a noise input process
to simulate in vivo synaptic barrage [5]. We apply periodic currents withdifferent frequencies into PV cells and evaluate the PYR firing rate.
By applying oscillatory activation in PV, theta-band resonance was
induced in PYRs whereas direct activation of PYRs did not show resonance, as experimentally reported. First, our results highlight theimportance of post-inhibitory rebound in order to transfer signals
from PV to PYR cells. Secondly, our results show that SOMs, adaptation, depression, and facilitation regulate these resonance effects. We
explain these effects in terms of additional frequency filters that areadded to the system: adaptation and facilitation act as a high-passfilter while depression acts as a low-pass filter. SOM cells regulate thelow frequencies since they connect to other neurons through facilitation. In summary, when a current with a specific frequency is applied to
the PV cells, this input signal is processed by a combination of filters, inthe form of synapses and ionic currents, until a final output is produced
from PYR cells. Our results highlight the importance of the combined
activity of different neocortical cells in flexibly selecting inputs.
Acknowledgments: This work was supported by the National Science
Foundation grant DMS-1608077 (HGR).
References
1. Buzsaki, G. Rhythms of the Brain. Oxford University Press, 2006.
2. Stark E, Eichler R, Roux L, Fujisawa S, Rotstein HG, Buzsáki G. Inhibitioninduced theta resonance in cortical circuits. Neuron. 2013; 80(5): 1263-76.
3. Pospischil M, et al. Minimal Hodgkin–Huxley type models for different
classes of cortical and thalamic neurons. Biological Cybernetics. 2008; 99(45): 427-41.
4. Tsodyks M, Pawelzik K, Markram H. Neural networks with dynamic synapses.
Neural computation. 1998; 10(4): 821-35.
5. Destexhe A, Rudolph M, Fellous JM, Sejnowski TJ. Fluctuating synaptic
conductances recreate in vivo-like activity in neocortical neurons. Neurosci‑
ence. 2001; 107(1): 13-24.

P178
Neuronal resonance may not be apparent, but still present,
for realistic input signals using standard impedance measurements
Rodrigo ­Pena1, Ulises ­Chialva2, Horacio R­ otstein3
1
New Jersey Institute of Technology, Federated Department of Biological
Sciences, Newark, New Jersey, United States of America; 2Universidad
Nacional del Sur, Departmento de Matemática and CONICET, Bahía Blanca,

Page 99 of 123

Argentina; 3New Jersey Institute of Technology, Federated Departmentof Biological Sciences, NJIT/ Rutgers University, Newark, New Jersey,
United States of America
Correspondence: Rodrigo Pena (pena@njit.edu)
BMC Neuroscience 2020, 21(Suppl 1):P178
The impedance of a neuron reflects the frequency-dependent inputoutput relationships and is typically computed using sinusoidal
inputs [1]. Oscillatory currents with gradually increasing frequencies
or broadband noise have been used so the response is equally tested
at all frequencies [2,3]. The ability of a neuron to amplify its responseto specific non-zero input frequencies (resonance) has been questioned [4]. The question arises of whether the differences in the typeof response (low- vs. band-pass) can be ascribed to the type of input
and not necessarily to the details of the frequency content. Morespecifically, whether and how the use of different types of biophysically plausible periodic inputs (e.g., sinusoidal, synaptic-like) produce
qualitatively different frequency-dependent input-output curves. We
address these issues by injecting different types of inputs to biophysically plausible neuronal models including currents that are known to
produce subthreshold (membrane potential) resonance in response
to sinusoidal inputs. All input signals have the same amplitude andfrequency content, but the frequencies may come in “different order”(e.g., monotonically increasing, randomly distributed or “shuffled”).
The waveforms include sinusoidal, square-waves and synaptic-likefunctions. The impedance is computed either (i) as the ratio of theFourier transforms (FT) of the voltage (V) and the current (I) or (ii) by
the difference in the amplitude envelope responses normalized by the
input amplitude.
We show that if the inputs involve abrupt changes (e.g., square-waves,synaptic-like), transients contribute to the output signal, which qualitatively modify the impedance profile. This can cause a mismatch
between the impedance computed using (i) vs. (ii) given that the FT
captures these transients as higher harmonics (Fig. 1). Therefore, a resonance observed in the response pattern may not be captured by the
impedance using standard definitions and may require a more careful analysis. Furthermore, when input frequencies are presented in a
“shuffled” order, these transient effects produce responses with additional amplification to the higher frequency responses.

Fig. 1 Three different inputs applied to the linear model. From top
to bottom: oscillatory input, synaptic input, and pulse input. First
column: voltage series. Second column: voltage-envelopes. Third
column: Impedance profile computed from the Fourier transform
method and from the envelope method. The figure shows how
different the impedance profile can be even within the same input
frequency content
Our results highlight both the flexibility and limitations of the impedance profile measurements and demonstrate that resonance may be

﻿BMC Neurosci 2020, 21(Suppl 1):54

present in neuronal systems, but are not apparent unless one uses
the appropriate types of inputs and output metrics. Furthermore, ourresults question the ability of the standard impedance metric to makepredictions for a more general class of inputs.
Acknowledgments: This work was supported by the National Science
Foundation grant DMS-1608077 (HGR).
References
1. Hutcheon B, Yarom Y. Resonance, oscillation and the intrinsic frequency
preferences of neurons. Trends in Neurosciences. 2000; 23(5): 216-22.
2. Kispersky TJ, Fernandez FR, Economo MN, White JA. Spike resonance prop‑
erties in hippocampal O-LM cells are dependent on refractory dynamics.
Journal of Neuroscience. 2012; 32(11): 3637-51.
3. Schreiber S, Erchova I, Heinemann U, Herz AV. Subthreshold resonanceexplains the frequency-dependent integration of periodic as well as
random stimuli in the entorhinal cortex. Journal of Neurophysiology. 2004;
92(1): 408-15.
4. Zemankovics R, Káli S, Paulsen O, Freund TF, Hájos N. Differences in sub‑threshold resonance of hippocampal pyramidal cells and interneurons:
the role of h‐current and passive membrane characteristics. The Journal of
Physiology. 2010; 588(12): 2109-32.

P179
Synaptic short‑term plasticity and temporal filters: interplayof synaptic and postsynaptic dynamics
Yugarshi ­Mondal1, Rodrigo ­Pena2, Horacio R­ otstein3
1
Stony Brook University, Applied Math, Stony Brook, New York, United Statesof America; 2New Jersey Institute of Technology, Federated Departmentof Biological Sciences, Newark, New Jersey, United States of America; 3New
Jersey Institute of Technology/ Rutgers University, Federated Departmentof Biological Sciences, Newark, New Jersey, United States of America
Correspondence: Yugarshi Mondal (yugarshi.mondal@stonybrook.edu)
BMC Neuroscience 2020, 21(Suppl 1):P179
Short-term plasticity (STP) is the process by which a synapse changes
its efficacy in a history-dependent manner. It is hypothesized that
STP’s information processing capabilities are connected to the way it
implements temporal filtering in response to a sequence of presynaptic events [1,2]. Depression (STD) and facilitation (STF) refer to thedecrease and increase (e.g., Fig. 1A-B) of the efficacy in the synaptic
response to a presynaptic spike as compared to the previous one.
Sequences of presynaptic spikes create synaptic temporal filters (TFs)(e.g., Fig. 1). STD and STF lead to low-pass and high-pass (Figs. 1A- B)
TFs. The presence of both STD and STF may lead to band-pass TFs(Fig. 1C). These synaptic TFs are communicated to the postsynaptic
membrane potential. However, it is unknown whether the propertiesof the postsynaptic TFs (PSTFs) are inherited from the synaptic TFs orthere is additional processing involved.
We report on the results of a computational study aimed at identifying
the types of TFs and PSTFs in response to periodic presynaptic inputs
and their dependence on the biophysical and dynamic properties ofthe participating components. We implement biophysically plausible(conductance-based) computational models of a synapse with STP [3],which drives a post-synaptic cell. We characterize the TFs that arise in
the synapse. We determine the conditions under which synaptic low-,high- and band-pass filters arise in terms of the STP and synaptic time
constants and the presynaptic input frequency. We also determine
how the long-term time constants of the synaptic envelope responses(Fig. 1, black curves) depend on the STP time constants, which operate
at the single event level. While the envelopes for the low- and highpass TFs have a single time constant that depends on depression or
facilitation time constants, accordingly, the envelope for the bandpass TF has three time constants that depends on the time constantsfor (i) depression only, (ii) facilitation only, and (iii) a combination of
both. This is in contrast to the naïve expectation that there would be
only two time constants involved (depression and facilitation only).
We then extend our study to include the postsynaptic cell. We identify
and characterize the different types of PSTFs in terms of the properties of the input TF and the properties of the receiving cell. We showthat while under certain conditions, the PSTFs are qualitatively a copyof the synaptic TFs and share many of the TF’s dynamic properties, inPage 100 of 123

other biophysical conditions, the PSTF exhibit a higher degree of complexity, which involve a multiplicity of time scales (e.g., depression/facilitation, synaptic, membrane, ionic currents, summation).

Fig. 1 Representative examples of TFs generated by STP. A low-pass
TF. B high-pass TF. C band-pass TF. Presynaptic input frequencies are
50 Hz (Top) and 150 Hz (Bottom). Examples of pairwise depression
and facilitation are shown in orange and purple. The black envelope
curves represent the TFs
Overall, our results highlight the complexity of TFs and PSTFs, whichreflects the complexity of the underlying interactions. This has implications for the understanding of network filters and the developmentof effective synaptic decoding algorithms.
Acknowledgments: Funded in part by a grant (DMS-1608077 to HGR)
and a Graduate Research Fellowship (to YM) both from the NSF.
References
1. Zucker RS, Regehr WG. Short-term synaptic plasticity. Annual review ofphysiology. 2002; 64(1): 355-405.
2. Fortune ES, Rose GJ. Short-term synaptic plasticity as a temporal filter.
Trends in neurosciences. 2001; 24(7): 381-5.
3. Tsodyks M, Pawelzik K, Markram H. Neural networks with dynamic synapses.
Neural computation. 1998; 10(4): 821-35.

P180
Mean field theory inference and learning in networks
with stochastic natural exponential family neurons
Ivan ­Davidovich1, Benjamin D­ unn2, John ­Hertz3, Yasser ­Roudi1
1
NTNU, Kavli Institute for Systems Neuroscience, Trondheim, Norway;
2
NTNU, Department of Mathematical Sciences, Trondheim, Norway;
3
Nordita and University of Copenhagen, Copenhagen, Denmark
Correspondence: Ivan Davidovich (ivan.a.davidovich@ntnu.no)
BMC Neuroscience 2020, 21(Suppl 1):P180
The advent of experimental techniques that allow for the simultaneous recording of an unprecedented number of neurons has created anecessity for analysis tools that can handle very large datasets. Nonequilibrium models from statistical mechanics such as the kinetic
Ising model have established themselves as natural tools for studying these types of complex networks and datasets. The applicabilityof the kinetic Ising model is however limited by the binary nature of
its neurons. To alleviate this constraint, we extend previous analyses
based on statistical mechanics to dynamical models of networks inwhich the state of the neurons can take values according to distributions in the mono-parametric natural exponential family. Using amean field approach, we obtain both dynamical TAP equations for the
expected activity of these states when the connectivity of the network
is known as well as a naive mean field estimation of the connectivity
when the activity of the neurons has been observed. We test the validity and accuracy of our analytical results by applying them to simulations, obtaining good agreement within specific regions of parameter
space. For the inference of the network’s connectivity we compare
to exact learning via gradient ascent, obtaining excellent agreement
for networks of Gaussian neurons and small discrepancies for Poisson
networks.

﻿BMC Neurosci 2020, 21(Suppl 1):54

P181
Long‑term turnover dynamics in area CA1of hippocampus are consistent with plasticity of non‑spatial inputs
Federico Devalle, Alex Roxin
Centre de Recerca Matemàtica, Barcelona, Spain
Correspondence: Federico Devalle (federico.devalle@gmail.com)
BMC Neuroscience 2020, 21(Suppl 1):P181
Hippocampal representation of space over long time scales is dynamic[1]. Longitudinal calcium imaging of CA1 neurons of mice repeatedly
traversing the same environment over weeks exhibits turnover: onlya small subset of pyramidal cells are active over the entire course of
the experiment, while most of the population drops in and out of theensemble representation of the environment. Yet, whenever active,
place cells typically retain their place field location.
Here, we hypothesize that cells turnover in CA1 is due to the interplay
between two types of synaptic inputs to CA1 pyramidal neurons: astable spatial input from CA3 place cells, and a time-varying non-spatial input. We first test this hypothesis by fitting a statistical model to
CA1 calcium imaging data of mice repeatedly visiting the same familiar track over the course of two weeks [2]. In the statistical model, cells
are described as threshold units, active when the sum of the spatial
and non-spatial inputs they receive is larger than a threshold. Spatial(stable) and non-spatial (time-varying) inputs are modeled as gaussian random variables. The statistical model has three parameters: therelative width of the distribution of spatial and non-spatial inputs, theneuronal threshold, and the auto-correlation of the time-varying nonspatial inputs. By fitting those parameters, the model quantitatively
describes all relevant turnover statistics observed in the experimental
data: the probability that a cell active on one day will be active on subsequent days, the distribution of the total number of sessions in whichcells are active, and cells survival probability.
Based on these results, we then propose a spiking network model of the
hippocampus which accounts for turnover dynamics. In the spiking network model, CA1 pyramidal cells integrate spatially-modulated synaptic
inputs from CA3 place cells, inhibitory inputs from CA1 interneurons,
and non-spatial inputs from a layer of cortical neurons. Integration ofa large number of random CA3 spatial inputs generates spatially-modulated subthreshold voltage in CA1 pyramidal cells, and non-spatial
inputs modulate the excitability of CA1 cells. While spatial connections
are stable, a fraction of the non-spatial connections are rewired overtime. Rewiring non-spatial connections shifts cells excitability, andhence determines whether a cell is active and participate to the ensemble representation at a given time or is silent, generating turnover.
Importantly, whenever cells are active, their place field is in the same
location of the environment, consistent with experimental findings.
By adjusting the relative width of the distributions of spatial and nonspatial inputs (which can be calculated analytically) and the auto-correlation of non-spatial inputs, the spiking network model accurately
fits all relevant turnover statistics observed in the experimental data.
Finally, introducing (weak) correlations among CA3 spatial inputs, themodel is also able to capture the distribution of spatial information
observed in CA1 pyramidal cells.
References
1. Ziv Y, Burns LD, Cocker ED, Hamel EO, Ghosh KK, Kitch LJ, El Gamal A,
Schnitzer MJ. Long-term dynamics of CA1 hippocampal place codes.
Nature Neuroscience. 2013; 16(3): 264.
2. Rubin A, Geva N, Sheintuch L, Ziv Y. Hippocampal ensemble dynamics
timestamp events in long-term memory. Elife. 2015; 4: e12247.

P182
Intermittent synchronization in a pyramidal‑interneuron gamma(PING) network
Quynh‑Anh ­Nguyen1, Leonid R­ ubchinsky2
1
Indiana University Purdue University Indianapolis, Mathematical Sciences,
Indianapolis, Indiana, United States of America; 2Indiana University Purdue
University Indianapolis and Indiana University School of Medicine,

Page 101 of 123

Department of Mathematical Sciences and Stark Neurosciences ResearchInstitute, Indianapolis, Indiana, United States of America
Correspondence: Quynh‑Anh Nguyen (qpnguyen@iu.edu)
BMC Neuroscience 2020, 21(Suppl 1):P182
Synchronization in neural system plays an important role in many
brain functions such as perception and memory. Abnormal synchronization can be observed in neurological disorders such as Parkinson’s
disease, schizophrenia, autism, and addiction. When the couplingstrength is moderate, the synchronization is highly intermittent evenin a short time scale. That is, a system exhibits intervals of synchronization followed by intervals of desynchronization. Thus, neural circuits
dynamics may show different distributions of duration of desynchronization even if the synchronization strength is similar.
In general, some partially synchronized systems can exhibit a few but
long desynchronized intervals while other systems can yield many but
short desynchronized intervals. Experimental data thus far has shownthat neural synchronization follows the latter trend in either healthy ordiseased brains [1-3]. It was suggested that there are functional advantages of circuitry with many short durations of desynchronization [4].
In this study, we use a conductance-based PING network to study neural synchronization specifically in the low gamma band. Gamma frequency rhythm is known to play important role in cognitive functions
such as percept formation, object representation, learning, and memory. Several experimental studies indicate that while both healthy anddiseased brains have many short intervals of desynchronization, there
are differences in their distributions of desynchronization durations[2,3,5]. This study explores the cellular and synaptic effects on the temporal patterning of the partially synchronized model gamma rhythms
and considers potential functional implications of different temporal
patterns.
Acknowledgements: This work was supported by NSF grant DMS
1813819.
References
1. Ahn S, Rubchinsky LL. Short desynchronization episodes prevail in synchro‑nous dynamics of human brain rhythms. Chaos. 2013; 23(1): 013138.
2. Malaia EA, Ahn S, Rubchinsky LL. Dysregulation of temporal dynamics ofsynchronous neural activity in adolescents on autism spectrum. Autism
Research. 2020; 13(1): 24-31.
3. Ahn S, Rubchinsky LL, Lapish CC. Dynamical reorganization of synchronous
activity patterns in prefrontal cortex–hippocampus networks during behav‑
ioral sensitization. Cerebral Cortex. 2014; 24(10): 2553-61.
4. Ahn S, Rubchinsky LL. Potential mechanisms and functions of intermittent
neural synchronization. Frontiers in Computational Neuroscience. 2017; 11:44.
5. Ahn S, Zauber SE, Worth RM, Witt T, Rubchinsky LL. Neural synchronization:
Average strength vs. temporal patterning. 2018.

P183
Universal fingerprints of slow‑wave activity in in vivo, in vitro
and in silico cortical networks
Alessandra ­Camassa1, Andrea ­Galluzzi2, Miguel ­Dasilva1, Beatriz ­Rebollo1,
Maurizio ­Mattia2, Maria V Sanchez‑Vives3
1
Institut d’Investigacions Biomèdiques August Pi i Sunyer, Systems
Neuroscience, Barcelona, Spain; 2Istituto Superiore di Sanitá, National
Center for Radiation Protection and Computational Physics, Rome, Italy;
3
Institut d’Investigacions Biomèdiques August Pi i Sunyer, ICREA, Systems
Neuroscience, Barcelona, Spain
Correspondence: Alessandra Camassa (ale.camassa22@gmail.com)
BMC Neuroscience 2020, 21(Suppl 1):P183
The cerebral cortex as a structured network is able to spontaneously
express different types of dynamics that are continuously changing
over time according to the ongoing brain state. Transitions acrossbrain states correlate with changes in network excitability and functional connectivity giving rise to a wide repertoire of spatiotemporal
patterns of neuronal activity [1]. The quasi-periodic occurrence oftravelling waves - namely slow-wave activity (SWA) - characterizes
the cortical networks under unconscious brain states. The spatiotemporal patterns generated under SWA are shaped by the structure and﻿BMC Neurosci 2020, 21(Suppl 1):54

excitability of the underlying network [2,3]. Thus, the emergent wavefronts portray the characteristics of the dynamical regime under which
they have been spawned. Here we aimed to develop novel analyticalmethods to capture wave propagation features and to identify theuniversal fingerprints of the cortical network activity generated by different preparations all spontaneously expressing SWA, in order to gaina deeper understanding of functional mechanisms underlying the cortical network organization. To do so, we studied the spatiotemporal
dynamics of the cortex under SWA in three different frameworks: invivo, performing extracellular recordings of cortical activity in deeply
anesthetized mice with a superficial multielectrode array; in vitro,
recording the electrophysiological signals from cortical slices cut fromferret visual cortex; in silico, in a simulated multimodular network of
spiking neurons [2,4]. We studied network dynamics by characterizing the spatiotemporal patterns of propagation of the activation
wavefronts developing a phase-based method that allow an accurate
reconstruction of the waves travelling across the cortex both in experimental and simulated data [5]. We complemented the study of network dynamics with the computation of network synchronization overtime, evaluating the variability of ongoing synchrony fluctuations thatentail dynamically changing states, in our case Up and Down states of
SWA. Finally, we evaluated the dynamical richness of the cortical activity by estimating the dimensionality of the system dynamics over time.
We adopted an approach drawn from experimental fluid dynamics inphysics [6]. Applying an empirical eigenfunction approach by meansof the algorithm of Singular Value Decomposition (SVD) it is possible
to quantify the instantaneous energy of the system and its effective
dimension, and to study the evolution of the system dimension overtime as well as its dependence on the structure and on the dynamical
state of the system. In this way, we were able to compare the mechanistic underpinning of SWA when the intact cortex is functionallydisconnected (in vivo under deep anesthesia) and when it is anatomically disconnected from the rest of the brain (in vitro in cortical slices),and finally exploiting the model, to emphasize the universal natureof this slow rhythm highlighting both the differences and similarities
between experimental conditions.
Acknowledgements: Funded by the EU H2020 Research and Innovation Programme, Grant 720270 (HBP SGA2).
References
1. Stitt I, Hollensteiner KJ, Galindo-Leon E, Pieper F, Fiedler E, Stieglitz T, Engler
G, Nolte G, Engel AK. Dynamic reconfiguration of cortical functional con‑nectivity across brain states. Scientific Reports. 2017; 7(1): 1-4.
2. Capone C, et al. Slow waves in cortical slices: how spontaneous activity isshaped by laminar structure. Cerebral Cortex. 2019; 29(1): 319-35.
3. Barbero-Castillo A, et al. Proceedings# 31: cortical network complexity
under different levels of excitability controlled by electric fields. Brain Stimu‑
lation: Basic, Translational, and Clinical Research in Neuromodulation. 2019;12(2): e97-9.
4. Mattia M, Pani P, Mirabella G, Costa S, Del Giudice P, Ferraina S. Heterogene‑ous attractor cell assemblies for motor planning in premotor cortex. Journalof Neuroscience. 2013; 33(27): 11155-68.
5. Muller L, Reynaud A, Chavane F, Destexhe A. The stimulus-evoked popula‑tion response in visual cortex of awake monkey is a propagating wave.
Nature communications. 2014; 5(1): 1-4.
6. Schiff SJ, Huang X, Wu JY. Dynamical evolution of spatiotemporal patterns
in mammalian middle cortex. Physical Review Letters. 2007; 98(17): 178102.

P184
Selection of ictogenic zones in epilepsy surgery using
computational models
Petroula ­Laiou1, Eugenio ­Abela1, Mark ­Richardson1, Marc G­ oodfellow2,
Eleftherios ­Avramidis3, Marinho L­ opes4, Michael ­Mueller5, Ozgur ­Akman6,
Christian ­Rummel7, Kaspar ­Schindler5
1King’s College London, Institute of Psychiatry, Psychology
and Neuroscience, London, United Kingdom; 2University of Exeter, Living
Systems Institute, Exeter, United Kingdom; 3University of Cambridge,
Research Computing Services, Cambridge, United Kingdom; 4Universityof Cardiff, Brain Research Imaging Centre, Cardiff, United Kingdom;
5
Bern University Hospital, Department of Neurology, Bern, Switzerland;
6
University of Exeter, College of Engineering, Mathematics and PhysicalPage 102 of 123

Sciences, Exeter, United Kingdom; 7Bern University Hospital, Support
Center for Advanced Neuroimaging, University Institute for Diagnostic
and Interventional Neu, Bern, Switzerland
Correspondence: Petroula Laiou (petroula.laiou@kcl.ac.uk)
BMC Neuroscience 2020, 21(Suppl 1):P184
Epilepsy surgery is a therapeutic option that can alleviate seizures inpeople with refractory epilepsy. The aim of surgery is to remove theepileptogenic zone, i.e. the brain tissue that is considered indispensable for seizure generation [1,2]. During the presurgical evaluation
clinical teams integrate diverse information and if seizure freedom isachieved after surgery, it is considered that the epileptogenic zone
has been resected [1]. Unfortunately, the rates of post-surgical seizure
freedom are currently sub-optimal, due to the lack of understanding
the seizure generating mechanism and assessing the consequences ofsurgical resections.
Recent computational studies in large-scale brain networks [3-5] havebeen developed with the aim to inform epilepsy surgery. Here, we use
a modelling framework that allows to investigate how sets of nodes
contribute to the seizure generating capability (i.e. ictogenicity) of a
network [6]. In particular, we use a measure called Set Ictogencity (SI)in order to quantify the contribution of a set of nodes to ictogenicity.
We use artificial networks with various topologies and examine how
SI varies across different sets of nodes. In networks with small size
we compute SI for all possible sets of nodes and show that the ictogenicity across sets depends on network topology. However, in large
networks the computation of SI of all possible sets is a combinatorial
problem that becomes intractable. Therefore, we combine computational models with a genetic algorithm to search for sets with minimal
size that contribute to the seizure generation.
We demonstrate the potential applicability of these methods by identifying optimal set of nodes to resect in brain networks derived froma cohort of 20 people who underwent epilepsy surgery. In addition,
we show that this modelling framework has the potential to assist epilepsy surgery by suggesting alternative resection sites as well as allowing the avoidance of brain regions that should not be resected.
References
1. Rosenow F, Lüders H. Presurgical evaluation of epilepsy. Brain. 2001; 124:
1683–700.
2. Nowell M, Miserocchi A, McEvoy AW, Duncan JS. Advances in epilepsy
surgery. Journal of Neurology Neurosurgery & Psychiatry. 2014; 85: 1273–9.
3. Goodfellow M, Rummel C, Abela E, Richardson MP, Schindler K, Terry JR.
Estimation of brain network ictogenicity predicts outcome from epilepsy
surgery. Scientific Reports. 2016; 6: 29215.
4. Steimer A, Müller M, Schindler K. Predictive modeling of EEG time series
for evaluating surgery targets in epilepsy patients. Human Brain Mapping.
2017; 38(5): 2509-31.
5. Müller M, et al. Linear and nonlinear interrelations show fundamentally
distinct network structure in preictal intracranial EEG of epilepsy patients.
Human Brain Mapping. 2020; 41(2): 467-83.
6. Laiou P, et al. Quantification and selection of ictogenic zones in epilepsy
surgery. Frontiers in Neurology. 2019; 10: 1045.

P185
Higher‑order interactions induced by strong shared inputs
Safura Rashid ­Shomali1, Seyyed Nader ­Rasuli2,3, Hideaki ­Shimazaki4
1
Institute for Research in Fundamental Sciences, School of Cognitive
Sciences, Tehran, Iran; 2University of Guilan, Department of Physics, Rasht,
Iran; 3Institute for Research in Fundamental Sciences, School of Physics,
Tehran, Iran; 4Hokkaido University, Center for Human Nature, Artificial
Intelligence and Neuroscience, Hokkaido, Japan
Correspondence: Safura Rashid Shomali (safura@ipm.ir)
BMC Neuroscience 2020, 21(Suppl 1):P185
Experimental studies demonstrated that neural populations exhibit
correlated spiking activity that goes beyond pairwise correlations andinvolves higher-order interactions [1-5]. These higher-order interactions are known to encode stimulus information or the internal stateof the brain [1-5]. However, the origin of this population activity andtypes of presynaptic neurons inducing the higher-order interactions

﻿BMC Neurosci 2020, 21(Suppl 1):54

remain unclear. Here we investigate how the interactions [6] amonggroups of 3, 4, and then N neurons emerge, when they receive common inputs on top of independent noisy background inputs, assuming simple connecting motifs. Given Poissonian common inputs, we
calculate the neural interactions among clusters of neurons in a small
time-window for the limit of the strong common input’s amplitude.
When 2 or 3 neurons share excitatory/inhibitory common inputs, theirpairwise and triple-wise interactions are well explained as functions of
their baseline spontaneous rate, and the common-input’s rate [7].
We analytically solve the interactions for a cluster of more than 3 neurons when all of them share strong excitatory/inhibitory common
input. Then, extending our analysis to the arbitrary number of N neurons we show that the N-th order interaction among neurons is stilla simple function of the postsynaptic and common input rates. However, in larger populations, the N-th order interaction more strongly
depends on the spontaneous rate of postsynaptic neuron rather than
input rate. We also observe that larger number of neurons induce
stronger magnitude of interactions, regardless of interaction’s sign.
Moreover, shared excitatory inputs to all neurons always generateinteractions with positive sign, while shared inhibitory inputs induceinteractions with oscillatory signs with respect to N. Finally, we obtain
the analytic result when excitatory or inhibitory inputs are shared
among N-1 out of all N neurons: surprisingly, the N-th order interactions exhibit signs opposite to those found when the common inputs
is shared by all N neurons.
In all mentioned cases, when the spontaneous activity of postsynaptic
neurons is low, excitatory inputs can generate strong positive/negative higher-order interactions, whereas for high spontaneous activity,
inhibitory neurons can induce large absolute values of higher-orderinteractions. These results are valid for any neuron model and solely
based on the assumption of strong common inputs given to neurons.
Since cortical, subcortical, and retinal neurons mostly exhibit spontaneous activity less than λ=40 Hz, for small time-window of Δ=5ms,
these neurons are in low spontaneous regime i.e. λΔ < 0.2. Therefore
we suggest that the significant higher-order interactions observed inretina, hippocampus, and cortices reveal that motifs of strong excitatory rather than inhibitory shared inputs are present and dominant
there.
Finally, we draw a table that links the strength of interactions andtheir signs to motifs, both for low and high spontaneous activity
regimes. So based on interactions obtained from experimental data,
it is possible to predict the underlying motif behind it. For example,
for a specific experiment done in the hippocampal CA3 region [8], theobserved negative 3rd-order, positive 4th-order, and negative 5thorder interactions leads us to the architecture of excitatory to pairs,that can generate such interactions simultaneously.
References
1. Ohiorhenuan IE, et al. Sparse coding and high-order correlations in finescale cortical networks. Nature. 2010; 466: 617–621.
2. Montani F, et al. The impact of high-order interactions on the rate of syn‑chronous discharge and information transmission in somatosensory cortex.
Philosophical Transactions of the Royal Society A: Mathematical, Physicaland Engineering Sciences. 2009; 367(1901): 3297-310.
3. Yu S, et al. Higher-order interactions characterized in cortical activity. The
Journal of Neuroscience. 2011; 31: 17514–17526.
4. Shimazaki H, et al. State-space analysis of time varying higher-order spikecorrelation for multiple neural spike train data. PLoS Computational Biology.
2012; 8: e1002385.
5. Ganmor E, Segev R, Schneidman E. Sparse low-order interaction network
underlies a highly correlated and learnable neural population code. Pro‑
ceedings of the National Academy of sciences. 2011; 108(23): 9679-84.
6. Nakahara H, Amari S. Information-geometric measure for neural spikes.
Neural Computation. 2002; 14: 2269–2316.
7. Shomali SR, Ahmadabadi MN, Rasuli SN, Shimazaki H. Uncovering Network
Architecture Using an Exact Statistical Input-Output Relation of a Neuron
Model. bioRxiv. 2018: 479956.
8. Shimazaki H, Sadeghi K, Ishikawa T, Ikegaya Y, Toyoizumi T. Simultaneous
silence organizes structured higher-order interactions in neural popula‑tions. Scientific Reports. 2015; 5(1): 1-3.

Page 103 of 123

P186
Inhibitory neurons locate at a center of effective cortical networks,
and have high ability to control other neurons.
Motoki ­Kajiwara1, Masanori ­Shimono2
1
Kyoto University, Kyoto, Japan; 2Kyoto University, Graduate Schoolof Medicine and Faculty of Medicine, Kyoto, Japan
Correspondence: Motoki Kajiwara (mokki.70878@gmail.com)
BMC Neuroscience 2020, 21(Suppl 1):P186
The brain is a network system in which excitatory and inhibitory neurons keep the activity balanced in the highly non-uniform connectivity pattern of the microconnectome. It is well known that the relative
percentage of inhibitory neurons is much smaller than excitatory neurons. So, in general, how the inhibitory neurons can keep the balancewith the surrounding excitatory neurons is an important question.
This study simultaneously recorded electric signals from ~1000 neurons from seven acute brain slices of mice with a MEA (multi-electrodearray) to analyze the network architectures of cortical neurons. Subsequently, we analyzed the spike data to reconstruct the causal interaction networks between the neurons from their spiking activities. The
utilized analysis mainly consists of the following three steps: first,
Transfer Entropy was adopted from previous research to reconstruct
the neural network. Briefly, Transfer Entropy quantifies the amount of
information transferred between neurons and is suitable for the effective connectivity analysis of neural networks. This allowed to elucidate
the microconnectome and the comprehensive and quantitative characteristics of interaction networks among neurons. Second, our study
distinguishes between excitatory synapses and inhibitory synapses
using a newly developed method called sorted local transfer entropy(fig. 1a). Third, we also applied methods from graph theory to evaluate
the network architecture. Especially, we observed that the precedence
in centrality and controlling ability of inhibitory neurons. The centrality was quantified with K-core centrality, and the controlling ability
was quantified with the ratio of nodes included in FVSs (Feedback Vertex Sets). Fourth, we stained acute brain slices and gave layer labels to
individual neurons. Further detail will be shown in [1].

Fig. 1 a Shows an example of interaction networks among neurons.
b shows the difference of averaged K core values for all excitatory
and inhibitory neurons. c shows the difference of ratio of number of
FVS within excitatory or inhibitory neuron pools. The E/I difference
was consistently and significantly observed among 7 cortical slices(Wilcoxon paired test; p<0.05)
As a result, we found that inhibitory neurons, locating highly central and having strong controlling ability of other neurons (fig. 1b-c),
mainly locate in deep cortical layers by comparing with distributionof neurons coloured by NeuN immunostaining data. Preceding theobservation, we also found that inhibitory neurons show higher firing rate than excitatory neurons, and that their firing rate also closely
obey a log-normal distribution as previously known about excitatoryneurons. Additionally, their connectivity strengths also obeyed a lognormal distribution.
Acknowledgements: This study was supported by several MEXT
fundings (19H05215, 17K19456) and Leading Initiative for Excellent
Young Researchers (LEADER) program, and grants from the Uehara
Memorial Foundation.

﻿BMC Neurosci 2020, 21(Suppl 1):54

Reference
1. Kajiwara M, Nomura R, Goetze F, Akutsu T, Shimono M. Inhibitory neurons
are a Central Controlling regulator in the effective cortical microconnec‑
tome. bioRxiv. 2020.

P187
Modulation of the hierarchical gradient of cognitive information
processing dynamics during rest and task
Oliver ­Cliff1, Mike ­Li1, Dennis H­ ernaus2, Lianne S­ choltens3, Eli Müller4,
Brandon ­Munn4, Gabriel ­Wainstein4, Ben ­Fulcher5, Joseph L­ izier1, James­Shine4
1
The University of Sydney, Centre for Complex Systems, Sydney, Australia;
2
University of Maryland, Maryland Psychiatric Research Center, Schoolof Medicine, Baltimore, Maryland, United States of America; 3Vrije
Universiteit Amsterdam, Amsterdam, Netherlands; 4The University
of Sydney, Brain and Mind Centre, Sydney, Australia; 5The University
of Sydney, School of Physics, Sydney, Australia
Correspondence: Joseph Lizier (joseph.lizier@sydney.edu.au)
BMC Neuroscience 2020, 21(Suppl 1):P187
Cognition involves the dynamic adaptation of information processing resources as a function of task demands. To date, the neuralmechanisms responsible for mediating this process remain poorly
understood. In this study, we integrated cognitive neuroscience withinformation theory, network topology and neuropharmacology toadvance our understanding of the fundamental computational processes that give rise to cognitive function in the human brain.
In our first experiment, we consider the contrast between dynamic
whole-brain blood oxygen level dependent (BOLD) data from both
the resting state and a cognitively-challenging N-back task from the
Human Connectome Project (N = 457) [1,2]. We translated the raw
BOLD activity levels into time series that represent the dynamics ofneural information processing by measuring information flows (pairwise between regions, using transfer entropy) and information storage (self-prediction in individual regions, using active information
storage) as a function of time throughout the experiment [3].
Our results show that cognitive task performance alters the wholebrain information-processing landscape in a low-dimensional manner: during rest, information flowed from granular to agranular
cortices, whereas this pattern was reversed during the performanceof the N-back task. These contrasting gradients of information flowreflect the difference between a stronger “bottom-up” mode duringrest (with inputs from sensory cortices sent up for interpretation as
the dominant flow) versus a stronger “top-down” mode during task
(where task performance is facilitated by higher level control and theincrease of associated flows).
To test a hypothesized mechanism for this switch [4], we modulated
central noradrenaline levels in a double-blind, cross-over atomoxetine
pharmacological fMRI study (N = 19) [5]. We found that potentiating
the noradrenergic system altered the information processing dynamics by augmenting information transfer to and from the frontoparietal
cortices. Together, our results provide a conceptual bridge between
cognitive function, network topology, information theory and theascending neuromodulatory arousal system.
References
1. Barch DM, et al. Function in the human connectome: task-fMRI and indi‑vidual differences in behavior. Neuroimage. 2013; 80: 169-89.
2. Glasser MF, et al. The minimal preprocessing pipelines for the Human Con‑
nectome Project. Neuroimage. 2013; 80: 105-24.
3. Lizier JT. JIDT: An information-theoretic toolkit for studying the dynamics ofcomplex systems. Frontiers in Robotics and AI. 2014; 1: 11.
4. Shine JM, Aburn MJ, Breakspear M, Poldrack RA. The modulation of neuralgain facilitates a transition between functional segregation and integrationin the brain. Elife. 2018; 7: e31130.
5. Hernaus D, Santa MM, Offermann JS, Van Amelsvoort T. Noradrenaline trans‑
porter blockade increases fronto-parietal functional connectivity relevant
for working memory. European Neuropsychopharmacology. 2017; 27(4):399-410.

Page 104 of 123

P188
Neuro‑PC: causal functional connectivity for neural dynamics
Rahul ­Biswas1, Eli S­ hlizerman2
1
University of Washington, Statistics, Seattle, Washington, UnitedStates of America; 2University of Washington, Electrical and Computer
Engineering, Seattle, Washington, United States of America
Correspondence: Rahul Biswas (rbiswas1@uw.edu)
BMC Neuroscience 2020, 21(Suppl 1):P188
Neuronal interactions lead to behavior and cognition, so that investigating the interactions in the brain network is essential to understanding brain function. However, approaches that characterize the causal
relationships between neuronal time series – causal functional connectivity – have not been well investigated. In this work, we develop
methodology for inferring the causal functional connectivity between
neurons. The methodology at the core relies on adapting the PC algorithm, a state-of-the-art method for statistical causal inference, to theneuronal time series scenario. We validate the performance of themethod in synthetic signals generated from continuous time artificial
neural networks. We further obtain the causal functional connectivity between neurons in mice brain under different visual stimuli fromelectro-physiological neural recordings.

Fig. 1 This figure compares the accuracy of the neuro-PC andtraditional PC algorithm in inferring the causal functional connectiv‑
ity from synthetic neural signals. The signals were generated by acontinuous-time recurrent neural network with connection weights
of 0.5 and connections shown by the motifs in the first row of thisfigure﻿BMC Neurosci 2020, 21(Suppl 1):54

P189
Computationally going where experiments cannot: a dynamicalassessment of dendritic currents in the behaving animal
Alexandre Guet ­McCreight1, Frances S­ kinner2
1
Centre for Addiction and Mental Health, Krembil Centre
for Neuroinformatics, Toronto, Canada; 2Krembil Research Institute,
Division of Clinical and Computational Neuroscience, Toronto, Canada
Correspondence: Alexandre Guet McCreight (agmccrei@gmail.com)
BMC Neuroscience 2020, 21(Suppl 1):P189
Though the electrophysiology techniques that we use to probe neuronal function have made large advancements, neuronal function
remains shrouded in mystery. Little is known about the current contributions that govern cell excitability across different neuronal subtypes
and their dendritic compartments in vivo. The picture that we do haveis largely based on somatic recordings performed in vitro.
Uncovering dendritic current contributions in neuron subtypes that
represent a minority of the neuronal population is not currently afeasible task using purely experimental means. Thus, we employ morphologically-detailed multi-compartment models, and specifically, we
use two models of a specific type of inhibitory interneuron, the oriens
lacunosum moleculare (OLM) cell. The OLM cell is a well-studied cell
type in CA1 hippocampus that is important in gating sensory and contextual information.
We use these models to assess the current contribution profile acrossthe different somatic and dendritic compartments of the models in
the presence of levels of synaptic bombardment that would occur
in vivo and compare them to corresponding in vitro scenarios withsomatic current injections that generate the same spike rates. Using
this approach, we identify changes in dendritic excitability, current
contributions, and current co-activation patterns.
We find that during in vivo-like scenarios the relative timing between
different channel current activation patterns and voltage are preserved. On the other hand, when compared across morphologicalcompartments, current and voltage signals were more decorrelated
during in vivo-like scenarios, suggesting decreased signal propagation.
We also observe that changes do occur during in vivo-like scenarios
on the level of relative current contribution profiles. More specifically,in addition to shifts in the relative balances of currents that are mostactive during spikes, we report robust enhancements in dendritic
hyperpolarization-activated cyclic nucleotide-gated channel (HCN, or
h-current) activation during in vivo-like contexts [1]. This suggests that
dendritically-located h-channels are functionally important in altering
signal propagation in the behaving animal.
Reference
1. Guet-McCreight A, Skinner FK. Computationally going where experiments
cannot: a dynamical assessment of dendritic ion channel currents duringin vivo-like states. F1000Research. 2020; 9(180): 180.

P190
Neural Field Theory: modelling development of topography
via activity based mechanisms.
Nicholas ­Gale1, Michael ­Small2
1
University of Cambridge, Applied Mathematics and Theoretical
Physics, Cambridge, United Kingdom; 2University of Western Australia,
Mathematics, Perth, Australia
Correspondence: Nicholas Gale (nmg41@cam.ac.uk)
BMC Neuroscience 2020, 21(Suppl 1):P190
Topographic maps are brain structures which connect two regions [1].
These maps are essential features of primary sensory signal processing.
A prototypical animal model of such a system is the mouse retinotopic
map [2]. Topography is developed using three distinct mechanisms:
chemotaxis, competition, and activity based refinement [3]. Chemotaxis establishes a coarse topography with broad dendritic arbors
which is followed by three stages of spontaneously generated wavesof electrical activity in the retina: first at E16-P0, then from P0-P11, andfinally from P11-P14 [4]. These three periods of have distinct spatiotemporal characteristics and likely perform different functions in thedevelopment of the retinotopic system. They are concurrent with

Page 105 of 123

electrical activity in the SC and the correlations between these signals guide Hebbian plasticity to make the refinement. Unified modelsof activity and genetics have found success in predicting the effects
of chemical perturbations, but not activity-based perturbations [5].
The activity mechanism in these models condenses the activity into apurely spatial and radially symmetric isotropic form.
A good model of electrical activity in brain regions with lateral connectivity and dense homogenous cell types such as those in the SC is neural field theory (NFT) [6]. A theoretical framework of Hebbian-based
plasticity that can incorporate time-signatures of activity has beendeveloped for NFT [7]. This framework allows for the incorporation ofa more accurate and complete description of spatio-temporally varying waves. In this paper we shall demonstrate that NFT can support
the refinement and establishment of precise topography via waves
of propagating activity and biologically reasonable Hebbian learning
rules and therefore establish it as a useful model to study the development of topographic systems.
We develop an analytical solution to the field equation by first linearizing the sigmoid activation function. We then proceed with computational analysis of three key parameters: the width of the wave
stimulus, wave-speed, and the width of the lateral connections.
Finally, we discuss the limitations of the model, implications of theseresults in the context of the β2 knock-out (an activity perturbation),and future directions.
References
1. Udin SB, Fawcett JW. Formation of topographic maps. Annual Review of
Neuroscience. 1988; 11(1): 289-327.
2. Ackman JB, Burbridge TJ, Crair MC. Retinal waves coordinate patterned
activity throughout the developing visual system. Nature. 2012; 490(7419):
219-25.
3. Cang J, Feldheim DA. Developmental mechanisms of topographic map
formation and alignment. Annual Review of Neuroscience. 2013; 36: 51-77.
4. Bansal A, et al. Mice lacking specific nicotinic acetylcholine receptor subu‑
nits exhibit dramatically altered spontaneous activity patterns and reveal
a limited role for retinal waves in forming ON and OFF circuits in the innerretina. Journal of Neuroscience. 2000; 20(20): 7672-81.
5. Hjorth JJ, Sterratt DC, Cutts CS, Willshaw DJ, Eglen SJ. Quantitative assess‑ment of computational models for retinotopic map formation. Develop‑
mental Neurobiology. 2015; 75(6): 641-66.
6. Amari SI. Dynamics of pattern formation in lateral-inhibition type neuralfields. Biological Cybernetics. 1977; 27(2): 77-87.
7. Robinson PA. Neural field theory of synaptic plasticity. Journal of Theoretical
Biology. 2011; 285(1): 156-63.

P191
Electro‑physiology models of cells with spherical geometry
Jiamu ­Jiang1, Mark van R­ ossum2, Paul S­ mith3
1
University of Nottingham, Mathematics, Nottingham, United
Kingdom; 2University of Nottingham, School of Psychology and Schoolof Mathematical Sciences, Nottingham, United Kingdom; 3Universityof Nottingham, School of Life Sciences, Nottingham, United Kingdom
Correspondence: Jiamu Jiang (jiamu.jiang@nottingham.ac.uk)
BMC Neuroscience 2020, 21(Suppl 1):P191
Neurons and other cells use electrical signals for intra- and intercellular communication. Accurate mathematical models have been developed to describe the spatial-temporal dynamics of their voltage in
response to input current. Best known is the cable equation, which
describes voltage propagation along the length of a passive cable inresponse to current injection [1]. However, this model is not appropriate for cells with geometries other than the cylinder. Here we study
the flow of electrical currents in cells with a spherical geometry inwhich only a thin shell close to the surface conducts. Such geometries
arise for instance in white adipocytes (fat cells), which are cells consisting of an insulating lipid droplet core surrounded by a thin conductive
cytoplasm shell, and might also be relevant for some types of spherically shaped peripheral neurons.
First, we construct a circuit model based on the nature of the passive
membrane, and derive the equivalent of the cable equation for spherical geometries. We derive the steady-state solution analytically and﻿BMC Neurosci 2020, 21(Suppl 1):54

show that the shape of the voltage profile depends on a single parameter that describes its electrotonic compactness. Furthermore, weshow that, in contrast to the cable equation, the voltage profile acrossthe cell is sensitive to the electrode geometry.
Next, we numerically explore the time-dependent solution to step
input currents. In particular, we find that the charging and discharging are much faster than one would expect from the membrane time
constant, which is important when one aims to extract fundamental
membrane properties from experimental recordings.
Finally, we consider voltage-clamping experiments, often used tomeasure input current of cells and examine the distortions arisingfrom imperfect space-clamp.
In conclusion, our study yields an equivalent of the cable equation for
spherical geometries, which can facilitate further investigations of theelectrical signals on cells with spherical structures.
Reference
1. Koch C. Biophysics of computation: information processing in single neu‑rons. Oxford university press; 2004.

P192
Estimating transfer entropy in continuous time for spike trains
David ­Shorten1, Joseph L­ izier1, Richard S­ pinney2
1
University of Sydney, Complex Systems Research Group, Sydney, Australia;
2
University of New South Wales, Single Molecule Science, Sydney, Australia
Correspondence: David Shorten (david.shorten@sydney.edu.au)
BMC Neuroscience 2020, 21(Suppl 1):P192
Transfer entropy (TE) [1] is a measure of the flow of information
between components in a system. It is defined as the mutual information between the past of a source and the present state of a target, conditioned on the past of the target. It has received widespread
application in neuroscience [2], both for characterising information
flows as well as inferring effective connectivity from data sources
such as MEG, EEG, fMRI, calcium imaging and electrode arrays. Previous applications of TE to spike trains have relied on time discretisation,
where the spike train is divided into time bins and the TE is estimatedfrom the numbers of spikes occurring in each bin. There are, however,
several disadvantages to estimating TE from time-discretised data [3].
Firstly, as time discretisation is a lossy transformation of the data, anyestimator based on time discretisation is not consistent (it will notconverge to the true value of the TE in the limit of infinite data). Secondly, whilst the loss of resolution of the discretisation will decrease
with decreasing bin size, this requires larger dimensionality of the history embeddings to capture correlations over similar time intervals.
This results in an exponential increase in the state space size being
sampled and therefore the data requirements.
Recently, a continuous-time framework [3] for transfer entropy wasdeveloped. This framework has a distinct advantage in that it demonstrates that, for spike trains, the TE can be calculated solely fromcontributions occurring at spikes. This presentation reports on a newly
developed continuous-time estimator for transfer entropy for spiketrains which utilises this framework. Importantly, this new estimator isa consistent estimator of the TE. As it does not require time discretisation, it calculates the TE based on the raw interspike interval timingsof the source and target neurons. Similar to the popular KSG estimator[4] for mutual information and TE, it performs estimation using the statistics of K-nearest-neighbour searches in the target and source history
spaces. Tests on synthetic datasets of coupled and uncoupled point
processes have confirmed that the estimator is consistent and has low
bias. Similar tests of the time-discretised estimator have found it to
not be consistent and have larger bias. The efficacy of the estimator isfurther demonstrated on the task of inferring the connectivity of biophyiscal models of the pyloric network of the crustacean stomatogastric ganglion. Granger causality (which is equivalent to TE under theassumption of Gaussian variables) has been shown to be incapableof inferring this particular network [5], although it was demonstrated
that it could be inferred by a generalised linear model.

Page 106 of 123

References
1. Schreiber T. Measuring information transfer. Physical Review Letters. 2000;
85(2): 461.
2. Wibral M, Vicente R, Lizier JT, editors. Directed information measures inneuroscience. Berlin: Springer; 2014.
3. Spinney RE, Prokopenko M, Lizier JT. Transfer entropy in continuous time,with applications to jump and neural spiking processes. Physical Review E.
2017; 95(3): 032319.
4. Kraskov A, Stögbauer H, Grassberger P. Estimating mutual information.
Physical review E. 2004; 69(6): 066138.
5. Kispersky T, Gutierrez GJ, Marder E. Functional connectivity in a rhythmic
inhibitory circuit using Granger causality. Neural Systems & Circuits. 2011;
1(1): 9.

P193
Input strength dependence of the beta component of gamma‑bandauditory steady‑state responses in patients with schizophrenia
Christoph ­Metzner1, Volker ­Steuber2
1
Technische Universität Berlin, Department of Software Engineeringand Theoretical Computer Science, Berlin, Germany; 2University
of Hertfordshire, Biocomputation Research Group, Hatfield, United
Kingdom
Correspondence: Christoph Metzner (cmetzner@ni.tu‑berlin.de)
BMC Neuroscience 2020, 21(Suppl 1):P193
The mechanisms underlying circuit dysfunctions in schizophrenia(SCZ) remain poorly understood. Auditory steady-state response(ASSRs), especially in the gamma and beta band, have been suggested as a potential biomarker for SCZ. While the reduction of 40Hz
power for 40Hz drive has been well established and replicated in SCZ
patients, studies are inconclusive when it comes to an increase in 20Hz
power during 40Hz drive [1]. There might be several factors explaining the inconsistencies, including differences in the sensitivity of therecording modality (EEG vs MEG), differences in stimuli (click-trains vsamplitude-modulated tones) and also large differences in the amplitude of the stimuli.
Here, we used a computational model of ASSR deficits in SCZ [2-4],
in which increased IPSC decay times at GABAergic synapses produce
ASSR deficits as seen experimentally. We investigated the effect of
input strength on gamma and beta band power during gamma ASSR
stimulation. We found that the pronounced increase in beta power
during gamma stimulation seen experimentally could only be reproduced in the model for a specific range of input strengths. More specifically, if the input was too weak the network failed to produce astrong oscillatory rhythm. When the input was in the specific range,
the rhythmic drive at 40Hz produced a strong 40Hz rhythm in the control network, however, in the ‘SCZ-like’ network, the prolonged inhibition led to a so-called ‘beat-skipping’, where the network would only
strongly respond to every other input. This mechanism was responsible for the emergence of the pronounced 20Hz beta peak in the power
spectrum. However, if the input exceeded a certain strength value, the20Hz peak in the power spectrum disappeared again. In this case, prolonged inhibition due to the increased IPSC times was insufficient tosuppress the now stronger gamma drive from the input, resulting inan absence of the beat-skipping and single peak at 40Hz in the power
spectrum.
Here, we employed an established model of gamma and beta band
ASSR deficits in SCZ to explore the dependence of a beta component in response to gamma drive on the strength of the input. Ourfinding that the beta component only existed for a specific range of
input strengths might explain the seemingly inconsistent reportingin experimental studies and suggests that future ASSR studies should
explicitly explore different amplitudes of their stimuli.

﻿BMC Neurosci 2020, 21(Suppl 1):54

References
1. Thune H, Recasens M, Uhlhaas PJ. The 40-Hz auditory steady-state response inpatients with schizophrenia: a meta-analysis. JAMA Psychiatry 2016; 73(11).
2. Vierling-Claassen D, Siekmeier P, Stufflebeam S, Kopell N. Modeling GABA
alterations in schizophrenia: a link between impaired inhibition and alteredgamma and beta range auditory entrainment. Journal of Neurophysiology.
2008; 99(5).
3. Metzner C. [Re] Modeling GABA alterations in schizophrenia: a link between
impaired inhibition and gamma and beta auditory entrainment. ReScience3(1).
4. Metzner C, Zurowski B, Steuber V. The role of parvalbumin-positive
interneurons in auditory steady-state response deficits in schizophrenia. Sci
Rep. 2019; 9(1).

P194
Pattern separation based on rate coding in a biologically detailed
cerebellar network model
Ohki ­Katakura1, Reinoud ­Maex2, Shabnam K­ adir1, Volker ­Steuber2
1
University of Hertfordshire, Centre for Computer Science and Informatics
Research, Hatfield, United Kingdom; 2University of Hertfordshire,
Biocomputation Research Group, Hatfield, United Kingdom
Correspondence: Ohki Katakura (mistypail.1058@gmail.com)
BMC Neuroscience 2020, 21(Suppl 1):P194
The cerebellum is involved in motor learning, temporal information
processing and cognition. Inspired by the well-characterised anatomyof the cerebellum, several network models and theories of cerebellar function have been developed, such as the Marr-Albus-Ito theoryof cerebellar learning. However, although morphologically realistic
cerebellar neuronal models with realistic ion channel dynamics exist
in isolation, a complete cerebellar cortical model comprising suchbiologically detailed neurons is still missing. Sudhakar et al. have
implemented a cerebellar granular layer (GL) model composed of
biologically detailed granule and Golgi cells (GrCs and GoCs) [1]. Here,
we modified this model and integrated it with a multi-compartmental
PC model, which included detailed Hodgkin-Huxley type representations of ion channels [2]. The original GL model had a length of 1.5 mm
along the transversal axis. As parallel fibres (PFs), the axons of GrCs,
extend for 2.0 mm along this axis, we rescaled the GL network model
to 4.0 mm in transversal direction and placed the dendritic tree of the
PC model at the centre of the network. Additionally, to reduce thecomputational requirements, we employed a sparser density of 1.92
million GrCs per mm3 in our GL model. Each spine of the PC model
was connected to the nearest PF within the sagittal-vertical plane,
which resulted in 143,725 PF inputs to the PC model. Inhibitory input
from molecular layer interneurons (MLIs) to the PC was modelled
implicitly by providing inhibitory Poisson input from 1,695 spike generators. Most of our simulations were run with 5 Hz MF backgroundexcitation and 8 Hz background MLI inhibition, which resulted in PC
baseline spike rates between 50 and 60 Hz.
In a first set of simulations, our network was tested in a simple pattern separation task: a patch of excitatory mossy fibre (MF) input to
the GL was stimulated; the network learnt the input pattern based onlong-term depression (LTD) at PF-PC synapses; and the PC behaviour
in response to learnt and novel patterns was compared. The stimulated MF patch had a radius of 100 um. The stimulation resulted in theactivation of a cylindrical region of the GL above the patch. Activated
GoCs spread out of the patch along the transversal axis. The initial GrC
excitation lasted for about 5 ms, after which feedback inhibition from
GoCs reduced the GrC spike rate to about 50% of the peak value. The
resulting burst of GrC activity activated the PC model with a delay up
to 5 ms. In the presence of a sufficient amount of MLI inhibition, thePC firing rate initially increased sharply in response to stimulation of
the MF patch. After the MF input had been learnt based on LTD at the
PF-PC synapses, the PC spike rate increases in response to learnt MF
input disappeared, while equivalent novel MF stimuli still resulted in
spike rate increases. These simulation results predict that a biophysically detailed PC model embedded in a realistic cerebellar networkmodel can, under certain circumstances, employ a rate code to distinguish between learnt and novel MF input patterns.

Page 107 of 123

References
1. Sudhakar SK, et al. Spatiotemporal network coding of physiological mossy
fiber inputs by the cerebellar granular layer. PLoS Computational Biology.
2017; 13(9): e1005754.
2. De Schutter ER, Bower JM. An active membrane model of the cerebellar
Purkinje cell. I. Simulation of current clamps in slice. Journal of Neurophysi‑
ology. 1994; 71(1): 375-400.

P195
Associative memory performance in peripherally‑lesioned networks
repaired by homeostatic structural plasticity
Ankur ­Sinha1, Christoph ­Metzner2, Rod A­ dams3, Neil D­ avey1, Michael­Schmuker1, Volker ­Steuber1
1
University of Hertfordshire, Biocomputation Research Group, Hatfield,
United Kingdom; 2Technische Universität Berlin, Department of Software
Engineering and Theoretical Computer Science, Berlin, Germany;3
University of Hertfordshire, Biocomputation Group, Hatfield, United
Kingdom
Correspondence: Ankur Sinha (a.sinha2@herts.ac.uk)
BMC Neuroscience 2020, 21(Suppl 1):P195
In spite of a plethora of peripheral lesion experiments documentingthat structural plasticity causes large scale changes in brain networks[1-3], our understanding of the mechanisms of structural plasticity
remains limited. Structural plasticity acts over extended periods oftime, albeit at a slow rate, to modify network connectivity by the formation and removal of synapses. Alterations in network connectivity
are expected to affect network function, but the resulting functional
consequences of structural plasticity have not been studied in detail.
To study the activity dependent growth characteristics of neurites,
which underlie network reconfiguration, we previously developed anovel model of peripheral lesioning and subsequent repair in a balanced cortical Asynchronous Irregular (AI) spiking network [4]. The
network used in our model, which represents a physiological brain
network, was selected since it has been demonstrated to function as
an attractor-less associative memory store [5]. Using this new model,
we investigated the functional effects of repair mediated by homeostatic structural plasticity on the network. We stored associative
memories in the network and recalled them at different stages of thesimulation by stimulating a random subset of their neurons: before
deafferentation, after deafferentation but before repair, and after
deafferentation during repair. At each recall, recall performance wasquantified using a Signal to Noise ratio (SNR) metric [6].
Associative memories that include neurons deafferented by the
peripheral lesion experience a reduction in their recall performance
proportionate to the number of deprived neurons. Our results indicate that while structural plasticity restores activity of deafferented
neurons to pre-injury levels, it does not restore the performance ofthe stored associative memories. This suggests that associative memories stored before a peripheral lesion are not necessarily protectedin the repair process. Further research is needed to explore whether
the repair process can be modulated to retain the performance of thestored associative memories.
References
1. Rasmusson DD. Reorganization of raccoon somatosensory cortex following
removal of the fifth digit. Journal of Comparative Neurology. 1982; 205(4):313-26.
2. Keck T, et al. Massive restructuring of neuronal circuits during functional
reorganization of adult visual cortex. Nature Neuroscience. 2008; 11(10):1162-7.
3. Keck T, et al. Loss of sensory input causes rapid structural changes of inhibi‑
tory neurons in adult mouse visual cortex. Neuron. 2011; 71(5): 869-82.
4. Sinha A, et al. Growth Rules for the Repair of Asynchronous Irregular Neu‑ronal Networks after Peripheral Lesions. BioRxiv. 2019; 810846.
5. Vogels TP, Sprekeler H, Zenke F, Clopath C, Gerstner W. Inhibitory plasticity
balances excitation and inhibition in sensory pathways and memory net‑
works. Science. 2011; 334(6062): 1569-73.
6. Dayan P, Willshaw DJ. Optimising synaptic learning rules in linear associative
memories. Biological Cybernetics. 1991; 65(4): 253-65.

﻿BMC Neurosci 2020, 21(Suppl 1):54

P196
Robustness of ultrasonic modulation of the subthalamic nucleus
to GABAergic perturbation­ artens1,
Thomas ­Tarnaud1, Wout ­Joseph1, Ruben ­Schoeters1, Luc M
Timothy Van ­Renterghem2, Emmeric ­Tanghe1
1
University of Ghent ‑ IMEC, INTEC WAVES, Ghent, Belgium; 2Universityof Ghent, INTEC WAVES, Ghent, Belgium
Correspondence: Thomas Tarnaud (thomas.tarnaud@ugent.be)
BMC Neuroscience 2020, 21(Suppl 1):P196
Introduction: Deep brain stimulation (DBS) is a surgical treatment
for movement and neuropsychiatric disorders. Here, the subthalamic nucleus (STN) is the most common target for the treatment ofadvanced Parkinson’s disease (PD). Although DBS has proven effective, the procedure is associated with surgical risks such as infection
and haemorrhage. Consequentially, we investigated the possibilityof using ultrasound (US) as a non-invasive and reversible alternativeof conventional DBS. Here, we expand on our study on the spiking
behaviour of a computational STN model [1], insonicated with continuous-wave and pulsed US of different intensities. In particular, the sensitivity of the simulated STN response to hyperpolarizing input (e.g.,
GABAergic globus pallidus afferents) is investigated.
Methods: A computational model for insonication of the STN is created by combining the Otsuka-model of a plateau-potential generating STN neuron [2] with the bilayer sonophore model [3,4]. After
careful validation of our model implementation by comparison withtheoretical and experimental literature, simulations are performedof the STN-neuron insonicated with different ultrasonic intensities
and pulse waveforms. The robustness of the simulated response to
GABAergic input is tested by injecting brief hyperpolarizing currents.
Results: Our model results predict intensity dependent spiking
modes of the STN neurons. For continuous waveforms, three different
observed spiking modes in order of increasing ultrasonic intensity are
low-frequency spiking, high-frequency (>120 Hz) spiking with significant spike-frequency and spike-amplitude adaptation, and a silenced
mode. Simulation results indicate that only the silenced mode isrobust to brief hyperpolarizing input. In contrast, the STN response
will saturate robustly to the pulse repetition frequency in pulsed US,
for sufficiently large intensity and pulse repetition frequency.
Conclusion: Model results of the ultrasonically stimulated plateaupotential generating STN predict intensity dependent spiking modes
that could be useful for the treatment of PD. High-frequency spikingof the STN might “jam” pathological network activity or result in thecreation of an information lesion due to short-term synaptic depression, which are potential mechanisms ascribed to conventional DBS. In
contrast, the silenced mode in which the STN transmembrane potential is fixed to a stable plateau might be functionally equivalent to subthalamotomy and to depolarization blockage of STN efferents during
DBS. The former and latter STN mode is induced robustly by pulsedand continuous wave US, respectively.
References
1. Tarnaud T, Joseph W, Martens L, Tanghe E. Computational modeling of
ultrasonic subthalamic nucleus stimulation. IEEE Transactions on Biomedical
Engineering. 2018; 66(4): 1155-64.
2. Otsuka T, Abe T, Tsukagawa T, Song WJ. Conductance-based model ofthe voltage-dependent generation of a plateau potential in subthalamic
neurons. Journal of Neurophysiology. 2004; 92(1): 255-64.
3. Plaksin M, Shoham S, Kimmel E. Intramembrane cavitation as a predictive
bio-piezoelectric mechanism for ultrasonic brain stimulation. PhysicalReview X. 2014; 4(1): 011004.
4. Lemaire T, Neufeld E, Kuster N, Micera S. Understanding ultrasound neu‑
romodulation using a computationally efficient and interpretable modelof intramembrane cavitation. Journal of Neural Engineering. 2019; 16(4):046007.

Page 108 of 123

P197
Computational modelling of the Locus Coeruleus
Ruben ­Schoeters1, Thomas T­ arnaud1, Wout ­Joseph1, Luc ­Martens1,
Robrecht ­Raedt2, Emmeric ­Tanghe1
1
University of Ghent ‑ IMEC, INTEC WAVES, Ghent, Belgium; 2Universityof Ghent, Department of head and skin ‑ 4Brain lab, Ghent, Belgium
Correspondence: Ruben Schoeters (ruben.schoeters@ugent.be)
BMC Neuroscience 2020, 21(Suppl 1):P197
The locus coeruleus (LC) is one of the most dominant noradrenergic
systems in the brain that supplies the central nervous system with norepinephrine through widespread efferent projections. Consequently,
it plays an important role in attention, feeding behaviour and sleepto-wake transition [1]. Moreover, studies have shown that the locus
coeruleus is correlated to the anticonvulsive action of vagus nerve
stimulation (VNS) [2]. To date, the underlying mechanisms of VNS
and the LC are, however, not fully understood. Therefore, we derived
a computational model, such that in silico investigations can be performed. Based on the work of Carter et al. [3], we created a single
compartment model that matched our in vivo measurements. These
were extracted from rat brains at the 4Brain lab. The original model
created by Carter et al. was a conductance-based model of the locus
coeruleus and hypocretin neurons, used for the investigation of thesleep-to-wake transition. When the hypocretin neurons are omitted,
our measured tonic firing rate of 3.35 ± 0.49Hz could not be reachedwith the original two compartment model by means of continuous
current injection. The maximal achievable tonic firing rate was 0.75
Hz for a current of 0.4 A/m2, while a bursting behaviour followed bydepolarization block was observed for higher inputs. When combined
into a single compartment model, the required frequency is reachedwith a 0.39 A/m2 current injection. There were no notable differencesin state occupancies that could explain the difference in firing rate.
Therefore, we concluded that the lower firing rate observed in the twocompartment model is solely due to spatial filtering. Finally, we compared the pinch response. The pinch was modelled as a rectangularcurrent pulse. With an amplitude of 0.0314 A/m2 and pulse duration of
0.9 s, an equivalent firing rate (13.64 ± 2.75Hz vs.13.86Hz) and refractory period (1.186 ± 0.234s vs.1.09s, the measurements and model,
respectively) are observed.
References
1. Purves D, et al. Neuroscience, volume 3. 2004.
2. Raedt R, et al. Increased hippocampal noradrenaline is a biomarker for
efficacy of vagus nerve stimulation in a limbic seizure model. Journal of
Neurochemistry. 2011; 117(3): 461-9.
3. Carter ME, et al. Mechanism for Hypocretin-mediated sleep-to-wake transi‑
tions. Proceedings of the National Academy of Sciences. 2012; 109(39):E2635-44.

P198
Voltage‑dependent synaptic plasticity in magnetic tunnel junctions
Saeideh R A­ kbarabadi1, Mojtaba M A­ sl2, Peter ­Tass3
1
University of Guilan, Department of Physics, Rasht, Iran; 2Institutefor Advanced Studies in Basic Sciences, Department of Physics, Zanjan,
Iran; 3Stanford University, Neurosurgery, Stanford, California, United Statesof America
Correspondence: Saeideh R Akbarabadi (saeidehramezani7@gmail.com)
BMC Neuroscience 2020, 21(Suppl 1):P198
Spike-timing-dependent plasticity (STDP) is a fundamental learning
mechanism that shapes plastic synaptic strengths in brain networks
according to pre- and post-synaptic spike times [1]. Later, a model ofvoltage-based STDP was proposed based on the postsynaptic membrane potential to explain experimentally observed connectivity
patterns in cortex [2]. Synaptic plasticity plays a key role in memory
retention by modulating functional cortical circuitry in memory networks. The development of solid-state devices in recent years provided a means for computational implementation and experimental
realization of neuromorphic structures designed to emulate adaptive
behavior of synapses in brain. Particularly, spin-polarized transport
through magnetic tunnel junctions (MTJs) is a well-characterized﻿BMC Neurosci 2020, 21(Suppl 1):54

mechanism for the implementation of learning process due to therapid and high-density information storage capabilities of MTJs as amemory device [3].
Previously, it has been shown that the emergent synaptic structure
between a pair of neurons characterized by two reciprocally coupledsynapses with STDP (see Fig. 1A) can be theoretically predicted by
the effective synaptic strength in the two-neuron motif, i.e. the ratioof relative synaptic strengths to their sum [4]. In this study, we considered a two-terminal single-molecule MTJ that consists of two ferromagnetic (FM) cobalt electrodes separated by a phenyl dithiol (PDT)
molecule (see Fig. 1B, top) and investigated transport properties using
a non-equilibrium Green’s function (NEGF) formalism. By introducing
an effective spin-polarized tunneling conductance, i.e. the ratio ofrelative conductances in parallel (P) and anti-parallel (AP) configurations to their sum, we show that the change in the two-component
conductance crucially depends on the bias voltage applied to the
MTJ where its behavior is reminiscent of the classical STDP (Fig 1A-B,
bottom).

Fig. 1 Voltage-dependent synaptic plasticity in the magnetic tunnel
junction. A Two-neuron motif (top) and the change in the synaptic
strengths induced by spike timings (bottom). B Two-terminal singlemolecule magnetic tunnel junction (top) and the voltage-dependentchange in the effective tunneling conductance (bottom)
While the optimized window for tunneling conductance is a propertyof the central molecule, the asymmetric change in the effective tunneling conductance is determined by the FM electrodes, and hence,
can be controlled by chemical engineering of the junction. The spinpolarized current is constant near zero bias voltage, however, whena voltage is applied to the junction, the molecular energy levels are
positioned within the bias window and the spin-polarized current isincreased (decreased) for positive (negative) bias voltage. This provides a suitable framework to study voltage-dependent long-term
potentiation (LTP) and depression (LTD) in MTJs. Ultimately, our results
may contribute to the further development of neuromorphic memory
devices engineered based on the adaptive properties of synapses inbrain networks.
References
1. Bi GQ, Poo MM. Synaptic modifications in cultured hippocampal neurons:
dependence on spike timing, synaptic strength, and postsynaptic cell type.
Journal of Neuroscience. 1998; 18(24): 10464-72.
2. Clopath C, Büsing L, Vasilaki E, Gerstner W. Connectivity reflects coding:a model of voltage-based STDP with homeostasis. Nature Neuroscience.
2010; 13(3): 344-352.

Page 109 of 123

3. Ramezani Akbarabadi S, Golsanamlou Z, Rahimpour Soleimani H. Studyof Length-Dependent Tunneling Magnetoresistance in Two Phenyl Based
Molecules. Current Physical Chemistry. 2014; 4(3): 285-9.
4. Asl MM, Valizadeh A, Tass PA. Delay-induced multistability and loop forma‑tion in neuronal networks with spike-timing-dependent plasticity. Scientific
Reports. 2018; 8(1): 1-5.

P199
Stimulation‑induced decoupling by spike‑timing‑dependent
plasticity in neuronal networks with propagation delays
Mojtaba Madadi ­Asl1, Alireza ­Valizade1, Peter A ­Tass2
1
Institute for Advanced Studies in Basic Sciences, Department of Physics,
Zanjan, Iran; 2Stanford University, Department of Neurosurgery, Stanford,
California, United States of America
Correspondence: Mojtaba Madadi Asl (mojtabamadadi7@gmail.com)
BMC Neuroscience 2020, 21(Suppl 1):P199
Several neurological disorders such as Parkinson’s disease and epilepsy are characterized by pathological neuronal synchronization incortical and sub-cortical areas. Therapeutic brain stimulation techniques aimed at shifting the pathological dynamics of the diseased
brain towards healthy attractor states, are employed to restore physiological patterns of synaptic connectivity by decoupling strongly
connected neurons [1]. Dynamics of cortical neuronal populations
crucially depends on the synaptic connectivity of the cortex which
continually change by spike-timing-dependent plasticity (STDP) [2]. It
has recently shown that the effect of the STDP on the structure andthe dynamics of the neuronal networks is profoundly dependent on
the delay in transmission of the signals between the neurons [3]. In
particular, propagation delays lead to multi-stability of the network
structure such that strong bidirectional loops, loosely connected pairsof neurons, or asymmetric unidirectional connections can emerge
between different pairs of neurons under the influence of STDP [4].
In the present study, by theoretical analysis of a reciprocally coupled two-neuron motif we show that the decoupling of the neurons
and neuronal populations by stimulation depends on the imbalanceof STDP potentiation/depression rates and time constants and thetransmission delays. Then the theoretical predictions were numerically tested in a two-layer model of oscillatory networks composed of
excitatory and inhibitory neurons where the individual neurons fireirregularly. The patterned stimulation is delivered simultaneously to
all neurons (excitatory and inhibitory) in both layers which are connected to each other by plastic excitatory synapses characterized byinterlayer propagation delays and are modified according to the STDP
rule. Figure 1A shows that the stimulation pattern can shape the interlayer connections by modulating the slowly evolving synaptic dynamics and accordingly desynchronized the neuronal activity. In this way,
the synaptic strengths between the layers can change from a strongly
coupled regime (Fig. 1B, grey) to a more physiologically favored
weakly connected state (Fig. 1B, colored distribution) due to stimulation. Furthermore, two-neuron loops which were prevalent before
the stimulation onset, are entirely eliminated after the stimulation
due to the stimulation-induced decoupling effect (Fig. 1C, red curve).
Our results may contribute to the further optimization of therapeutic
brain stimulation protocols and thus can provide new insights to thetreatment of patients with hyper-synchronized neurological brain
disorders.

﻿BMC Neurosci 2020, 21(Suppl 1):54

Page 110 of 123

information. However, circuit-level information processing has notbeen well-understood.
Here we investigated this question by characterizing how MF firings
transform into PC outputs in recording data from those cells (n=110
and 135, respectively) in rhesus monkeys that were performing a sensorimotor task (M. Mulatta; n=2). We trained the animals for a saccadic eye movement task, where they followed a target jumping backand forth between two horizontal target locations. The fast pace andrepetitive nature of the task led to a gradual decline in saccade velocities (fatigue).
We found that the firing rates of MFs linearly encoded eye speed andsaccade duration, consistent with previous studies (e.g. [1]). Using thelinear rate coding property of MFs and also PCs, we constructed therate coding models of individual cells from the data and formed thevirtual populations of those models for each cell type. This method
enabled us to analyze eye speed-dependent variability of the population responses beyond the firing rate across trials.
By using the virtual population of MFs and PCs, we found that theactivities of MFs and PCs can be both characterized by low dimensional “manifolds” [2] that resemble the limit cycles. Here, the PC
manifold is higher-dimensional as compared to that of MFs and has
more complex representations of variability in eye movements.
Nonetheless, there exists a linear transformation between the two
populations [3], which can accurately predict the average and also
velocity-dependent variability in the firing rate of individual neurons.
Based on these results, we suggest that the MFs deliver a compressed,
low dimensional copy of sensorimotor information from other brain
areas, possibly via convergence [3], and the cerebellar cortical circuit
decompresses/transforms it to higher dimensional outputs, carrying
the reorganized representation of the behavioral variability.
Fig. 1 Stimulation-induced decoupling of the synaptic strengths inthe two-layer network. A Population activity (A1/A2) and interlayermean coupling (G21/G12). B Distribution of the interlayer synaptic
strengths before (grey) and after (colored) stimulation cycle. C The
number of two-neuron loops (l2) and the interlayer connection asym‑
metry index (C)References
1. Tass PA, Majtanik M. Long-term anti-kindling effects of desynchronizing
brain stimulation: a theoretical study. Biological Cybernetics. 2006; 94(1):58-66.
2. Song S, Abbott LF. Cortical development and remapping through spiketiming-dependent plasticity. Neuron. 2001 Oct; 32(2): 339-50.
3. Madadi Asl M, Valizadeh A, Tass PA. Dendritic and axonal propagation delays
determine emergent structures of neuronal networks with plastic synapses.
Scientific Reports. 2017; 7: 39682.
4. Madadi Asl M, Valizadeh A, Tass PA. Delay-induced multistability and loop
formation in neuronal networks with spike-timing-dependent plasticity.
Scientific Reports. 2018; 8(1): 1-5.

P200
Computational modeling of the input/output mappingin the cerebellar cortex
Akshay ­Markanday1, Sungho H­ ong2, Junya ­Inoue1, Peter D­ icke1, Erik De­Schutter2, Peter ­Thier1
1Hertie Institute for Clinical Brain Research, Department of Cognitive
Neurology, Tübingen, Germany; 2Okinawa Institute of Scienceand Technology, Computational Neuroscience Unit, Onna‑son, Japan
Correspondence: Sungho Hong (shhong@oist.jp)
BMC Neuroscience 2020, 21(Suppl 1):P200
The cerebellar cortex is a brain region deeply involved in sensorimotor coordination and adaptation. It receives external inputs via axons
called the mossy fibers (MF), delivering diverse information, including
sensory and motor signals, from other brain regions. Then, the output neurons, Purkinje cells (PC), transmit the result of computationby the network. Many studies have elucidated how different stagesof computation in this neural circuit represents sensory and motor

References
1. Ohtsuka K, Noda H. Burst discharges of mossy fibers in the oculomotor ver‑mis of macaque monkeys during saccadic eye movements. Neuroscience
Research. 1992; 15: 102–114.
2. Gallego JA, et al. Neural manifolds for the control of movement. Neuron.
2017; 94: 978–984.
3. Tanaka H, Ishikawa T, Kakei S. Neural evidence of the cerebellum as a statepredictor. Cerebellum. 2019; 18: 349–371.

P201
Frequency‑separated principal components analysis of cortical
population activity
Jean‑Philippe Thivierge
University of Ottawa, School of Psychology, Ottawa, Canada
Correspondence: Jean‑Philippe Thivierge (jean‑philippe.thivierge@uottawa.ca)
BMC Neuroscience 2020, 21(Suppl 1):P201
Neocortical activity is characterized by the presence of low-dimensional fluctuations in firing rate that are coordinated across neurons[1]. Despite a wealth of experiments and models, the role of lowdimensional fluctuations remains unclear, in part due to limited data
analysis techniques. While several approaches exist to perform dimensionality reduction [2], there is a lack of methods designed to extract
frequency-specific, low-dimensional fluctuations from neural signals.
This is true even with methods aimed at finding rotational structurein PCA [3], as these approaches suffer from a lack of frequency‐specificseparation of components.
Here, we describe a technique termed frequency-separated principal
components analysis (FS-PCA) that addresses this issue. This talk isorganized as a tutorial where we first show toy examples that apply
FS-PCA to artificial signals. Then, we provide an application of FS-PCA
to both spontaneous and evoked cortical activity. Finally, we discuss
the interpretation, limitations, and possible extensions of this technique to problems in systems neuroscience.
FS-PCA is based on recent theoretical advances on the eigenspectrumof Hankel matrices [4]. As a first example, we consider a sine wave withadded zero-mean Gaussian noise (Fig.1a). We show that this signal canbe converted to a Hankel matrix (Fig. 1b) whose eigenspectrum contains 2f + 1 largest eigenvalues, where f is the number of characteristic

﻿BMC Neurosci 2020, 21(Suppl 1):54

frequencies of the original signal. The reconstructed signal obtained
from FS-PCA closely matches the amplitude, phase, and frequency ofthe original signal (Fig. 1c).

Page 111 of 123

t-Distributed Stochastic Neighbor embedding (t-SNE) on it to evaluate
how distinguishable are distinct behavioral states (rest, tremor, voluntary movements). We also describe which data features (both spectral
and time-domain ones) contribute most to such classification.
P203
A neural mechanism of working‑memory manipulation
in a categorization‑task performance
Yoshiki Kashimori, Hikaru Tokuhara
The University of Electro‑Communications, Department of Engineering
Science, Tokyo, Japan
Correspondence: Yoshiki Kashimori (kashi@pc.uec.ac.jp)
BMC Neuroscience 2020, 21(Suppl 1):P203

Fig. 1 Frequency-separated principal components analysis ofartificial and cortical signals. a Example of noisy sine wave. b Hankel
matrix obtained from “a”. c Top, original and reconstructed signals.
Bottom, power spectra. d Reconstruction of V1 mean population
activity. e Distribution of ranked eigenvalues. Inset, relation between
rank and characteristic frequency of each component
Next, we apply FS-PCA to population recordings from macaque V1
cortex. We show that the first dimension of the reconstructed signal
captures the slow, low-frequency fluctuations in mean population
activity observed over time (Fig. 1d, red line). Adding further dimensions markedly improves the reconstruction of population activity(Fig.1d, blue line). Overall, ranked eigenvalues obtained from FS-PCA
followed an approximate power-law where the highest ranked dimensions captured a large proportion of the data (Fig.1e). In turn, highestranked dimensions had a lower characteristic frequency than lower
dimensions (Fig.1e, inset). In sum, these results suggest that while abroad spectrum of frequencies contributed to population activity,
fluctuations in spontaneous activity were dominated by low-frequency components.
References
1. Cohen MR, Kohn A. Measuring and interpreting neuronal correlations.
Nature Neuroscience. 2011; 14: 811–819.
2. Cunningham JP, Yu BM. Dimensionality reduction for large-scale neuralrecordings. Nature Neuroscience. 2014; 17: 1500-1509.
3. Churchland MM, et al. Neural population dynamics during reaching. Nature.
2012; 487: 51-56.
4. Li Z, Li W, Zhao X. Feature frequency extraction based on principal com‑ponent analysis and its application in axis orbit. Shock and Vibration. 2018;
2530248.

P202
Analysis of Parkinson’s disease tremor with t‑SNE
Dmitrii Todoro
Centre de Recerca Matemàtica, Computational Neuroscience Group, Spain
Correspondence: Dmitrii Todoro (todorovdi@gmail.com)
BMC Neuroscience 2020, 21(Suppl 1):P202
Many symptoms of Parkinson’s disease, despite a long history of
research, are still not completely understood. In particular, there is noclear understanding on how Parkinsonian rest tremor is generated andeven how it can be detected and distinguished from voluntary movements in neural recordings. We use multimodal (MEG and STN LFP)
brain data, recorded from tremor-dominant PD patients and employ

Working memory has a function by which temporal information is
maintained and recognized in the brain, and is ubiquitous in various
brain regions. A growing body of working memory research has indicated the neural mechanism underlying the maintenance of working
memory. Several studies have demonstrated that working memory is
maintained by a persistent activity of neural assemblies. Other studies
have proposed that it is maintained by a short-term synaptic plasticity. The mechanism of working memory maintenance is still a matterof debate. Furthermore, it is unclear how working memory is linked tobehavior and decision-making.
In this study, to clarify the neural mechanisms underlying the maintenance and manipulation of working memory, we focus on the functionof prefrontal cortex in a delayed match-to-categorization task studied by Freedman et al. [1]. In this task, monkeys were presented witha sample and a test stimulus, separated by a delay period, and weretrained to judge whether these stimuli were from the same category.
Freedman et al. showed that working memory of category information
was formed in the PFC. Our previous model demonstrated the neural mechanism of the working memory shaped in the PFC [2]. In thisstudy, we aim to understand a unified mechanism of working memory
maintenance and its manipulation for behavior. We develop a networkmodel that performs the maintenance and recognition of temporal
information of a sample and a test stimulus. The model consists of the
networks of IT and PFC. The PFC model is further constructed with apositive-feedback-loop layer, a recurrent network, and a decision
layer. The positive-feedback-loop layer produces a persistent activity of a previously presented stimulus, allowing the layer to maintain
information of a sample stimulus as working memory. The recurrent
network encodes the temporal information of a sample stimulus and atest stimulus. The learning of temporal information was made by Backpropagation Through Time method. The decision layer has neurons
responding to a match and a non-match trial. We also investigate thediscrimination ability of our model for more complex tasks that have
longer temporal sequences and many category numbers.
We demonstrate that maintenance of working memory and encodingof temporal sequence are sequentially manipulated in different areasof the PFC. We also show that the temporal sequence is encoded by
activity pattern of the recurrent circuit, independently of task decision.
The sparseness of activity pattern increases with increasing the number of category. The principal component analysis of activity patterns
reveals that the activity patterns of non-match trials move far awayfrom the activity patterns of match trials as the learning proceeds. Furthermore, we show that the decision of task trials is adjusted by thelearning of the connections between recurrent neurons representing
the activity patterns and decision neurons, according to task context.
References
1. Freedman DJ, Riesenhuber M, Poggio T, Miller EK. A comparison of primate
prefrontal and inferior temporal cortices during visual categorization. Jour‑nal of Neuroscience. 2003; 23(12): 5235-46.
2. Abe Y, Fujita K, Kashimori Y. Visual and Category Representations Shaped by
the Interaction Between Inferior Temporal and Prefrontal Cortices. Cognitive
Computation. 2018; 10(5): 687-702.

﻿BMC Neurosci 2020, 21(Suppl 1):54

P204
Stabilization of spiking neural networks using stochastic
and high‑frequency neurostimulation disrupts seizure‑liketransitions
Scott ­Rich1, Axel ­Hutt2, Frances S­ kinner1, Jeremie ­Lefebvre3, Taufik­Valiante1
1
Krembil Research Institute, Division of Clinical and Computational
Neuroscience, Toronto, Canada; 2INRIA Nancy Grand Est, MIMESIS,
Strasbourg, France; 3University of Ottawa, Department of Biology, Ottawa,
CanadaCorrespondence: Scott Rich (sbrich@umich.edu)
BMC Neuroscience 2020, 21(Suppl 1):P204
Epilepsy is one of the most common serious neurological disorders
in the world, typified by repeated unprovoked seizures. Such seizures
are characterized by an abrupt transition to a hyper-active, and often
hyper-synchronous, brain state [1]. These in vivo transitions sharenoted similarities to mathematical transitions occurring through bifurcations, suggesting that the study of seizure onset is fertile ground forinterdisciplinary research [2]. Neuromodulation represents a promising avenue for clinical intervention in patients with epilepsy, although
the field is still wanting for principled understandings of how suchdevices mitigate seizure onset [3]. Such understanding of neuromodulatory mechanisms may allow for better stimulation strategies toreduce the burden of seizures.
Here, we use a network model to probe how sudden transitions into
oscillatory dynamics, which share clear parallels with seizure onset, areinfluenced by both intrinsic and extrinsic inputs. These extrinsic inputs
can be viewed as a model of a neuromodulatory intervention. Building
on a previous model of cortical gamma activity [4], the model consistsof 500 excitatory and 500 inhibitory all-to-all connected Poisson neurons with heterogeneity implemented in their rheobase analogues.
A combination of numerical simulations and mean-field analyses
revealed that high variance and/or high frequency stimulation waveforms were most efficient in preventing multi-stability in these networks, where multi-stability serves as a mathematical harbinger ofthe sudden transition between asynchronous and oscillatory network
dynamics. Furthermore, our analysis showed that stabilization of neural activity is via a selective recruitment of inhibitory cells, providing atheoretical undergird for the known key role these cells play in boththe healthy and diseased brain. Interestingly, this effect occurred
without the need to precisely “target” the inhibitory population,highlighting that neuromodulatory devices utilizing these stimulation paradigms may not need to be excessively “precise” in order toelicit the desired response. While deep brain stimulation systems havelong been thought to affect neural circuits via the creation of a “functional” or “informational” lesion [5], potentially through depolarization
blockade [6], these findings provide theoretical support for a distinct
mechanism of action through selective interneuronal activation.
Taken together, these results provide new vistas on the underlying
mechanisms through which neuromodulatory approaches stabilize
neural microcircuit activity, utilizing a variety of computational tools
including numerical simulation, mean-field reduction, and stochastic
stability analysis.
References
1. Reynolds EH. Introduction: epilepsy in the world. Epilepsia. 2002; 43: 1-3.
2. Wending F, et al. Computational models of epileptiform activity. Journal of
Neuroscience Methods. 2016; 260: 233-251.
3. Salanova V. Deep brain stimulation for epilepsy. Epilepsy & Behavior. 2018;18(6): 514-532.
4. Herrmann C, et al. Shaping neural oscillations with periodic stimulation.
Journal of Neuroscience. 2016; 36(19): 5328-5337.

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5. Grill WM, et al. Deep brain stimulation creates an informational lesion of thestimulated nucleus. Neuroreport. 2004; 15(7): 1137-1140.
6. McIntyre CC, et al. Uncovering the mechanism(s) of action of deep brain
stimulation: activation, inhibition, or both. Clinical Neurophysiology. 2004;
115(6): 1239-1248.

P205
Development of efficient connectivity for reliable signaltransmission through STDP
Hedyeh ­Rezaei1, Ad ­Aertsen2, Arvind K­ umar3, Alireza ­Valizade1
1
Institute for Advanced Studies in Basic Sciences, Physics, Zanjan,
Iran; 2Bernstein Center Freiburg, University Freiburg, Biology, Freiburg,
Germany; 3KTH Royal Institute of Technology, Computational Brain
Science, Stockholm, Sweden
Correspondence: Hedyeh Rezaei (h.rezaei@iasbs.ac.ir)
BMC Neuroscience 2020, 21(Suppl 1):P205
A fundamental requirement for brain function is the efficient communication between different brain regions. This requirement turns out
not to be trivial in the presence of various noise sources in the brain.
Two possible strategies taken by the nervous system to face the abundance of noise could be to integrate signals either across time (firingrate) or across a population of neurons (synchrony) to retain the signal
in spite of the uncorrelated noisy background. However, the reliabletransmission of signals requires either strong and sparse or dense andweak connections for reliable transmission of rate codes or synchrony
codes, respectively [1].
However, the typical connectivity between brain regions is neither
strong nor dense. Recent work has highlighted the importance of
feedback connections. Feedback connections can strengthen thesignals through reverberations in the bi-directionally coupled modules [2]. This mechanism depends on the matching of the total effective delay along forward and backward intermodule connections andthe period of the local oscillations in the modules, determined by thewithin and between module connections. This raises the question
how the networks in the brain might tune their parameters in a rangethat favours such reliable and economic signal transmission. Here,
we tested if the biological synaptic plasticity rules can self-organize
an initially disorganized network to such a tuned regime for reliable
signal transmission. Inspired by Hebb’s postulate [3], we hypothesizedthat in the presence of abundant synaptic connections between themodules in a developmental stage of the nervous system, only thosewith matching parameters for reliable transmission can potentiate.
While potentiation of these synapses facilitates the reliable transmission of signals, depression of other ‘unfit’ connections reduces the
structural cost and gives rise to an efficient substrate for reliable signal transmission. We found that with STDP, the intermodule connections with delays matching the oscillation period of a single network
module were potentiated, whereas other connections were ultimately
eliminated (Fig. 1a-b). We also found how this mechanism facilitatedreliable signal transmission to downstream areas in case the network
consisted of several (up to 10) such modules (Fig. 1c). Our results suggest that STDP can lead to the emergence of networks with tuned
parameters for reliable and efficient signal transmission out of an initially inefficient network with an extravagant structural cost.

﻿BMC Neurosci 2020, 21(Suppl 1):54

Fig. 1 Development of efficient connectivity for reliable signaltransmission. Scatter plot shows the initial and final weights anddelays of forward and backward synaptic connections. Distribution of
the delays in the initial and final states in two directions. Sum of peaksof final distribution of feedforward and feedback delays matched theresonance period (20-25ms) of the network modules
References
1. Kumar A, Rotter S, Aertsen A. Spiking activity propagation in neuronal net‑
works: reconciling different perspectives on neural coding. Nature Reviews
Neuroscience. 2010; 11(9): 615-27.
2. Rezaei H, Aertsen A, Kumar A, Valizadeh A. Facilitating the propagation of
spiking activity in feedforward networks by including feedback. BioRxiv.
2019; 712547.
3. Bi GQ, Poo MM. Synaptic modification by correlated activity: Hebb’s postu‑
late revisited. Annual Review of Neuroscience. 2001 Mar; 24(1): 139-66.

P206
Parameter exploration in neuron and synapse models driven
by stimuli from living neuron recordings
Manuel Reyes‑Sanchez1, Irene E­ lices1, Rodrigo ­Amaducci1, Francisco B­Rodriguez2, Pablo ­Varona2
1
Universidad Autónoma Madrid, Grupo de Neurocomputación Biológica,
Madrid, Spain; 2Universidad Autónoma Madrid, Ingeniería Informática,
Madrid, Spain
Correspondence: Ronald Calabrese (manuel.reyes@uam.es)
BMC Neuroscience 2020, 21(Suppl 1):P206
In this work, we present an approach to automatically explore neuron
and synapse model parameter to archive target dynamics or characterize emergent phenomena which rely on the temporal structure of
biological recordings that are used as inputs to the models. The associated exploration and mapping allow us to assess the role of different elements in the equations of the neuron and synapse models tobuild a nontrivial integration of sequential information, which is also
reflected in the time course of the corresponding model response.
We illustrate this methodology in the context of dynamical invariants
defined as cycle-by-cycle preserved relationships between time intervals that build robust sequences in neural rhythms. We have recently
unveiled the existence of such invariants in the pyloric CPG of crustacean, even under the presence of intrinsic or induced large variability
in the rhythms [1]. The proposed strategy can be generalized for many
types of neural recordings and models.

Page 113 of 123

During such protocol, we input biological data with a characteristic
temporal structure to different model neurons. The biological recordings are preprocessed online to adapt the corresponding time andamplitude scales to those of the synapse and neuron models using a
set of algorithms developed in our previous works [2,3]. Our methodology can then map the neuron and synapse parameters that yield apredefined dynamic taking into account the temporal structure of themodel output. The algorithms allow for a full characterization of theparameter space that contributes to the generation of the predefined
dynamics.
To illustrate this protocol that combines experimental recordings andtheoretical paradigms, we have applied it to the search for dynamical
invariants established between a living CPG cell and a model neuron
connected through a graded synapse model. Dynamical invariants
are preserved cycle-by-cycle, even during transients. In our validation
tests, we have mapped the presence of a linear relationship, i.e. aninvariant, between the interval defined by the beginning of the bursting activity of the two neurons (first-to-first spike interval between
the living and model neurons) and the instantaneous period of theirsequence in such hybrid circuit.
The protocol has been used to assess the role of model and synaptic
parameters in the generation of the dynamical invariant, achieving ahigh efficient mapping in a few minutes. We argue that this approach
can also be employed to readily characterize optimal parameters in
the construction of hybrid circuits built with living and artificial neurons and connections, and, generally, to validate neuron and synapse
models.
Acknowledgements: Funded by AEI/FEDER PGC2018-095895-B-I00
and TIN2017-84452-R.
References
1. Elices I, Levi R, Arroyo D, Rodriguez FB, Varona P. Robust dynamical invariants
in sequential neural activity. Scientific Reports. 2019; 9(1): 1-3.
2. Amaducci R, Reyes-Sanchez M, Elices I, Rodriguez FB, Varona P. RTHybrid: astandardized and open-source real-time software model library for experi‑mental neuroscience. Frontiers in Neuroinformatics. 2019; 13:11.
3. Reyes-Sanchez M, Amaducci R, Elices I, Rodriguez FB, Varona P. Automatic
adaptation of model neurons and connections to build hybrid circuits withliving networks. Neuroinformatics. 2020; 1-7.

P207
Hybrid robot driven by a closed‑loop interaction with a living
central pattern generator with online feedback
Rodrigo ­Amaducci1, Irene ­Elices1, Manuel Reyes‑Sanchez1, Alicia
Garrido‑Peña1, Rafael L­ evi1, Francisco B R­ odriguez2, Pablo ­Varona2
1
Universidad Autónoma Madrid, Grupo de Neurocomputacion Biologica,
Madrid, Spain; 2Universidad Autónoma Madrid, Ingeniería Informática,
Madrid, Spain
Correspondence: Rodrigo Amaducci (amaducci.rodrigo@gmail.com)
BMC Neuroscience 2020, 21(Suppl 1):P207
A hybrid robot or hybrot is a technology that combines living cells andnetworks with robotics. This technology is largely undeveloped andhas been mainly implemented with neuron cultures and multichannel
electrode arrays [1,2]. Hybrots have a lot of potential to study neuralnetworks properties involved in the control of locomotion, sensorimotor transformation and behavior.
Central pattern generators (CPG) are neural circuits that produce
robust rhythmic sequences involved in motor functions such as
breathing or walking. Because of their role in generating and coordinating motor rhythms, bio-inspired CPGs have been widely employed
in robotic paradigms [3] including the design of novel mechanisms forautonomous locomotion [4]. However, the intrinsic mechanisms thatgive rise to the coordination of living CPG dynamics have not beenused yet for hybrid robot implementation.
In this work, we present the first hybrot controlled by a living CPG fromthe crab Carcinus Maenas. The robot and the living neural circuit areconnected following a closed-loop protocol that involves a dynamicclamp setup to communicate both elements through Bluetooth signaling. We show that effective robotic locomotion is achieved when
it is controlled and coordinated by the flexible rhythmic sequences

﻿BMC Neurosci 2020, 21(Suppl 1):54

produced by the circuit of living motoneurons. The robot is equippedwith a light sensor that sends a sensory feedback to the CPG in theform of intracellular current injection. We report the analysis of thepresence of dynamical invariants in the intervals that build up thesequential activations of the living circuit [5] and how they are transmitted to the robot resulting in a coordinated locomotion. In turn,the robotic sensory feedback is translated into a variation of the living
network activity while keeping the motor sequence, which results in a
coherent response to the change in the environmental light.
Acknowledgements: We acknowledge support from AEI/FEDER
PGC2018-095895-B-I00 and TIN2017-84452-R.
References
1. Potter SM. Hybrots: hybrid systems of cultured neurons+ robots, for study‑
ing dynamic computation and learning. In Simulation of Adaptive Behavior
7: Workshop on Motor Control in Humans and Robots-On the interplay ofreal brains and artificial devices, Edinburgh, Scotland 2002.
2. Li Y, Sun R, Wang Y, Li H, Zheng X. A novel robot system integrating biologi‑
cal and mechanical intelligence based on dissociated neural networkcontrolled closed-loop environment. PloS one. 2016; 11(11): e0165600.
3. Ijspeert AJ. Central pattern generators for locomotion control in animals
and robots: a review. Neural Networks. 2008; 21(4): 642-53.
4. Herrero-Carrón F, Rodríguez FB, Varona P. Bio-inspired design strategies
for central pattern generator control in modular robotics. Bioinspiration &Biomimetics. 2011; 6(1): 016006.
5. Buzsáki G, Watson BO. Brain rhythms and neural syntax: implications for effi‑
cient coding of cognitive content and neuropsychiatric disease. Dialogues
in Clinical Neuroscience. 2012; 14(4): 345.

P208
NEST 3.0, NESTML and NEST Desktop: new user experienceand classroom readinessMarkus ­Diesmann1, Jochen M E­ ppler1, Susanne K­ unkel2, Charl L­ inssen3,Håkon Mørk2, Abigail ­Morrison1, Hans Ekkehard ­Plesser2, Sebastian­Spreizer4, Stine Brekke ­Vennemo2
1
Forschungszentrum Jülich GmbH, Jülich, Germany; 2Norwegian
University of Life Sciences, Faculty of Science and Technology, Ås, Norway;
3
Forschungszentrum Jülich, Jülich, Germany; 4Forschungszentrum Jülich
GmbH; University of Freiburg, Freiburg, Germany
Correspondence: Charl Linssen (c.linssen@fz‑juelich.de)
BMC Neuroscience 2020, 21(Suppl 1):P208
Over the last year, major advances have taken place in NEST Simulator
and its associated tooling. This poster describes updates in NEST 3.0,
NESTML and NEST Desktop.
NEST 3.0 is the next major version update of NEST. With it, changes
are made not only to the user interface but also to the inner workings of NEST. In the PyNEST interface, new concepts are introduced
for the compact and efficient description of large populations ofneurons and synapses as well as distributions of parameter values.
The PyNEST Topology module is integrated into the standard PyNEST
package, so that creation and connection of spatial networks can now
be performed by calling the standard functions. NEST 3.0 improves theexpressiveness of model descriptions and the speed of network creation. A new and improved infrastructure for handling recordings has
been implemented, with built-in backends to record to memory, ASCII
files and screen.
NESTML is a domain-specific language for neurons and synapses. It
serves as a specification and exchange format, where dynamical systems are expressed in continuous time (e.g., using differential equations) and have the additional ability to receive and emit precisely
timed events (representing action potentials). Feature highlights
include a concise yet expressive syntax inspired by Python, directentry of dynamical equations, and imperative programming-stylespecification of event handling and generation.
NESTML comes with a powerful toolchain, written in Python, and isreleased under the GNU GPL v2.0. It parses a given model and performs code generation (“transpiling”). The generated code targets
a particular hardware and software platform (e.g. NEST running on
a high-performance computing cluster) with highly optimised andperformant code. The toolchain performs detailed analytical andPage 114 of 123

numerical analysis to yield optimal solver recommendations, and precise solutions where possible. Target platforms can be added flexibly
using Jinja2 templates. As a result, NEST users can now specify neuron and synapse models in the same way they specify the network
structure, using a domain-specific language that is independent of theunderlying C++ code.
NEST Desktop is a web-based graphical user interface which enables the rapid construction, parametrization, and instrumentation ofneuronal network models typically used in computational neuroscience. The client-server architecture supports installation-free accessto NEST. The primary objective is to provide an accessible classroom
tool that allows users to rapidly explore neuroscience concepts without the need to learn a simulator control language at the same time.
NEST Desktop opens NEST technology for a new user group, namely
students in the classroom, and contributes to equal opportunities in
education.
These advances, combined with work on the user-level documentation and deployment mechanisms, contribute to the creation andmaturation of the NEST ecosystem as a component of a software infrastructure for neuroscience.
Acknowledgements: This research has received partial funding fromthe Helmholtz Association through IVF no. SO-092 (Advanced Computing Architectures, ACA) and the European Union’s Horizon 2020
research and innovation programme under grant agreement No
720270 (HBP SGA1) and No 785907 (HBP SGA2), and the EU Horizon
2020 programme “DEEP-EST” (contract no. ICT-754304). Use of the
JURECA supercomputer through VSR grant JINB33.
P209
A predictive model of serotonergic fiber densities basedon reflected Fractional Brownian Motion
Skirmantas ­Janusonis1, Ralf M­ etzler2, Thomas ­Vojta3
1
University of California, Department of Psychological and Brain Sciences,
Santa Barbara, California, United States of America; 2University of Potsdam,
Institute of Physics and Astronomy, Potsdam‑Golm, Germany; 3Missouri
University of Science and Technology, Department of Physics, Rolla,
Missouri, United States of America
Correspondence: Skirmantas Janusonis (janusonis@ucsb.edu)
BMC Neuroscience 2020, 21(Suppl 1):P209
All vertebrate brains contain a dense matrix of thin axons (fibers)that release serotonin (5-hydroxytryptamine), a neurotransmitterthat modulates a wide range of neural, glial, and vascular processes.
Altered serotonergic fiber densities have been associated with a number of mental disorders and conditions, such as Autism Spectrum
Disorder, Major Depressive Disorder, and exposure to 3,4-methylenedioxymethamphetamine (MDMA, “Ecstasy”). Also, serotonergic fibers can regrow in adulthood and therefore can support the functional
recovery of the brain after injury. However, the processes that lead tothe self-organization and plasticity of this fiber system remain poorly
understood.
Our previous research has shown that the trajectories of serotonergic
fibers in terminal fields can be modeled as random walks [1,2]. We now
introduce a computational model that is based on Fractional Brownian
Motion (FBM), a continuous stochastic process that generalizes normal
Brownian Motion and allows correlations between non-overlapping
increments. The model capitalizes on the recently discovered properties of the reflected FBM (rFBM) in one-dimensional domains [3,4].
FBM is parametrized by the Hurst index (H) that allows subdiffusion (H< ½) and superdiffusion (H > ½). We show that in the superdiffusive
regime rFBM-walks recapitulate some key features of regional serotonergic fiber densities, on the whole-brain scale. Specifically, by using
supercomputing simulations of fibers as FBM-paths in two-dimensional brain-like domains, we demonstrate that the resultant steadystate distributions approximate the fiber distributions in mouse brain
sections immunostained for the serotonin transporter (a marker for
serotonergic fibers in the adult brain). These results do not sensitively
depend on the H-value (for H > ½), precise estimates of which are currently difficult to obtain experimentally.

﻿BMC Neurosci 2020, 21(Suppl 1):54

This novel framework can support predictive descriptions and manipulations of the serotonergic matrix and it can be further extended
to incorporate the detailed physical properties of the fibers andtheir environment. We also show that this neuroscience-motivatedapproach can stimulate theoretical investigations of rFBM in two- andthree-dimensional domains, with potential applications in other fields
of science.
Acknowledgements: This research is funded by the National Science
Foundation (grants #1822517 and #1921515 to SJ), the National Institute of Mental Health (grant #MH117488 to SJ), the California NanoSystems Institute (Challenge grants to SJ), the Research Corporationfor Science Advancement (a Cottrell SEED Award to TV), and the German Research Foundation (DFG grant #ME 1535/7-1 to RM), and theFoundation of Polish Science (an Alexander von Humboldt Polish Honorary Research Scholarship to RM).
References
1. Janušonis S, Detering N. A stochastic approach to serotonergic fibers inmental disorders. Biochimie. 2019; 161: 15-22.
2. Janušonis S, Mays KC, Hingorani MT. Serotonergic Axons as 3D-Walks. 2019.
3. Wada AH, Vojta T. Fractional Brownian motion with a reflecting wall. PhysicalReview E. 2018; 97(2): 020102.
4. Guggenberger T, Pagnini G, Vojta T, Metzler R. Fractional Brownian motion ina finite interval: correlations effect depletion or accretion zones of particles
near boundaries. New Journal of Physics. 2019; 21(2): 022002.

P210
Seizure pathways change on circadian and slower timescales
in individual patients with focal epilepsy
Yujiang Wang
Newcastle University, Newcastle upon Tyne, United Kingdom
Correspondence: Yujiang Wang (yujiang.wang@newcastle.ac.uk)
BMC Neuroscience 2020, 21(Suppl 1):P210
Personalised medicine requires that treatments adapt to not only
the patient, but changing factors within each individual. Althoughepilepsy is a dynamic disorder characterised by pathological fluctuations in brain state, surprisingly little is known about whether andhow seizures vary in the same patient. We quantitatively comparedwithin-patient seizure network evolutions using intracranial electroencephalographic (iEEG) recordings of over 500 seizures from 31 patientswith focal epilepsy (mean 16.5 seizures/patient). In all patients, wefound variability in seizure paths through the space of possible network dynamics. Seizures with similar pathways tended to occur closer
together in time, and a simple model suggested that seizure pathways change on circadian and/or slower timescales in the majorityof patients. These temporal relationships occurred independent of
whether the patient underwent antiepileptic medication reduction.
Our results suggest that various modulatory processes, operating atdifferent timescales, shape within-patient seizure evolutions, leading to variable seizure pathways that may require tailored treatment
approaches [1].
Reference
1. Schroeder GM, et al. Seizure pathways change on circadian and slower
timescales in individual patients with focal epilepsy. Proceedings of the
National Academy of Sciences. 2020; 117(20): 11048-58.

P211
Neural networks architectures that detect visual motion
like biological brains
Hamish ­Pratt1, Bernard ­Evans2, Thomas ­Rowntree3, Ian R­ eid3, Steven­Wiederman2
1
The University of Adelaide, School of Computer Science, Adelaide,
Australia; 2The University of Adelaide, Adelaide Medical School, Adelaide,
Australia; 3The University of Adelaide, Australian Institute of Machine
Learning, Adelaide, Australia
Correspondence: Hamish Pratt (hamishc.pratt@gmail.com)
BMC Neuroscience 2020, 21(Suppl 1):P211

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Convolutional neural networks (CNNs) have become the-state-of-theart for image classification and object detection tasks, as they havethe ability to combine appearance features in a scene. CNNs used for
detection and classification tasks primarily process single static images
to combine the features. In a manner similar to biological brains, someneural networks also utilise motion as complementary information toaid object detection tasks. However, unlike the brain, these networks
rarely classify ‘moving objects’ in a scene. Our research analyses a neural network’s ability to detect unique motion cues in scenes without
any appearance, to understand the limits for neural networks to process motion information. We generated variant CNN models to understand different architectures that can process motion information andbuilt a recurrent CNN with information skip layers for our experiments.
By comparing our network’s detection rates against psychophysicalstimuli used in human experiments, we found the neural network andhumans both struggled to correctly detect unique motion in similar
conditions. When trained for detecting higher orders of motion, stimuli observable by even small insects, the network responded strongly
to the order of motion for which it was trained against, and was, forthe majority, unresponsive to the other motion orders. To further test
the ability of motion detection in neural networks, we trained a neuralnetwork against detecting repeating spatio-temporal signals inside ascene of random noise. The results from our experiments show thatalongside convolutional neural networks’ success in detecting appearance features for object classification, they are able to detect motion
without appearance. With the understanding of similarities to biological brains and limitations in which these neural networks performfundamental vision tasks like motion detection, we will have a better
understanding of a network’s suitability for real-world applications.
P212
Neuron conduction delay plasticity for unsupervised learning
Joshua Arnold, Peter Stratton, Janet Wiles
University of Queensland, School of ITEE, Brisbane, Australia
Correspondence: Joshua Arnold (joshua.arnold1@uqconnect.edu.au)
BMC Neuroscience 2020, 21(Suppl 1):P212
Spiking neurons inherently represent time due to their momentary
discrete action potentials; as such, they are well poised to process spatiotemporal data. Despite their temporal nature, most computational
learning rules focus on modulating synaptic efficacy (weight), which
only indirectly influences a neuron’s temporal dynamics.
Weight-based rules are well suited to solving synchronous spatial
learning tasks, as demonstrated by the surge of interest in ratecoded neurons performing frame-based image classification using
backpropagation.
For temporal tasks, however, weight based learning rules often implicitly rely on the temporal dynamics of membrane equations or synaptictransfer functions to discriminate between spatially identical, but temporally distinct, inputs.
Allowing spiking neurons to perform some aspect of explicit temporallearning offers significant advantages for learning asynchronous spatiotemporal patterns compared to weight-based rules alone.
With improvements in imaging techniques, there is accumulating evidence for action-potential conduction velocity plasticity over long andshort timescales [1,2].
The biological mechanisms implementing Conduction Delay Plasticity(CDP) could include myelination, changes in axon diameter, changes
to nodes of Ranvier length, bouton movement, or likely some combination of these mechanisms and others not listed.
While the precise nature and interaction of the biological mechanisms
underlying CDP remain elusive, computational models provide aframework in which theories can be tested.
Several CDP learning rules have been suggested with greatly varying
levels of biological fidelity and computational efficiency; in particular,
we focus on one rule called Synaptic Delay Variance Learning [3].
Here we demonstrate the ability of a Leaky Integrate and Fire spiking
model using only CDP (no weight learning) to learn a repeating spatiotemporal pattern in a continuous time input stream with no training

﻿BMC Neurosci 2020, 21(Suppl 1):54

Page 116 of 123

signal; that is, the delays self-organise to represent the temporal structure of the input.
A neuron receives 2000 afferents firing with Poisson distributions of
10Hz, while the embedded pattern is presented with a Poisson distribution of 5Hz and consists of 500 afferents firing once within a 50ms
period.
The input is normalised such that the patterns cause no change inoverall activity during presentations and all afferents involved in thepattern are adjusted to maintain a 10Hz firing rate.
After 250 seconds of training, the neuron is tested for 50 seconds andsuccessfully responds to 99.7% of pattern presentations with 3.1%
false positives, averaged over 100 trials.
These results provide a demonstration of CDP as a functional computational learning rule enabling spiking neurons to perform unsupervised learning of spatiotemporal data.
References
1. Fields RD. A new mechanism of nervous system plasticity: activity-depend‑ent myelination. Nature Reviews Neuroscience. 2015; 16(12): 756-67.
2. Arancibia-Carcamo IL, Ford MC, Cossell L, Ishida K, Tohyama K, Attwell D.
Node of Ranvier length as a potential regulator of myelinated axon conduc‑tion speed. Elife. 2017; 6: e23329.
3. Wright PW, Wiles J. Learning transmission delays in spiking neural networks:
A novel approach to sequence learning based on spike delay variance. In
The 2012 International Joint Conference on Neural Networks. 2012 Jun 10(pp. 1-8). IEEE.

P213
Local synaptic connections alter spike responses and signal
propagation in models of globus pallidus pars externa
Erick Olivares, Matthew Higgs, Charles Wilson
Universidad de Texas at San Antonio, Department of Biology, San Antonio,
Texas, United States of America
Correspondence: Erick Olivares (erickolivaresb@gmail.com)
BMC Neuroscience 2020, 21(Suppl 1):P213
Globus pallidus pars externa (GPe) has been seen as a relay nucleus
in the indirect pathway of the basal ganglia, which simply inverts
the inhibitory signal arriving from striatum. In this view, the information flowing through GPe runs in parallel paths having no interaction
with one another. However, GPe neurons are fast autonomous oscillators that project axon collaterals spanning a wide area, creating anactive local inhibitory network. How does local connectivity affect GPe
steady-state firing and responses to stimuli? To answer that question,
we constructed network models of GPe using experimentally measured neuron firing rates and input-output properties and four different structures of local connectivity.
GPe neurons are intrinsic oscillators, so they can be simulated using
phase models, which allow us to predict the time of the next spike
considering all the synaptic inputs arriving during the inter-spikeinterval (ISI). We experimentally measured the firing rates, phase resetting curves (PRCs), and inter-spike membrane potential trajectories ofa sample of GPe neurons (n=19) in mouse brain slices. Using the dataset of PRCs and firing rates, we generated 1000 artificial neurons withthe diversity of the recorded neurons. The local connectivity in GPe
is known to be GABAergic, and we can measure the amplitudes andkinetics of the unitary synaptic conductances. In addition, the average probability of connection between any two neurons in GPe canbe estimated from anatomical studies. As the connectivity patterns
are unknown, we simulated networks with four qualitatively different
adjacency matrices (Fig. 1A): “regular”, “random”, “small world” and“hierarchical”. Each network was created using the same set of 1000
neurons and the same total number of connections (10000). At steadystate, the four networks produced similar distributions of firing rates
and coefficient of variation of ISI.

Fig. 1 A Simplified representation of the four networks used in the
study. Each architecture contains the same number of neurons andconnections. B Perturbation generated by a single IPSP applied to
one neuron propagates across the entire network. Figures show theabsolute difference in phase between the perturbed and unper‑
turbed simulations
In the phase model, the local inhibitory synaptic barrage alters thestationary density of phase from the uniform distribution of an unperturbed oscillator. In the networks simulated here, the neurons spent
more time at late phases, i.e. near spiking. This was more evident inregular and small-world networks compared to hierarchical and random networks. The skewed phase density altered the response to an
external excitatory input, as measured by the PSTH. The networks withthe greatest skew produced the largest response amplitude and thebriefest response time course.
Because of the GPe neurons’ autonomous firing and connectivity,
a local perturbation might propagate through the entire network(Fig. 1B). We investigated this by comparing the neurons’ phases inmatched simulations with and without perturbation by a single synaptic input. The disturbance propagated more often in the random network (69%) than in the regular network (61%). When the disturbance
propagated, it changed the phase of most neurons in the network, but
did not alter the overall firing rates, ISI distributions, or phase density.
The perturbations propagated faster and with shorter latencies in hierarchical and random networks, and slowest with longest latency in theregular network.
Our results show that in sparsely coupled networks of autonomous
oscillator neurons like GPe, the architecture of connections has little
effect on the steady-state firing rates, yet can determine the response
to external perturbation, and may allow widespread signal propagation within the network.
P214
Bayesian model for multisensory integration and segregation
Ma ­Xiangyu1, He ­Wang1, Min ­Yan1, Wenhao ­Zhang2, K Y Michael ­Wong1
1
Hong Kong University of Science and Technology, Institute for Physics,
Hong Kong; 2University of Pittsburgh, Institute for Physics, Pittsburgh,
Pennsylvania, United States of America
Correspondence: Ma Xiangyu (xmaal@connect.ust.hk)
BMC Neuroscience 2020, 21(Suppl 1):P214
The brain processes information from different sensory modalities
in our daily routine, and the neural system should have the ability to
distinguish whether different signals originate from the same source.
Experimental data suggested that the brain can integrate visual andvestibular cues to infer heading-direction according to Bayesianprediction. In the dorsal medial superior temporal (MSTd) area andthe ventral intraparietal (VIP) area, there exist two types of neurons,
congruent and opposite neurons. By focusing on a prior distribution of stimuli that is fully correlated, a recent work by Zhang et al.

﻿BMC Neurosci 2020, 21(Suppl 1):54[1] suggested that those two distinct types of neurons have complementary roles in multisensory integration and segregation. In the
proposed distributed network architecture, cues of different modalities are processed by different modules, but the modules are reciprocally connected. Congruent neurons of given preferred stimuli in one
module are connected to the congruent neurons in the other modulewith similar preferred stimuli. In contrast, opposite neurons of given
preferred stimuli in the two modules are connected to their counterparts with opposite preferred stimuli. This facilitates the congruent
neurons to yield Bayesian posterior estimates of multisensory integration in a broad range of parameters, and the opposite neurons to provide signals dependent on cue disparity, enabling the segregation ofcues in subsequent processing. However, in the previous model, there
are parameter ranges that the inference can only be approximately
Bayesian. Hence, in this work, we will approach the dynamics analytically and propose improvements for achieving more accurate Bayesian inference.
Furthermore, the Bayes-optimality in the previous work was based ona prior distribution of stimuli that is fully correlated, whereas in practice, there are many other scenarios described by priors with morethan one components. For example, studies in causal inference consider prior distributions with a correlated and an independent component. In the second part of our work, we propose a neural circuit withadditional modules to tackle these cases. In addition, we further illustrate that the network encodes strong evidence for the correlations
between the prior information and the network structure. Finally,
we discuss how the Bayes factor reveals the potential of our networkmodel as a decision making neural circuit for causal inference.
Reference
1. Zhang WH, et al. Complementary congruent and opposite neurons achieve
concurrent multisensory integration and segregation. Elife. 2019; 8: e43753.

P215
Loss aversion and outcome‑value encoding: a negative association
between posterior insula activity and loss aversion coefficient
Ka Chun ­Wu1, Isaac ­Ip2, Fiona C­ hing2, Heytou ­Chiu2, Rosa ­Chan3, Savio­Wong2
1
The Chinese University of Hong Kong, Department of Educational
Psychology, Hong Kong; 2The Chinese University of Hong Kong, Laboratory
for Brain and Education; Department of Educational Psychology, HongKong; 3City University of Hong Kong, Department of Electrical Engineering,Hong Kong
Correspondence: Ka Chun Wu (kachunwu7‑s@link.cuhk.edu.hk)
BMC Neuroscience 2020, 21(Suppl 1):P215
In prospect theory, loss aversion is one important parameter thatmodulates one’s decision in involving risk. Previous studies find that
amygdala activity is related to the degree of loss aversion during
the action-selection processes. In this study, we examine the brain
response associated with decision outcome and how that varies
across subjects with different degrees of loss aversion. We expect thatpeople with high loss aversion experience stronger emotional impact
when receiving a negative outcome after taking risk. We hypothesize
a person’s degree of loss aversion could be reflected by the BOLD contrast across decision outcomes.
To test this hypothesis, we recorded and analysed the fMRI data oftwenty-one participants (10 males and 11 females; M age = 17.9
± 0.75) during the Loss Aversion Task (LAT) [1]. The LAT was implemented with a rapid event-related design in which participants were
given two options: NoGamble option with a guaranteed outcome
and Gamble with 50% chance of getting a better-than-NoGamble
outcome and 50% chance of getting a worse-than-NoGamble outcome (Fig. 1A). The utility of the two options varied so that one option
has higher or equal utility respect to another. Participants were presented with a feedback indicating the outcome. Loss aversion coefficient (lambda; ƛ= −beta loss / beta gain) is estimated by fitting the
behavioural responses to the logistic function. A higher lambda value
indicates stronger loss aversion, with ƛ = 1 meaning equal weight forgain and loss.

Page 117 of 123

Fig. 1 A Design of the Loss Aversion Task (LAT). B Example of thethree feedback conditions: Win, Loss and No gamble. C Lambda isnegatively correlated with the activity of left posterior insular cortex
We find feedback-related activities at medial prefrontal cortex (mPFC)
but find no significant difference among the valence of feedback (gain,
loss, no-gain/loss). Condition contrasts reveal that the activity of theleft posterior insular cortex during gambling loss relative to guaranteed loss is negatively correlated with participants’ lambda (Fig. 1B). In
the other words, gambling loss elicit stronger insula response relative
to guaranteed loss in participants with lower lambda, while those withhigher lambda do not differentiate between gambling and guaranteed loss. The insular cortex potentially increases the sensitivity of the
anticipated loss, or alternatively, reduces the sensitivity to the gamble
loss [2]. Both interpretations lead to the likelihood a person choose to
take risk in a long run given both gambling loss and guaranteed loss
had similar subjective value in the past. In conclusion, the individual
difference in loss aversion could be capture by condition contrasts in a
LAT and gives insight to the model of outcome-value encoding.
References
1. Tom SM, Fox CR, Trepel C, Poldrack RA. The neural basis of loss aversion indecision-making under risk. Science. 2007; 315(5811): 515-518.
2. Canessa N, et al. Neural markers of loss aversion in resting-state brain activ‑
ity. NeuroImage. 2017;146: 257-265.

P216
Multiplicity and correlations of unidirectional and reciprocal
connections in the nervous system of the Caenorhabditis elegans
Edgar ­Wright1, Alexander G­ oltsev1,2
1
University of Aveiro, Department of Physics, Aveiro, Portugal; 2A.F. Ioffe
Physical‑Technical Institute, St. Petersburg, Russia
Correspondence: Alexander Goltsev (goltsev@ua.pt)
BMC Neuroscience 2020, 21(Suppl 1):P216
Reciprocally connected pairs (RCPs) of neurons are the simplest structural motif in neuronal networks. More complex structural motifs arecomposed of three or more neurons. RCPs are formed by reciprocal
synapses, and represent local microcircuits that can act as feedback
loops. Evidence of the ubiquitous presence of RCPs in the central nervous system of different animals is well-established. Statistical analysis
of connections between principal cortical cells has shown that RCPs
are overrepresented in the somatosensory cortex, neocortex, andolfactory bulb. RCPs are also overrepresented in the neuronal networkof the nematode Caenorhabditis elegans (C. elegans) [1].
In this work we analysed the statistics of reciprocal and undirectional chemical connections between pairs of neurons in the neuronal connectomes of the male and hermaphrodite C. elegans, using
data recently published in [2]. First, our analysis shows that even if
all unidirectional connections are removed, i.e. if approximately 63%
of all connections are removed, approximately 83% of neurons with﻿BMC Neurosci 2020, 21(Suppl 1):54

chemical synapses in the male (87%) in the hermaphrodite) remain in
the strongly connected cluster, where they are reachable from eachother through sequences of reciprocal connections. This result showsthat reciprocal connections provide communication between mostneurons with chemical synapses in the C. elegans. Second, average
multiplicity was found to be larger among reciprocal connections thanunidirectional connections, both among afferent and efferent connections. The probability that a connection has large multiplicity (over10 synapses per connection) is larger among reciprocal connections.
Third, it was found that most neurons with an above-average numberof presynaptic neighbors have a number of afferent synapses which is
on average larger than the average connectome multiplicity. Moreover, the larger the in-degree of a neuron the larger the multiplicity of
the afferent connections to this neuron (Fig. 1). The number of efferent connections, however, was found to be largely independent of thenumber of postsynaptic neurons. Fourth, the number of afferent synapses and the number of presynaptic neurons are strongly correlated,such that neurons with more presynaptic neighbors receive disproportionally more synapses.
Given the known functional roles of some RCPs, it is possible thatenhanced multiplicity among RCPs is the result of their function. For
example, RCPs have been implicated in memory formation. Since theformation of long-term memory results in an increase in the numberof dendritic spines on neurons that are part of a memory engram, it ispossible that a similar mechanism plays a role in the enhanced multiplicity of reciprocal connections in the C. elegans. The enhanced multiplicity may in part result from Hebbian structural plasticity. As neurons
with a larger number of presynaptic neighbors are more likely to be
activated, they are also more likely to experience prolonged periodsof high activity, which in turn can induce the formation of more synapses. Conversely, the multiplicity of neurons with less presynaptic
neighbors should decrease as the result of increased periods of low
neuronal activity.

Fig. 1 Average synaptic multiplicity win of afferent connections
to neurons with qin presynaptic neighbors, for the male C. elegans.
Above-average multiplicity (>3.61) is indicated by upward (purple)
triangles and below-average multiplicity is indicated by downward(orange) triangles. Vertical bars measure the standard deviation(spread about the average value). The vertical dotted line indicatesthe average number of presynaptic neighbors

Page 118 of 123

References
1. Varshney LR, Chen BL, Paniagua E, Hall DH, Chklovskii DB. Structural proper‑
ties of the Caenorhabditis elegans neuronal network. PLoS Computational
Biology. 2011; 7(2): e1001066.
2. Cook SJ, et al. Whole-animal connectomes of both Caenorhabditis elegans
sexes. Nature. 2019; 571(7763): 63-71.

P217
Autaptic connections can implement state transitions in spiking
neural networks for temporal pattern recognition
Yaqoob ­Muhammad1, Volker ­Steuber2, Borys Wróbel3
1
University of Hertfordshire, Computer Science, Hatfield, United Kingdom;
2
University of Hertfordshire, Biocomputation Research Group, Hatfield,
United Kingdom; 3Adam Mickiewicz University in Poznan, Evolving
Systems Laboratory, Poznan, Poland
Correspondence: Yaqoob Muhammad (m.yaqoob3@herts.ac.uk)
BMC Neuroscience 2020, 21(Suppl 1):P217
In biological neuronal networks, autaptic connection or autapses aresynaptic connections between the axon and dendrites of a single
neuron, which can be either excitatory (glutamatergic) or inhibitory(GABAergic). Since their first discovery four decades ago [1], the existence of autapses has now been documented in various brain regions
including neocortex, hippocampus and cerebellum [2]. However, thefunctional role of autapses is still unknown [3]. In this work, we show
the importance of autapses for temporal pattern recognition in simple
spiking neural networks. The computational task is to recognise a specific signal sequence in a stream of inputs so that a single output neuron spikes for the correct input signal, while remaining silent for other
input signals. Having understood the role of autapses and the resulting switching mechanism in networks evolved for recognising signalsof length two and three [4], we were able to define rules for constructing the topology of a network handcrafted for recognising a signal
sequence of length m with n interneurons. We show that autapses
are crucial for switching the network between states and observe thata minimal network recognising a signal of length m requires at least(m-1) autaptic connections. In contrast to solutions obtained by theevolutionary algorithm in [4] we show that the number of interneurons required to recognise a signal is equal to the length of the signal.
Finally, we demonstrate that a successful recogniser network (where
n is greater than or equal to three) must have three specialised neurons: a “lock”, “switch” and “accept” neuron, in addition to the otherstate maintaining neurons (N0, N1, … Nn-4), whose number depends
on length of the signal. All interneurons in the network require an
excitatory autaptic connection, apart from the “accept” neuron.The“lock” neuron is always active (thanks to an excitatory autapse), which
prevents the output from spiking except when the network receives
the second to last correct input signal and allows the output neuron
to spike in response to the correct last input. If the lock is released by
the second to last correct input signal, the “accept” neuron (i) produces spike/s in the output neuron when the network receives thelast correct input and (ii) sends a signal to the “switch” neuron, whichtransforms the network back into the start state. The “switch” neuron
is responsible for the transition between the network start state andother possible inter-signal network states. In the future, we intend to
explore other functional roles of autapses and higher-order loops inlarger neuronal networks.
References
1. Van Der Loos H, Glaser EM. Autapses in neocortex cerebri: synapses
between a pyramidal cell’s axon and its own dendrites. Brain Research.
1972; 48: 355-60.
2. Bacci A, Huguenard JR. Enhancement of spike-timing precision by autaptictransmission in neocortical inhibitory interneurons. Neuron. 2006; 49(1):119-30.
3. Wiles L, et al. Autaptic connections shift network excitability and bursting.
Scientific reports. 2017; 7: 44006.
4. Yaqoob M, Steuber V, Wróbel B. The Importance of Self-excitation in Spiking
Neural Networks Evolved to Recognize Temporal Patterns. In International
Conference on Artificial Neural Networks 2019 Sep 17 (pp. 758-771).
Springer, Cham.

﻿BMC Neurosci 2020, 21(Suppl 1):54

Page 119 of 123

P218
The geometry of spatio‑temporal odorant mixture encodingin the Drosophila mushroom body
Aurel A Lazar, Tingkai Liu, Chung‑Heng Yeh
Columbia University, Electrical Engineering, New York, United Statesof America
Correspondence: Tingkai Liu (tl2747@columbia.edu)
BMC Neuroscience 2020, 21(Suppl 1):P218
Biological organisms are constantly challenged with navigating odorant scenes comprised of complex time-varying mixtures of volatilecompounds. To characterize odorant mixture encoding and processing, two seemingly contradictory hypotheses have been considered:
Elemental and Configural. The Elemental scheme [1,2] encodes mixtures linearly with identifiable components, while the Configuralscheme [3] encodes mixtures as a holistic odor object distinct fromits components. Here, we advance a feedback normalization model ofthe Drosophila early olfactory system that reconciles the two encoding schemes, and analyze the geometry of the resulting odorantencoding space.
Our model consists of Projection Neurons (PNs), Kenyon Cells (KCs)and the Anterior Paired Lateral (APL) neuron. To quantify the degree
to which a mixture is encoded elementally vs configurally, we employ
the Cosine Similarity (CS) between the KC code of the odorant mixture
and its pure components. We show that, due to the global feedback
gain control exerted by the APL neuron and the KC spiking mechanism, the steady-state KC output is an input-invariant sparse combinatorial code with consistently 5-10% active neurons. This sparsecode results in a configural mixture code with low CS scores against
all pure components, enabling the association of different valences toan odorant mixture from its components. Preceding the steady-statephase, the circuit makes full use of gradient encoding in the first twolayers of the olfactory pathway [4] and the APL temporal dynamics
to encode each mixture elementally with about 25% active neurons.
This code exhibits a high (∼1) CS score with all pure components inthe mixture, indicating high linear decodability. Moreover, we demonstrate that the elemental encoding phase enables cognitive functions
for odorant processing. For example, combined with an attentiondriven modulation signal, elemental encoding overtakes configural
encoding in steady state and promotes odorant tracing for navigation.
Next, we investigate the geometry of the odorant and the KC spaces
and show that smooth interpolation between odorant input vectors
leads to sharp discontinuities in the KC representation space. Further
analysis reveals that the steady-state KC combinatorial codes arealmost binary, concentrating around the corners of a high dimensional cube in KC space.
This sparse grid-like structure gives rise to a distinctive clusteringof odorant mixture identities in the KC space, with high intra-cluster
similarity and inter-cluster dis-similarity. This geometric view of the KC
encoding space suggests that sharp transitions in the KC representation is the result of crossing cluster boundaries, which leads to large
jumps across vertices of the KC cube, and explains previous observations that small compositional changes of mixtures (mixture ratio orcomponent identities) can incur large differences in perception. Similarly, the transition from elemental to configural phases across timecorresponds to a trajectory in the KC space from a subspace spanned
by KC codes of odorant components to the vertices of the cube.

Fig. 1 A1 PN-KC-APL Architecture Overview. A2 Spatio-Temporal
Dynamics of Input/Output of PN-KC-APL Circuit. B1 Transient andSteady-state KC across binary odorant mixtures shows different
transition smoothness properties. B2 KC Combinatorial code occupy
corners of a hypercube. Mixing odorants causes KC response to jump
between vertices
Acknowledgments: The research reported here was supported,in part, by NSF grant #1544383 and in part by AFOSR grant#FA9550-16-1-0410.
References
1. Rokni D, Murthy VN. Analysis and Synthesis in Olfaction. ACS Chemical
Neuroscience. 2014 5(10):870–72.
2. Sehdev A, Szyszka P. Segregation of unknown odors from mixtures basedon stimulus onset asynchrony in honey bees. Frontiers in Behavioral Neuro‑science. 2019; 13: 155.
3. Thomas-Danguin T, et al. The perception of odor objects in everyday life: areview on the processing of odor mixtures. Frontiers in Psychology. 2014; 5:504.
4. Kim AJ, Lazar AA, Slutskiy YB. Projection neurons in Drosophila antennal
lobes signal the acceleration of odor concentrations. Elife. 2015; 4: e06651.

P219
Interpretable modeling of neurons in cortical area V4
via compressed convolutional neural networks
Reza Abbasi‑Asl1, Bin ­Yu2
1
University of California, Neurology, Bioengineering and Therapeutic
Sciences, San Francisco, California, United States of America; 2Universityof California, Statistics, Electrical Engineering and Computer Sciences, San
Francisco, California, United States of America
Correspondence: Reza Abbasi‑Asl (reza.abbasiasl@ucsf.edu)
BMC Neuroscience 2020, 21(Suppl 1):P219
Characterizing the functions of neurons in visual cortex is a central
problem in visual sensory processing. Along the ventral visual pathway, functions of the neurons in the cortical area V4 are less understood compared to early visual areas V1 and V2. This is primarily
because of V4 neurons’ highly nonlinear response properties. As aconsequence, building predictive models for these neurons has been
one of the challenging tasks in computational neuroscience. Recently,
models based on convolutional neural networks (CNNs) have shownpromise in predicting the activity of V4 neurons. More importantly,interpreting CNN-based models has offered tools to understand V4
neurons’ functional properties through visualizing their pattern selectivity. These interpretations, however, are based on models with hundreds of convolutional filters. Therefore, it is challenging to present
a sparse set of filter bases to model each V4 neuron. To address thislimitation, we propose two algorithms to remove redundant filtersin the CNN-based models of V4 neurons. First, CAR compression thatprunes filters from the CNN based on the filter’s contribution to theimage classification accuracy. CAR is a greedy compression scheme to﻿BMC Neurosci 2020, 21(Suppl 1):54

obtain smaller and more interpretable CNNs, while achieving close tooriginal accuracy. Second, RAR compression that prunes filters basedon their contribution to the neural response prediction accuracy. BothCAR and RAR provide a new set of simpler accurate models for V4 neurons. These models achieve almost similar (for CAR) or higher (for RAR)
accuracy compared to the original model. Using the compressed models, we are able to find a sparse set of filters that accurately model V4
neurons. We identify and visualize a total of 25 filters in AlexNet that
accurately model V4 neurons in non-human primates. The features
extracted by these 25 filters can predict the spike rates of 71 V4 neurons with an average correlation coefficient of 51%. By visualizing thepatterns selected by these models, we further demonstrate that V4
neurons are modeled via curvature or texture filters, as well as othermore complicated filters. Our results present one of the first efforts
to bridge between large-scale convolutional models of neurons andinterpretable sparse networks.
P220
Extracellular positive spikes in cat primary visual cortex may
correspond from the axons of cells originating from the thalamus­ effin2, Michael R ­Ibbotson1, Molis
Shi Hai ­Sun1, Ali ­Almasi1, Hamish M­Yunzab1
1
Australian College of Optometry, The National Vision Research Institute,
Melbourne, Australia; 2University of Melbourne, Biomedical Engineering,
Melbourne, Australia
Correspondence: Shi Hai Sun (sunsh@student.unimelb.edu.au)
BMC Neuroscience 2020, 21(Suppl 1):P220
Extracellular spike waveforms from recordings in the visual cortex
have been classified into either regular spiking (RS) or fast spiking (FS)units, which are commonly associated with excitatory and inhibitory
neurons, respectively. While both these types of spike waveforms arenegative-dominant, we show that there are also distinct classes withpositive-dominant waveforms, which are not regularly reported. The
spatial receptive fields (RFs) of these different spike waveform types
were estimated and we found that each spike type had distinctly different RF structure.
In this study, we systemically classified 1,225 single units (SUs) in cat
visual area 17 (V1) into five categories by the shape of their spike
waveforms: RS units (53%, n =645) which are biphasic, have a dominant negative peak, and a slow declining slope at the end of the waveform; FS units (18%, n = 226) which are biphasic, have a dominantnegative peak, and a fast declining slope at the end of the waveform;
triphasic spiking units (TS, 10%, n = 122) which have a positive firstpeak that is >10% of the negative peak, followed by a large negative
peak and then a smaller positive peak; compound spiking units (CS,7%, n = 82) which are also triphasic but with a significantly longer
waveform; and positive spiking units (PS, 12%, n = 150) which have apositive peak greater than the negative peak.
Of these 1,225 SUs, 341 had their spatial RFs estimated as the spatial filters in a powerful model-based analysis method to objectively
determine the RF characteristics of the recorded units, which revealed
the existence of non-oriented and blob-like (orientation bandwidth> 110°) and oriented and Gabor-like RFs (orientation bandwidth <90°). RS and FS units had mostly oriented RFs (94%, and 96%, respectively), TS and CS units have an even mixture of both RF types (47%oriented and 53% non-oriented, and 56% oriented and 44% non-oriented, respectively), while PS units had mostly non-oriented RFs (83%blob-like).
Units with non-oriented RFs have similar spatial structures to the centre-surround RFs reported in the thalamus, suggesting that units with
non-oriented RFs could have originated from the sub-cortical area. We
calculated several response properties that are statistically distinguishable between cortical and thalamic neural populations: spike-rate,
burstiness, and response latency. On average, PS units had significantly higher spike-rate (t-test, p < 0.01), significantly higher proportion of burst spikes (p < 0.001), and significantly shorter response
latency (p < 0.001) to RS and FS units. We also recorded from V1 before

Page 120 of 123

and after the application of muscimol (a cortical silencer) and foundthat PS units remained while RS and FS units did not.
Thus, our results suggest that PS units, which have mostly non-oriented RFs, thalamic-like response properties and remain after cortical silencing, are recordings of axons originating from the thalamus.
RS and FS units correspond to cortical neurons, which have mostly
orientated RFs and do not remain after cortical silencing. Our results
suggest that cortically implanted electrodes are able to record activity simultaneously from thalamic axon afferents and from the somas ofcortical neurons, thus allowing us to assess connectivity between two
brain areas while only recording from one area.
P221
Visualization of pathways of potential neurostructuresin neurorehabilitation period based on MRI data processing­ aleshin2
Margarita ­Zaleshina1, Alexander Z
1
Moscow Institute of Physics and Technology, Moscow, Russia; 2Instituteof Higher Nervous Activity and Neurophysiology, Moscow, Russia
Correspondence: Margarita Zaleshina (zaleshina@gmail.com)
BMC Neuroscience 2020, 21(Suppl 1):P221
Growth, formation and movement of biological structures are determined by characteristics of the environment and requirements for
obtaining external resources. Likewise, the topological organizationof the brain, consisting of a set of neurostructures, has a direct effecton the brain’s ability to perceive or process data. Additionally, localized damage to a small part of the brain will result in specific disturbances of isolated mental facilities, such as perception or movement[1]. Many researchers are currently studying regeneration and formation of a new spatial filling of tissue at the sites of damage. The individual variability of the anatomy and connectivity of the brain affects
the formation of its structure [2]. Studies of both tissue features andthe distribution and orientation of individual components are widelyused to visualize the microstructures of individual brain regions or todetermine the locations of biomarkers [3]. At the same time, it can beshown that neurorehabilitation depends not only on the characteristics of the whole brain, but also on the particular features of the distinct area where growth and recovery occur directly.
In this work, we study cases of regeneration of cortical neurostructures, when the damaged area is filled with new elements for a long
period of time. The analysis compares the calculated growth directions of neurostructures, the calculated trajectories of their growth,
taking into account the existing environment, and the real growth
paths identified on the basis of MRI data.
Our study takes into account that the ways of formation of neural
structures during neurorehabilitation have two main characteristics
that differ in scale and in details. The first characteristic is the average
direction of the formation of new neurostructures. Such a direction, as
a whole, is caused by an increase in the “favorableness” of the environment in which growth occurs. The second characteristic is a detailed
following of external elements in the existing biological environment,that is, on the one hand, rounding obstacles, and on the other hand,the use of convenient “corridors” for growth and advancement (Fig. 1).
Data packages (fMRI) are collected from Human Connectome Project(https://www.humanconnectome.org/data/). These fMRI could be
converted to diffusion-weighted images (dMRI), which are used for
tractography analysis and for investigate the heterogeneity of microstructural features.
The study uses spatial data analysis, which calculates the main corridors and growth directions, taking into account the available cortical volume filling. Data at the boundaries of tissue are excluded fromanalysis to minimize the impact of partial volume averaging with surrounding tissues.

﻿BMC Neurosci 2020, 21(Suppl 1):54

Fig. 1 Formation of growth paths depending on the density of
obstacles
References
1. Eickhoff SB, Constable RT, Yeo BT. Topographic organization of the cerebral
cortex and brain cartography. Neuroimage. 2018; 170: 332-47.
2. Maier-Hein L, et al. Why rankings of biomedical image analysis competitions
should be interpreted with care. Nature Communications. 2018; 9(1): 1-3.
3. Fick RH, Wassermann D, Deriche R. The dmipy toolbox: Diffusion mri multicompartment modeling and microstructure recovery made easy. Frontiersin Neuroinformatics. 2019; 13: 64.

Page 121 of 123

vertices (~32,000 nodes per hemisphere) as well as regions comprising an established atlas (180 regions per hemisphere), yielding 12 total
measures.
For each measure, a similarity metric for all scan pairs was computed.
This similarity information was used to quantify the extent of identifiable information captured by neuroimaging measures. We computed
the effect size difference in intra- and inter-subject similarity distributions as an identifiability metric. Hence, higher effect size differencestranslated to higher precision in identification. To capture the extent
of behavioral associations of every measure, independent component
analysis was used to decompose 109 behavioral measures sourced
from HCP to five core continuous dimensions, characterizing cognitive
performance, illicit substance use, tobacco use, personality-emotion
traits, and mental health. Variance component modeling was used
to evaluate the extent to which each neuroimaging measure could
explain individual variation in each behavioral dimension.
Results: Comparing the correlates of neural identity and behavior inatlas-based models with the ultra-high-resolution alternative revealed
the extent of information gain achieved by the increase in spatial resolution (Fig. 1). Our findings show behavioral associations of all neuroimaging modalities significantly increased as a result of the increasein spatial resolution. In particular, behavior associations of structuralconnectivity, functional connectivity, and cortical thickness benefitted the most from ultra-high-resolution analyses. The neural correlates
of individual identity were also better detected in higher resolution,
especially for structural connectivity and all measures of morphology(cortical curvature, thickness, and sulcal depth). The identification
improvements of functional connectivity and myelination were minimal. We proposed a novel model of high-resolution structural brain
networks that surpasses the ability of atlas-based alternatives in both
identification and behavior explanation of individuals.

P222
High‑resolution connectivity analyses
Sina M ­Lakouraj1, Vanessa ­Cropley2, Ye ­Tian3, Andrew Z­ alesky1,3
1
The University of Melbourne, Department of Biomedical Engineering,
Melbourne, Australia; 2Melbourne Neuropsychiatry Centre, Melbourne,
Australia; 3The University of Melbourne, Department of Psychiatry,
Melbourne, Australia
Correspondence: Sina M Lakouraj (sina.mansour.lakouraj@gmail.com)
BMC Neuroscience 2020, 21(Suppl 1):P222
Introduction: No two brains are alike. Neuroimaging can be used toelucidate the neural basis of human identity and to map neural correlates of human behavior and cognition. While most neuroimaging
features are commonly studied in the highest resolution provided by
the scanner, brain connectivity studies are traditionally conducted ina lower resolution of a predefined atlas. The aim of this study was tocontrast the identification (fingerprinting) and behavior prediction
capabilities of the atlas-based brain networks with a novel ultra-highresolution model of brain networks. This comparison will explore thepotential gains of conducting brain connectivity analyses in a higher
resolution.
Methods: We analyzed neuroimaging and behavioral data acquired
from 1000 individuals participating in the Human Connectome Project (HCP). These individuals formed a “test” group. A repeated MRI
scan on a different day from 42 of these individuals formed a “retest”
group. Structural connectivity was mapped using probabilistic tractography of Diffusion MRI. Functional connectivity was mapped using
resting-state functional MRI. Additionally, surface maps for corticalmeasures of thickness, curvature, sulcal depth, and myelination weresourced from the HCP. Hence, each scan was associated with 6 different neuroimaging characteristics of structure, morphology, andconnectivity, each of which was mapped at the higher resolution ofFig. 1 Identification effect size (top left) and overall behavior associa‑
tion (top right) captured at the resolution of a well-established corti‑
cal atlas (violet), and a high-resolution alternative (pink). Associations
are decomposed to data-driven components of behavior (bottomrow). The dashed lines present the 95% confidence interval. Asterisks
quantify effect sizes (***: large, **: medium, *: small)

P223
Less is more: wiring‑economical modular networks support
self‑sustained firing‑economical neural avalanches for efficient
processing
Shengjun ­Wang1, Junhao L­ iang2, Changsong ­Zhou3
1
Shananxi Normal University, Physics, Xi’An, China; 2Hong Kong Baptist
University, Hong Kong, China; 3Hong Kong Baptist University, Physics,
Hong Kong, China
Correspondence: Changsong Zhou (cszhou@hkbu.edu.hk)
BMC Neuroscience 2020, 21(Suppl 1):P223

﻿BMC Neurosci 2020, 21(Suppl 1):54

Complex neural network in the brain is remarkably cost-efficient while
the basic mechanisms underlying its structure-dynamics economy are
not clear. Here we study the intricate interplay between wiring andrunning cost with modular network topology, self-sustained activity and critical avalanche dynamical mode in biologically plausible
excitation-inhibition balanced spatial neuronal network. When rewiring the initially wiring-expensive sparse random network gradually
to wiring-economical modular network, its self-sustained dynamics
changes from asynchronous spiking to critical avalanches state withstrongly reduced firing rate and greatly enhanced response sensitivity
to transient stimuli. Thus, the system can counter intuitively achievemuch more functional values with much less costs in both wiring andfiring. The dynamic mechanism is explained as a proximity to Hopf
bifurcation in the macroscopic mean-field in separated modules
when increasing the connection density. Our work reveals the genericmechanism underlying the cost-economical structural organizationand function-efficient critical dynamics of neural systems, providing
insights to brain-inspired efficient computational designs.

Page 122 of 123

P225
Functional identification of the odorant transduction processof Drosophila olfactory sensory neurons
Aurel A Lazar, Tingkai Liu, Yiyin Zhou
Columbia University, Electrical Engineering, New York, United Statesof America
Correspondence: Tingkai Liu (tl2747@columbia.edu)
BMC Neuroscience 2020, 21(Suppl 1):P225
A recent empirical model of the olfactory sensory neurons (OSNs)
described on the molecular level the mechanics of the Odorant
Transduction Process (OTP, Fig. 1A, top) [1]. A system of nonlinear
differential equations modeling the OTP in cascade with a biologicalspike generator successfully captured the experimentally observed
responses of OSNs.

P224
Hopf bifurcation in mean field explains critical avalanches
in excitation‑inhibition balanced neuronal networks: a mechanism
for multiscale variability
Junhao ­Liang1, Tianshou ­Zhou2, Changsong Z­ hou3
1
Hong Kong Baptist University, Hong Kong, China; 2Sun Yat‑sen University,
School of Mathematics, Guang Zhou, China; 3Hong Kong Baptist University,
Physics, Hong Kong, China
Correspondence: Changsong Zhou (cszhou@hkbu.edu.hk)
BMC Neuroscience 2020, 21(Suppl 1):P224
Cortical neural circuits display highly irregular spiking in individual
neurons but variably sized collective firing, oscillations and critical avalanches at the population level, all of which have functional
importance for information processing. Theoretically, the balanceof excitation and inhibition inputs is thought to account for spiking
irregularity and critical avalanches may originate from an underlying
phase transition. However, the theoretical reconciliation of these multilevel dynamic aspects remains an open question. Herein, we showthat excitation-inhibition (E-I) balanced network with synaptic kinetics
can maintain irregular spiking dynamics with different levels of synchrony and critical avalanches emerge near the synchronous transition point. The mechanism is unveiled by a novel mean-field theory
that derives the field equations governing the network macroscopic
dynamics. It reveals that the E-I balanced state of the network manifesting irregular individual spiking is characterized by a macroscopic
stable state, which can be either a fixed point or a periodic motion andthe transition is predicted by a Hopf bifurcation in the macroscopic
field. Furthermore, these multiscale variable behaviours can be jointly
observed in the spontaneous activities of mouse cortical slice in vitro,
indicating universality of the theoretical prediction. Our theory unveils
the mechanism that permits complex neural activities in different spatiotemporal scales to coexist and elucidates a possible origin of thecriticality of neural systems. It also provides a theoretical framework
for analyzing the macroscopic dynamics of E-I balanced networks andits relationship to the microscopic counterparts, which can be usefulfor large-scale modeling and computation of cortical dynamics.

Fig. 1 A Model architectures of OTP, CIM and DNP. B1 Instantiationsof the identification circuits and their corresponding degree of free‑dom. B2 Comparison between output of OTP (in black) and identified
models. Peak-SNRs between original and identified outputs areshown to the right. B2 inset Zoomed in view of transient responses
Here we functionally identify the OTP model on the algorithmic levelwith two state-of-the-art system identification methods (i) Channel
Identification Machines (CIMs) [2] (Fig. 1A, middle) and (ii) Divisive Normalization Processors (DNPs) [3, 4] (Fig. 1A, bottom). We examined 5
model structures with different degrees of freedom under these twomodel architectures (Fig. 1B1).
Overall, the full temporal DNP successfully captured the OTP dynamics with the highest average and peak signal-to-noise ratios (ASNR& PSNR) of 36.6 dB and 41.5 dB respectively when predicting the
response to a novel stimulus (Fig. 1B2). This is 6 dB higher than theidentified CIM model of comparable model complexity (rank 2), and4 dB higher than the full-rank CIM. While the linear filter alone identified by the CIM has a PSNR of 29.2 dB that is comparable to the prediction given by the DNP with only FF-D processor, a closer examination
revealed that it did not predict very well the transient responses at theonset of the stimulus that is critical in the context of olfactory encoding. The highly nonlinear transient response is nonetheless well captured by the full temporal DNP (Fig. 1B2, inset).
Furthermore, we observed that the FF-N and FF-D processors identified in the full temporal DNP consistently resemble each other in theirfunctional forms, with FF-N generally having higher 3 dB Bandwidththan FF-D (data not shown). This prompted us to closely examine themechanism that gives rise to the OTP’s 2D encoding property where
the output of OTP model captures both the odorant concentrationand concentration gradient. We instantiated a DNP with FF-N and
FF-D processors modeled as linear lowpass filters with different bandwidths. Surprisingly, the simple model is able to capture the essential 2D encoding across all stimuli described in [1] (data not shown),

﻿BMC Neurosci 2020, 21(Suppl 1):54

suggesting a general approach that enables simultaneous encodingof input signal’s amplitude and gradient with divisive normalization.
Concluding, by evaluating two functional identification methods, weestablished a functional description of the empirical OTP model using
divisive normalization processors. In addition, the identified DNP provided insights on the form of divisive normalization that leads to thesimultaneous encoding of both the concentration and concentration
gradient. Divisive processing has previously been used as a key component in describing the functional dynamics of blowfly photoreceptors [5], suggesting that DNPs may be universally employed for theidentification of nonlinear processing in the early sensory systems.

Page 123 of 123

3. Lazar AA, Ukani NH, Zhou Y. Sparse identification of contrast gain control
in the fruit fly photoreceptor and amacrine cell layer. The Journal of Math‑ematical Neuroscience. 2020; 10(1): 1-35.
4. Brittain C, et al. Severity dependent distribution of impairments in PSP and
CBS: Interactive visualizations. Parkinsonism & Related Disorders. 2019; 60:
138-45.
5. Van Hateren JH, Snippe HP. Information theoretical evaluation of parametricmodels of gain control in blowfly photoreceptor cells. Vision Research.
2001; 41(14): 1851-65.

Acknowledgments: The research reported here was supported by
AFOSR under grant #FA9550-16-1-0410 and by DARPA under contract#HR0011-19-9-0035.
References
1. Lazar AA, Yeh CH. A molecular odorant transduction model and thecomplexity of spatio-temporal encoding in the Drosophila antenna. PLoS
Computational Biology. 2020; 16(4): e1007751.
2. Lazar AA, Slutskiy YB. Spiking neural circuits with dendritic stimulus proces‑sors. Journal of Computational Neuroscience. 2015; 38(1): 1-24.

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