<figure><img src="image_85.png" /></figure>

properties of fan-out/fan-in structures that are ubiquitous in the brain. We developed a model to reproduce experimental data and analyze the optimal reaction times of the network model, finding a balance between fast information transmission and high accuracy in pattern recognition. Our contribution improves understanding of the role of divergent convergent feedforward networks on the stability of fast and accurate decision-making.

## References

1. Barlow HB. Single units and sensation: a neuron doctrine for perceptual psychology? Perception. 2009;38:371-94.

2. Shepherd GM. The synaptic organization of the brain. Oxford: Oxford Press; 2003.

3. Mosqueiro TS, Huerta R. Computational models to understand decision making and pattern recognition in the insect brain. Curr Opin Insect Sci. 2014;6:80-5.

4. Strube-Bloss MF, Herrera-Valdez MA, Smith BH. Ensemble response in mushroom body output neurons of the honey bee outpaces spatiotemporal odor processing two synapses earlier in the antennal lobe. PLoS One. 2012;7:e50322.

## P146

### Swinging networks

Michal Hadrava<fnref n="1" />,<fnref n="2" />,³, Jaroslav Hlinka<fnref n="2" />,³

Nature is a powerful illusionist who, unfortunately for life sciences, hates revealing her secrets. One of her most rewarding tricks involves interconnecting a bunch of non-oscillatory neurons in such a way that they collectively behave like an oscillator [1]. Contemporary neuroscience strives to decipher this magic and does so not out of mere curiosity: the trick can go wrong, causing myriads of neurons to march to the deadly rhythm of epileptic seizure. It is of vital importance to determine which connectivity patterns promote and suppress epileptiform activity if surgery is to be effective when nothing else is [2]. On a lighter note, oscillatory dynamics explains many aspects of musical experience [3-5]. A question comes up again and again in ethnomusicological discourse as to whether these aspects are learned or not. In the context of oscillatory dynamics on networks, we might ask whether a single connectivity leads to the emergence of dynamics relevant to each musical culture or a different (learned) connectivity is at work each time. In conclusion, establishing a link between connectivity and oscillatory dynamics on networks seems to be an important problem with repercussions in such diverse fields as epileptology and ethnomusicology.

The mainstream approach to the problem can be characterized as follows: first, choose a dynamical model of single unit—e.g. neuron, synapse, or population thereof. Next, connect units of the selected type(s) in a network. Finally, study the effect of connectivity parameters on the global dynamics analytically, computationally, or using a combination of both. The major drawback of any analysis performed in this way is that the validity of its results is put in doubt whenever that of the single unit model is. Needless to say, none of the ever-growing variety of models has gained a wide acceptance yet. The mainstream approach could be dubbed the “object-oriented” one. The alternative approach, advocated by category theorists and adopted by us, could be called the “relational” one: instead of analysing a particular dynamical system, one investigates a whole class of dynamical systems on a particular manifold characterized only by its relations to classes of dynamical systems on different manifolds. This latter approach is epitomized by a recently introduced algebraic structure [6] which relates global network dynamics to its connectivity. We are currently trying to prove the existence of global periodic solutions in selected classes of simple networks with a given structure using this new theory.

**Acknowledgements:** This work was supported by the Grant Agency of the Czech Technical University in Prague, grant No. SGS14/192/OHK3/3T/13, the Czech Science Foundation Project No. P303-14-02634S, and the Czech Health Research Council Project No. NV15-29835A.

## References

1. Buzsáki G. Rhythms of the brain. New York: Oxford University Press; 2006.

2. Dixit AB, Banerjee J, Tripathi M, Chandra PS. Presurgical epileptogenic network analysis: a way to enhance epilepsy surgery outcome. Neurol India. 2015;63(5):743-50.

3. Cartwright JHE, Gonzalez DL, Piro O. Pitch perception: a dynamical-systems perspective. Proc Natl Acad Sci USA. 2001;98(9):4855-59.

4. Large EW: A dynamical systems approach to musical tonality. In: Huys R, Jirsa VK, editors. Studies in computational intelligence: nonlinear dynamics in human behavior, vol 328. Berlin: Springer; 2011. p. 193-211.

5. Large EW, Snyder JS. Pulse and meter as neural resonance. In: DallaBella S, Kraus N, Overy K, Pantev C, Snyder JS, Tervaniemi M, Tillmann B, Schlaug G, editors. Neurosciences and music III: disorders and plasticity, vol 1169; 2009. p. 46-57.

6. Lerman E, Spivak DI. An algebra of open continuous time dynamical systems and networks. arXiv:1602.01017v1 [math.DS].

<footnote n="1">¹Department of Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, 166 27, Czech Republic;</footnote>

<footnote n="2">²Department of Nonlinear Dynamics and Complex Systems, Institute of Computer Science, The Czech Academy of Sciences, Prague, 182 07, Czech Republic; ³National Institute of Mental Health, Klecany, 250 67, Czech Republic</footnote>

<footnote>Correspondence: Michal Hadrava - hadrava@cs.cas.cz</footnote>

<footnote>BMC Neuroscience 2016, 17(Suppl 1):P146</footnote>
P147

**Inferring dynamically relevant motifs from oscillatory stimuli: challenges, pitfalls, and solutions**

Hannah Bos¹, Moritz Helias¹,²

¹Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA BRAIN Institute I, Jülich Research Centre, 52425 Jülich, Germany; ²Department of Physics, Faculty 1, RWTH Aachen University, 52074 Aachen, Germany

**Correspondence:** Hannah Bos - h.bos@fz-juelich.de

BMC Neuroscience 2016, 17(Suppl 1):P147

Applications of oscillatory stimuli in optogenetical studies have been used to gather evidence that γ oscillations are generated by the interaction of inter-neurons (also termed the inter-neuron γ or ING mechanism) [1, 2]. We elaborate the pitfalls of inferring the origin of the oscillation from absolute (response spectra) as well as relative (power ratios) changes in spectra of neural activity induced by oscillatory input. We consider minimalistic models that isolate the difficulties and limitations arising in the interpretation of response spectra. The described effects generalize to more realistic models. This is demonstrated in simulations of a multi-laminar model of V1 composed of leaky-integrate-and-fire (LIF) model neurons [3], where the ground truth regarding the sub-circuits generating the oscillations is known [4]. In this structured model these effects combine and yield misleading results. By extending mean-field theoretical descriptions of population dynamics [5] by oscillatory input, we can close the loop to the condensed models.

We identify three main complications: First, the input can modify the excitability of the population in a linear or non-linear fashion, yielding significantly different changes in the spectra. Second, depending on the properties of the system, the input to the populations is potentially low pass filtered before it enters the system. Since this low pass filter is reflected in the response spectra, without revealing information regarding the internal dynamics of the network, we propose a stimulation protocol counteracting this effect by emphasizing high frequencies. Third, in general, the stimulation of a single population excites a mixture of dynamical modes. One frequency is generated by one dynamical mode that can be mapped to its anatomical origin [4]. Since the observable response is composed of an inseparable mixture of modes, the mode generating the oscillation cannot easily be isolated. Hence reconstructing the underlying connectivity as well as identifying the role of the stimulated population in the generation of the rhythm is not straightforward. Instead, the stimulus vector needs to reflect the structure of the circuit generating the oscillation in order to allow insights into the dynamically relevant components of the system.

These problems can be regarded as a sub-set of challenges that need to be faced when interpreting the results of circuits composed of more complex units. The proposed solutions may be used to construct new experimental stimulation protocols.

**Acknowledgements:** We acknowledge funding by the Helmholtz Association: portfolio theme SMHB and Young Investigator's Group VH-NG-1028, and 604102 (Human Brain Project). All network simulations were carried out with NEST (http://www.nest-initiative.org).

**References**

1. Cardin JA, Carlé M, Meletis K, Knoblich U, Zhang F, Deisseroth K, Tsai L-H, Moore CI. Driving fast-spiking cells induces gamma rhythm and controls sensory responses. Nature. 2009;459:663-7.

2. Buzsáki G, Wang XJ. Mechanisms of gamma oscillations. Annu Rev Neurosci. 2012;35:203-25.

3. Potjans TC, Diesmann M. The cell-type specific cortical microcircuit: relating structure and activity in a full-scale spiking network model. Cereb Cortex. 2014;24:785-806.

4. Bos H, Diesmann M, Helias M. Identifying anatomical origins of coexisting oscillations in the cortical microcircuit. 2015, arXiv preprint arXiv:1510.00642.

5. Brunel N. Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons. J Comput Neurosci. 2000;8:183-208.

P148

**Spatiotemporal mapping of brain network dynamics during cognitive tasks using magnetoencephalography and deep learning**

Charles M. Welzig¹, Zachary J. Harper¹,²

¹Departments of Neurology and Physiology, Medical College of Wisconsin, Milwaukee, WI 53226, USA; ²College of Engineering and Applied Science, University of Wisconsin-Milwaukee, Milwaukee, WI 53211, USA

**Correspondence:** Charles M. Welzig - welzig@mcw.edu

BMC Neuroscience 2016, 17(Suppl 1):P148

Magnetoencephalography (MEG) offers the high spatiotemporal resolution necessary to capture dynamic mesoscale cortical activation features [1] for spatiotemporal mapping of brain networks. In order to adapt such data for effective pathological or cognitive state classifiers, novel techniques are required to extract complex connectivity dynamics that vary in duration and latency. We have developed an advanced deep-learning system to explore such network dynamics through parcellated connectomes in individual subjects. The machine learning system produces transparent classifiers that can define spatiotemporal characteristics of state-specific connectivity, model neurophysiological pathology and expand understanding of connectivity dynamics. Our implementation uses source localized activation patterns extracted from event related epochs to classify cognitive states corresponding to working memory tasks. Subject-wise MEG data is mapped to segmented morphology from magnetic resonance imaging for source localization, then preprocessed to optimize neural network performance. The following steps minimize dimensionality and accommodate the deep-learning system's input requirements. First, spatiotemporal data are encoded using a wavelet transformation that extracts oscillatory data in the theta, alpha and beta/gamma bands per parcel (Fig. 78A). Next, synchronicity between parcels is calculated to populate 2D connectivity matrices respective to the frequency bands. These matrices are normalized and combined into frame images where theta, alpha and beta/gamma synchronicity is encoded as blue, green and red intensity values respectively. These pixel grids are smoothed and expanded using grid-ded, cubic interpolation (Fig. 78B). The deep learning system consists of a recursive neural network utilizing a long-short term memory (LSTM) architecture [2] that preserves temporal input characteristics (Fig. 78C). LSTM presents dynamically changing oscillatory patterns to the deep learning system by integrating features of a specified range of contiguous frames relative to each training frame. As this classification system allows for visualization of activation at each layer, we are able to identify specific patterns that mediate the classification process (Fig. 78D) [3]. Our classification methods have demonstrated significantly low error rate of 0.236 ± 0.425 (mean ± SD) in binary working memory state classification after 3 min of GPU-accelerated training. Additionally, weight patterns at specific layers within the deep learning network

<figure><img src="image_86.png" /><figcaption>**Fig. 78** Visualisations of processing and deep learning stages. A Wavelet decomposition across bands of interest. B Progression of image-encoded oscillatory synchronization in BA10. C Deep learning network improvement across training epochs. D One trained network layer displaying parcel dynamics that mediate classification</figcaption></figure>
highlighted relevant parcel interactions with significant effect on func-
tional connectivity dynamics within classified cognitive states. This
project represents an advancement in preserving critical spatiotempo-
ral information required to classify complex cognitive states that char-
acterize dynamically changing oscillatory and synchronous functional
activity patterns across the connectome.

**References**

1. Larson-Prior LJ, Oostenveld R, Della Penna S, Michalareas G, Prior F, Babajani-Feremi A, Schoffelen JM et al. Adding dynamics to the human connectome project with MEG. Neurolmage. 2013;80:190-201.

2. Donahue J, Hendricks LA, Guadarrama S, Rohrbach M, Venugopalan S, Saenko K, Darrell T. Long-term recurrent convolutional networks for visual recognition and description; 2014. arXiv:14114389.

3. Plis SM, Hjelm D, Salakhutdinov R, Allen EA, Bockholt HJ, Long JD, John-
son HJ, Paulsen J, Turner JA, Calhoun VD. Deep learning for neuroimaging:
a validation study. Front Neurosci. 2014;8.

P149

Multiscale complexity analysis for the segmentation of MRI
images

Won Sup Kim¹, In-Seob Shin¹, Hyeon-Man Baek², Seung Kee Han¹
¹Department of Physics, Chungbuk National University, Cheongju,
Chungbuk 28644, Republic of Korea; ²Korea Basic Science Institute,
Cheongju, Chungbuk 28119, Republic of Korea

**Correspondence:** Seung Kee Han - skhan@chungbuk.ac.kr

BMC Neuroscience 2016, 17(Suppl 1):P149

Segmentation of human brain images into regions with homoge-
neous intensity or texture is very crucial for the diagnosis of various
brain diseases. However, the presence of noises or artifacts remains
as one of the biggest obstacles for the successful segmentation. Here
we propose a novel method of segmentation based on the multiscale
complexity analysis. The idea is to characterize the complexity in visual
images by the multiscale profile representing the scale dependence
of compositional complexity. Our claim is that the multiscale profile
of human brain images combining scale dependent information on
intensity and texture information could be effectively utilized for the
segmentation of human brain images.

We have applied the multiscale complexity analysis for the segmen-
tation of two dimensional MRI images. Our method consists of three
steps. (I) An MRI image is partitioned into homogeneous regions uti-
lizing the information bottleneck method. (II) Multiscale complexity
profiles of individual pixels are computed from the partitioned image
of the MRI. (III) Feature vectors combining both intensity and texture
information are extracted for the segmentation. For the segmentation,
the feature vectors of individual pixels are clustered using a simple
K-mean clustering algorithm.

Using the simulated MRI images provided the BrainWeb database [1], the performance of the segmentation was tested. The performance shown in Fig. 79 indicates that the multiscale complexity analysis is very robust against noise. Details will be presented during the meeting.

**Reference**

1. Cocosco C, Kollokian V, Kwan RS, Evan A. BrainWeb: on line interface to a 3D MRI simulated brain database. Neurolmage. 1997;5:S425.

<figure><img src="image_87.png" /><figcaption>**Fig. 79 A** An MRI image from the BrainWeb [1] and the result of seg-
mentation into five clusters. **B** An MRI image with 7 % noise added
and the result of segmentation into five clusters</figcaption></figure>

P150

A neuro-computational model of emotional attention

René Richter¹, Julien Vitay¹, Frederick Beuth¹, Fred H. Hamker¹,²

¹Department of Computer Science, Chemnitz University of Technology, Chemnitz, Germany; ²Bernstein Center for Computational Neuroscience, Charité University Medicine, Berlin, Germany

**Correspondence:** René Richter - rene.richter@cs.tu-chemnitz.de

BMC Neuroscience 2016, 17(Suppl 1):P150

Emotional stimuli attract attention so the brain can focus its process-
ing resources on them. The questions that arise is how these stimuli
acquire their emotional value and how they can influence attentional
processes. Evidence suggests that this association might be learned
through conditioning in the amygdala, more specifically the basal
lateral amygdala (BLA). Furthermore, feedback connections from the
BLA to the visual cortex seem to enhance the activation of neural rep-
resentations which is a possible top-down attention mechanism of
the emergent attention hypothesis. While neuro-computational mod-
els of attention mechanisms attract increasing interest due to their
importance for the focused processing of information in the brain, the
possible emotional feedback from the amygdala is to date largely unex-
plored. Therefore, we propose a rate-coded, biological realistic neuro-
computational model constructed of 3 smaller functional models. First,
we combined a model of the visual processing pathway for object rec-
ognition [1] that includes the retina, the lateral geniculate nucleus, the
visual areas V1, V2 and V4 as well as the frontal eye field with an amyg-
dala model for the associative conditioning of a visual stimulus with a
bodily reaction representing a particular emotional state. Second, in
order to provide the model with realistic temporal learning properties,
a reward-timing model [2] simulating the afferent system to the dopa-
minergic area VTA has been integrated to temporally adjust the learn-
ing process through dopamine-mediated modulation of plasticity. This
timing model includes a number of brain areas, most prominently the
ventral tegmental area, the nucleus accumbens, the lateral hypothala-
mus, the ventral medial prefrontal cortex and the amygdala. In order
to enable emotional attention, 2 simulation phases were implemented:
(1) a conditioning phase to learn the association between an impor-
tant stimulus and the body reaction, and (2) an attention phase where
the representation of the visual stimulus activates the BLA which then
sends back a feedback to enhanced this specific stimulus. Afterwards,
the enhanced representation in V4 suppresses the competing ones
and allows the frontal eye field to initiate a saccade in its direction. As
a result of the biologically based connectivity and the realistic learning
process, the model outcomes are coherent with several experimental
findings and increase our understanding of the brain network's inter-
action. In the future, the model could furthermore be used for facial
analysis and the process of learning the importance of specific facial
features for emotional expressions.

**References**

1. Beuth F, Hamker FH. A mechanistic cortical microcircuit of attention for amplification, normalization and suppression. Vis Res. 2015;116:241-57.

2. Vitay J, Hamker FH. Timing and expectation of reward: a neuro-computational model of the afferents to the ventral tegmental area. Front Neurorobot. 2014;8:1-25.

P151

Multi-site delayed feedback stimulation in parkinsonian networks

Kelly Toppin¹, Yixin Guo¹

¹Department of Mathematics, Drexel University, Philadelphia, PA 19104, USA

**Correspondence:** Yixin Guo - yixin@math.drexel.edu

BMC Neuroscience 2016, 17(Suppl 1):P151

The conventional deep brain stimulation (DBS), as a surgical procedure to alleviate debilitating and disrupting symptoms of Parkinson's disease (PD), has several drawbacks. Multi-site delayed feedback stimulation (MDFS) has been proposed as a feasible alternative to overcome the drawbacks of the conventional DBS [2, 3]. We first build two types of large scale biophysical networks to explore the effectiveness of MDFS. The persistent parkinsonian network has strongly synchronized
<figure><img src="image_88.png" /></figure>

**Fig. 80 A** The network model. Plus symbol indicates excitatory connection, and minus symbol indicates inhibitory connection. LFP is computed from GPi and STN populations separately. These two LFP signals are used as the source of the five MDFS stimulations (dashed-arrows): STN-to-STN, STN-to-GPi, GPi-to-STN, GPi-to-GPe and GPi-to-GPi, shown by arrow labeled 1–5. B Error index values for 80 different model TC neurons in an intermittent network. Comparison of MDFS among different stimulation targets using either STN or GPi LFP signal. Whisker plots show mean (red line), 25–75 percentile range (blue box), 95 % confidence interval (black lines) and outliers (red plus signs)

bursting clusters with elevated firing rates present in subthalamic nucleus (STN), internal and external segments of globus pallidus (GPi and GPe) neurons. However, the brain of a PD patient may not be in a constant strong synchronized and clustered state, and short desynchronized events may present when the brain is in between high synchronization [1]. We build an intermittent parkinsonian network that can transit between synchronized and desynchronized dynamics. Using both parkinsonian networks, we compute the TC error index, the fraction of miss responses and excessive responses when a TC neuron relays multiple excitatory inputs, in five different stimulation settings: MDFS from STN to STN, from STN to GPe, from GPi to STN, from GPi to GPe and from GPi to GPi, shown by the dashed-arrows labeled 1–5 in Fig. 80A. Each “to” population is stimulated by the signal based on the LFP calculated at the “from” population. Our results of lower TC relay errors with the five different stimulations in Fig. 80B show that MDFS improves the fidelity of the TC relay neurons’ communication and responses to the input motor signal in both persistent and intermittent parkinsonian networks. We also find that MDFS with STN or GPe as a stimulation target is more effective in reducing TC relay errors.

**Acknowledgements:** The study was supported by NSF grant DMS-1226180 awarded to Yixin Guo.

## References

1. Ahn S, Rubchinsky L. Short desynchronization episodes prevail in synchronous dynamics of human brain rhythms. Chaos. 2013;23:013138.

2. Guo Y, Rubin JE. Multi-site stimulation of subthalamic nucleus diminishes thalamocortical relay errors in a biophysical network model. Neural Netw. 2011;24(6):602-16.

3. Hauptmann C, Omel'Chenko O, Popovych V, Maistrenko Y, Tass PA. Control of spatially patterned synchrony with multisite delayed feedback. Phys Rev E. 2007;76(6):066209.

## P152

### Bistability in Hodgkin–Huxley-type equations

Tatiana Kameneva¹, Hamish Meffin², Anthony N. Burkitt¹, David B Grayden¹,³

¹NeuroEngineering Laboratory, Department of Electrical & Electronic Engineering, University of Melbourne, Parkville, VIC 3010, Australia;

²National Vision Research Institute, Australian College of Optometry, Carlton, VIC 3053, Australia; ³Centre for Neural Engineering, University of Melbourne, Parkville, VIC 3010, Australia

**Correspondence:** René Richter - tkam@unimelb.edu.au

BMC Neuroscience 2016, 17(Suppl 1):P152

<figure><img src="image_89.png" /></figure>

**Fig. 81** Response of a modeled neuron for different initial conditions, V(0). Sodium, three types of potassium, calcium, and hyperpolarisation-activated currents are included in the model

**Background** Purkinje cells have two states of the resting membrane potential: a hyperpolarized quiescent state (down state) and a depolarized spiking state (up state) [1]. This bistability has been observed in in vitro and in vivo recordings, in anesthetized animals, and in slices. It has been proposed that bistability in Purkinje cells play a key role in the short-term processing and storage of sensory-motor information.

**Methods** To investigate bistability of the neuronal resting state, we use computer simulations in neuron. We simulate single compartment neurons and use the Hodgkin–Huxley-type formalism to study how initial conditions and a combination of ionic channels affect neuronal response. We systematically apply intracellular current pulse stimulation to set the membrane potential to different levels and observe the neuronal dynamics after the stimulation is released.

**Results** We show that the neural response after release of the pulse stimulation depends on the amplitude of the current pulses. For some stimulation levels, the cells return to the level of the activity prior to stimulation, while for other levels, the neuronal dynamics are different to prior activity levels for a long time post stimulation. We show that different initial conditions lead to different neuronal dynamics even when all other parameters in the Hodgkin–Huxley-type model are set the same (Fig. 81). We explore the region of attraction for two stable states and find that they differ for different parameters of the model, in particular that different ionic channel combinations do not change our qualitative results.

**Conclusions** This work demonstrates a potential method to explore the mechanisms underlying bistability in Purkinje cells. In particular, the proposed methodology allows the exploration of the circumstances under which Purkinje cells transit from the down state to the up state and return. This work implies that results obtained using the Hodgkin–Huxley formalism should be carefully considered since the choice of initial conditions may significantly affect the final outcome.

**Acknowledgements:** This research was supported by the Australian Research Council (ARC). TK acknowledge support through ARC Discovery Early Career Researcher Award (DE120102210).

## Reference

1. Loewenstein Y, Mahon S, Chadderton P, Kitamura K, Sompolinsky H, Yarom Y, Hausser. Bistability of cerebella Purkinje cells modulated by sensory stimulation. Nat Neurosci. 2005;8(2):202–11.

## P153

### Phase changes in postsynaptic spiking due to synaptic connectivity and short term plasticity: mathematical analysis of frequency dependency

Mark D. McDonnell¹, Bruce P. Graham²

¹Computational and Theoretical Neuroscience Laboratory, School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia; ²Computing Science and Mathematics, School of Natural Sciences, University of Stirling, Stirling, FK9 4LA, UK

**Correspondence:** Mark D. McDonnell - mark.mcdonnell@unisa.edu.au

BMC Neuroscience 2016, 17(Suppl 1):P153
<figure><img src="image_90.png" /><figcaption>**Fig. 82** The phase of post-synaptic firing in response to frequency-modulated inhomogenous Poisson pre-synaptic spike trains depends on the configuration of input synapses. **A** The connectivity involves M independent pre-synaptic neurons each with NM synaptic release sites at a post-synaptic neuron. Vesicles are released probabilistically when activated by a pre-synaptic action-potential, if one is available at that site. The post-synaptic neuron depolarizes and produces action potentials after arrival of neurotransmitter according to standard models. **B** The phase lead of output spiking relative to periodic modulation at frequency f, for M active-zones is both frequency-dependent and configuration-dependent. The figure is from simulations but we also derived the same result mathematically</figcaption></figure>

We examined how short-term synaptic depression due to vesicle depletion [1] interacts with the configuration of the synaptic pathways onto an output neuron. Using both simulations and mathematical analysis, we found significant frequency-dependent phase-shifts of the spiking response of a neuron driven by independent frequency-modulated Poisson input signals. The synaptic inputs to the neuron are assumed to consist of a fixed number of release sites that are divided between active zones, with each active zone being the presynaptic axonal target of a single input neuron (Fig. 82A). For the same number of release sites, at one extreme the output neuron receives input from a large number of neurons through independent active zones, each containing a single release site, similar to cortical cells. At the other extreme (similar to a Calyx of Held in the auditory brain-stem), the neuron is driven by a single input neuron through a giant synapse containing a single active zone with a very large number of release sites.

Using standard stochastic models of short term depression due to vesicle depletion [2], and post-synaptic current dynamics, we found strong phase dependencies for input modulation rates up to 5 Hz. The phase shift also depends strongly on the configuration (Fig. 82B). However, the phase shift otherwise remains invariant for a wide range of post-synaptic conditions, such as for Hodgkin-Huxley or leaky integrate-and-fire models, and whether or not the dynamics of post synaptic currents included rise-times, or longer or shorter decay times.

**Acknowledgements:** M. D. McDonnell was supported by an Australian Research Fellowship from the Australian Research Council (project DP1093425). B. P. Graham's contribution was supported by the BBSRC project grant BB/K01854X/1.

**References**

1. Abbott LF, Regehr WG. Synaptic computation. Nature. 2004;431:796–803.

2. McDonnell MD, Mohan A, Stricker C. Mathematical analysis and algorithms for efficiently and accurately implementing stochastic simulations of short-term synaptic depression and facilitation. Front Comput Neurosci. 2013;7:58.

P154

**Quantifying resilience patterns in brain networks: the importance of directionality**

Penelope J. Kale¹, Leonardo L. Gollo¹

¹Systems Neuroscience Group, QIMR Berghofer Medical Research Institute, Brisbane, QLD, 4006, Australia

**Correspondence:** Penelope J. Kale - Penelope.Kale@qimr.edu.au

BMC Neuroscience 2016, 17(Suppl 1):P154

Defining how interactions take place, directionality is major feature of network connections. Brain networks are intrinsically directed because of the nature of chemical synapses, which comprise most of the neuronal connections. The specific fingerprint of the interactions between cortical regions and neurons thereof are crucial to the neuronal dynamics. The neuronal ability to synchronize is extremely sensitive to the presence of reciprocal connections in neuronal motifs and circuits [1]. The type of synchronization (or the phase relation between phase locked neurons and cortical regions) also depends on the relation between the synaptic strengths between these regions [2, 3]. Moreover, whole brain network dynamics is also shaped by reciprocal connections, which stabilizes the network dynamics and reduce transitions between metastable states [4]. However, due to limitations in current brain imaging techniques, the directionality of edges between structurally connected regions of the human brain cannot be confirmed. Additionally, despite the demonstrated importance of synaptic direction, its effect over main network features is not yet elucidated. Comparing several directed brain networks from different species (macaque, cat, mouse, and *C. elegans*) and with variable node size (parcellation), we estimate the error that is made in characterizing and identifying brains as complex network when undirected networks are assumed. We use different approaches to turn directed networks undirected: (i) remove unidirectional links; (ii) add reciprocal links; (iii) add one reciprocal for each removed unidirectional link thus keeping the same network density. We find that directionality plays a major role in shaping the brain networks. All regions are affected, including hub nodes, which have large degree and enhanced importance in information integration for cognitive functions [5]. We compute and rank graph theoretical measures and determine their resilience with respect to the loss of directionality of the network. Overall, our results suggest that the characterization of connectomes can be compromised in the absence of data regarding the directionality of brain networks.

**References**

1. Gollo LL, Mirasso C, Sporns O, Breakspear M. Mechanisms of zero-lag synchronization in cortical motifs. PLoS Comput Biol. 2014;10(4):e1003548.

2. Matias FS, Carelli PV, Mirasso CR, Copelli M. Anticipated synchronization in a biologically plausible model of neuronal motifs. Phys Rev E. 2011;84(2):021922.

3. Matias FS, Gollo LL, Carelli PV, Bressler SL, Copelli M, Mirasso CR. Modeling positive Granger causality and negative phase lag between cortical areas. NeuroImage. 2014;99:411–8.

4. Gollo LL, Zalesky A, Hutchison RM, van den Heuvel M, Breakspear M. Dwelling quietly in the rich club: brain network determinants of slow cortical fluctuations. Philos Trans R Soc Lond B Biol Sci. 2015;370(1668):20140165.

5. van den Heuvel MP, Sporns O. Network hubs in the human brain. Trends Cogn Sci. 2013;17(12):683–96.

P155

**Dynamics of rate-model networks with separate excitatory and inhibitory populations**

Merav Stern¹, L.F. Abbott²

¹Faculty of Medicine, Technion, Haifa, Israel; ²Department of Neuroscience and Department of Physiology and Cellular Biophysics, Columbia University, New York, NY, USA

**Correspondence:** Merav Stern - merav.stern@mail.huji.ac.il

BMC Neuroscience 2016, 17(Suppl 1):P155

Randomly connected networks of rate-model neurons have a rich dynamics [1], a feature that has been exploited to model a variety of phenomena [2]. These model networks typically do not distinguish between excitatory and inhibitory neuron classes. Doing this requires constraining the network connectivity matrix to have columns with exclusively positive entries, representing input from excitatory neurons, and with negative entries, representing input from inhibitory neurons. The eigenvalue spectra of random matrices satisfying this constraint have a number of interesting properties [3, 4].

Here we study the dynamics of rate-model networks that result from using such connectivity matrices. We find that neural activity is
correlated across all neurons, including both excitatory and inhibitory subpopulations. This correlation depends on the difference between the mean strengths of the excitation and inhibition connections and it increases as this difference is increased. For very large values of this difference, the network reaches a stable fixed point, otherwise it is chaotic. Chaos arises from the residual activity deviating from the cor- related mean network activity and it acts to reduce these correlations. The magnitude of the residual chaotic activity is determined by the variances of the synaptic strengths within the excitatory and inhibitory populations.

In summary, unlike models with a single mixed excitatory/inhibitory population, in which the activity between pairs of neurons is uncor- related for every value of synaptic gain, networks with distinct excita- tory and inhibitory subpopulations exhibit strongly correlated activity across the entire network reminiscent of the up/down states seen in neural recordings [5].

## References

1. Sompolinsky H, Crissanti A, Sommers HJ. Chaos in random neural networks. Cerebral Phys Rev Lett. 1988;61:259-62.

2. Sussillo D. Neural circuits as computational dynamical systems. Curr Opin Neurobiol. 2014;25:156-63.

3. Rajan K, Abbott LF. Eigenvalue spectra of random matrices for neural networks. Phys Rev Lett. 2006;97:188104.

4. Tao T. Outliers in the spectrum of IID matrices with bounded rank pertur- bations. arXiv: 1012.4818v6.

5. Steriade M, Nunez A, Amzica F. A novel slow (<1 Hz) oscillation of neo- cortical neurons in vivo: depolarizing and hyperpolarizing components. J. Neurosci. 1993;13:3252-65.

# P156
## A model for multi-stable dynamics in action recognition modulated by integration of silhouette and shading cues

Leonid A. Fedorov¹,², Martin A Giese¹,²

¹Section for Computational Sensomotorics, Department of Cognitive Neurology, CIN&HIH, Tübingen, Germany; ²GTC, International Max Planck Research School, University of Tübingen, Tübingen, Germany

**Correspondence:** Leonid A. Fedorov - leonid.fedorov@uni-tuebingen.de
BMC Neuroscience 2016, 17(Suppl 1):P156

The visual perception of body motion can show interesting multi-sta- bility. For example, a walking body silhouette (bottom inset Fig. 83A) is seen alternately as walking in two different directions [1]. For stimuli with minimal texture information, such as shading, this multi-stability disappears. Existing neural models for body motion perception [2-4] do not reproduce perceptual switching. Extending the model [2], we developed a neurodynamic model that accounts for this multi-stability (Fig. 83A). The core of the model is a two-dimensional neural field that consists of recurrently coupled neurons with selectivity for instantane- ous body postures ('snapshots'). The dimensions of the field encode

<figure><img src="image_91.png" /></figure>

the keyframe number $\theta$ and the view of the walker $\phi$. The lateral con- nectivity of the field stabilizes two competing traveling pulse solu- tions that encode the perceived temporally changing action patterns (walking in the directions $\pm$45°). The input activity of the field is gener- ated by two visual pathways that recognize body postures from gray- level input movies. One pathway ('silhouette pathway') was adapted from [2] and recognizes shapes, mainly based on the contrast edges between the moving figure and the background. The second pathway is specialized for the analysis of luminance gradients inside the mov- ing figure. Both pathways are hierarchical (deep) architectures, built from detectors that reproduce known properties of cortical neurons. Higher levels of the hierarchies extract more complex features with higher degree of position/scale invariance. The field activity is read out by two Motion Pattern (MP) neurons, which encode the two possible perceived walking directions. Testing the model with an unshaded sil- houette stimulus, it produces randomly switching percepts that alter- nate between the walking directions ($\pm$45°) (Fig. 83B, C). Addition of shading cues disambiguates the percept and removes the bistability (Fig. 83D). The developed architecture accounts for the disambiga- tion by shape-from shading.

**Acknowledgements:** Supported by EC Fp7-PEOPLE-2011-ITN PITN- GA-011-290011 (ABC), FP7-ICT-2013-FET-F/604102 (HBP), FP7-ICT-2013- 10/611909 (Koroibot), BMBF, FKZ: 01GQ1002A, DFG GI 305/4-1 + KA 1258/15-1.

## References

1. Vangeneugden J, et al. Activity in areas MT+ and EBA, but not pSTS, allows prediction of perceptual states during ambiguous biological motion. Soc Neurosci Meet. 2012;127(04).

2. Giese MA, Poggio T. Neural mechanisms for the recognition of biological movements and action. Nat Rev Neurosci. 2003;4:179-92.

3. Lange J, Lappe M. A model of biological motion perception from con- figural form cues. J Neurosci. 2006;26:2894-906.

4. Jhuang H, et al. A biologically inspired system for action recognition. In: ICCV 2007. p. 1-8.

# P157
## Spiking model for the interaction between action recognition and action execution

Mohammad Hovaidi Ardestani¹,², Martin Giese¹

¹Section Computational Sensomotorics, CIN & HIH, Department of Cognitive Neurology, Tübingen, 72076, Germany; ²IMPRS for Cognitive and Systems Neuroscience, University Clinic Tübingen, Tübingen, 72076, Germany

**Correspondence:** Mohammad Hovaidi Ardestani - Mohammad. Hovaidi-Ardestani@uni-tuebingen.de

BMC Neuroscience 2016, 17(Suppl 1):P157

Action perception and the control of action execution are intrinsi- cally linked in the human brain. Experiments show that the concurrent motor execution influences the visual perception of actions and biological motion (e.g. [1]). This interaction likely is mediated by action-selective neurons in the STS, premotor and parietal cortex. We have developed a model based on biophysically realistic spiking neurons that accounts for the observed interactions between action perception and motor planning. The model is based on two dynamic representation levels (Fig. 84A), one modeling a representation of perceived action patters (vision field), and one representing associ- ated motor programs (motor field). Both levels are modeled by recur- rent spiking networks that approximate neural fields, where each field consists of 30 coupled neural ensembles, each consisting of 80 excita- tory and 20 inhibitory adaptive Exponential Integrate-and-Fire (aEIF) neurons [2]. Within each field asymmetric recurrent connections between the ensembles stabilize a traveling pulse solution, which is stimulus-driven in the visual field and autonomously propagating in the motor field after initiation by a go-signal. Both fields are coupled by interaction kernels that results in mutual excitation between the fields of the traveling pulse propagate synchronously and in mutual inhibition otherwise. We used the model to reproduce the result of a psychophysical experiment that tested the detection of point-light
<figure><img src="image_92.png" /><figcaption>**Fig. 84** A Model architecture consisting of two coupled neural fields, implemented with biophysically realistic neurons. B Psychophysical results from [1] showing the dependence of the detectability of visual point-light stimuli in dependence of the delay between a visually observed and the concurrently executed action. C Simulated detectability derived from the model for the same experimental conditions</figcaption></figure>

stimuli in noise during concurrent motor execution [1]. The point-light patterns showed arm movements of the observer, which were synchronized with varying time delays with the executed movements. Compared to a baseline without concurrent motor execution, the detectability of the visual stimulus was higher for very small time delays between the visual stimulus and the executed arm movement, and it was lower when the observed movement was strongly delayed (>300 ms) against the executed motor patterns (Fig. 84B). The same pattern arises from the detectability of the visual stimulus as predicted from our model, where we assumed that the level of neural activity (compared to a noise level) provides a measure for the detectability of the stimulus (Fig. 84C). The proposed model, which is derived by simplification from physiologically-inspired neural models for action execution and motor planning, reproduces correctly the modulation of visual detection by the synchrony of the stimulus with executed motor behavior. Present work extends the model by a full visual pathway and an effector model, allowing for the simulation of a broader spectrum of experimental results.

**Acknowledgements:** Supported by EC FP7-ICT-2013-FET-F/604102 (HBP), Fp7-PEOPLE-2011-ITN PITN-GA-011-290011 (ABC), FP7-ICT-2013-10/611909 (Koroibot), BMBF, FKZ: 01GQ1002A, DFG GI 305/4-1 + KA 1258/15-1.

## References

1. Christensen A, Ilg W, Giese MA. Spatiotemporal tuning of the facilitation of biological motion perception by concurrent motor execution. J Neurosci. 2011;31:3493–9.

2. Brette R, Gerstner W. Adaptive exponential integrate-and-fire model as an effective description of neuronal activity. J Neurophysiol. 2005;94:3637–42.

## P158

**Surprise-modulated belief update: how to learn within changing environments?**

Mohammad Javad Faraji¹, Kerstin Preuschoff², Wulfram Gerstner¹
¹School of Life Sciences, Brain Mind Institute and School of Computer and Communication Sciences, Ecole Polytechnique Federal de Lausanne (EPFL), CH-1015 Lausanne, Switzerland; ²Geneva Finance Research Institute (GFRI) and Swiss Center for Affective Sciences (CISA), University of Geneva, CH-1211 Geneva, Switzerland

**Correspondence:** Mohammad Javad Faraji - mohammadjavaad.faraji@epfl.ch

BMC Neuroscience 2016, 17(Suppl 1):P158

Surprise is informative because it drives attention [1] and modifies learning [2]. Correlates of surprise have been observed at different stages of neural processing, and found to be relevant for learning and memory formation [3]. Although surprise is ubiquitous, there is neither a widely accepted theory that quantitatively links surprise to observed behavior, such as the startle response, nor an agreement on how surprise should influence learning speed or other parameters in iterative statistical learning algorithms. Building on and going beyond earlier surprise measures [4–6], we propose a novel information theoretic measure for calculating surprise in a Bayesian framework so as to capture uncertainty of the world as well as imperfections of the subjective model of the world, two important aspects of surprise. The principle of future surprise minimization leads to a learning rule that can be interpreted as a surprise modulated belief update suitable for learning within changing environments. Importantly, we do not need an assumption on how quickly the world changes. We apply our surprise-modulated learning rule to an exploration task in a maze-like environment. Our results are consistent with the behavioral finding that surprising events induce humans and animals to learn faster and to adapt more quickly to changing environments. Information content [5] captures the inherent unexpectedness of a piece of data for a given set of models (uncertainty of the world), whereas Bayesian surprise [4] measures the change in belief caused by a new data point (observer dependent). These are two complementary approaches for calculating surprise. In our approach both aspects are combined with a third aspect: if we are uncertain about what to expect, receiving a low-probability data sample is less surprising than in a situation when we are almost certain about the world. A surprise minimizing learning (SMiLe) rule is derived by solving a constrained optimization problem defined as follows: the objective is to maximally reduce surprise when facing the same data again in the not so far future, under the constraint that the posterior belief (after the update step) is not too different from the prior. The resulting SMiLe rule balances the influence of newly acquired data with prior knowledge where the balance depends on surprise. In case of a fundamental change in the world signaled by surprising samples, data acquired before the change is downgraded as less informative about the current state of the world. A simultaneous increase of the influence of newly acquired data on learning leads to a fast adaptation of the model to an environmental change. While in a stationary environment our algorithm approaches the known Bayesian update rule, it also allows the model to react to changes in the environment. In summary, surprising data increases the uncertainty we have about our current model of the world and gives a bigger influence of newly acquired data on belief update. The interaction between surprise and uncertainty is important for modeling the behavior of humans and animals in changing environments. The surprise signal could be broadly transmitted in the brain by a neuromodulator with widespread axonal ramifications (e.g., norepinephrine (NE) released from locus coeruleus (LC) neurons) and influence synaptic plasticity rules.

**Acknowledgements:** This research was supported by the European Research Council (Grant Agreement No. 268 689).

## References

1. Itti L, Baldi P. Bayesian surprise attracts human attention. Vis Res. 2009;49(10):1295–1306.

2. Schultz W, Dickinson A. Neuronal coding of prediction errors. Annu Rev Neurosci. 2000;23(1):473–500.

3. Ranganath C, Rainer G. Neural mechanisms for detecting and remembering novel events. Nat Rev Neurosci. 2003;4(3):193–202.

4. Baldi P, Itti L. Of bits and wows: a bayesian theory of surprise with applications to attention. Neural Netw. 2010;23(5):649–66.

5. Shannon CE. A mathematical theory of communication. ACM SIGMOBILE Mobile Comput Commun Rev. 2001;5(1):3–55.

6. Palm G. Novelty, information and surprise. Berlin: Springer; 2012.
P159

**A fast, stochastic and adaptive model of auditory nerve responses to cochlear implant stimulation**

Margriet J. van Gendt¹, Jeroen J. Briaire¹, Randy K. Kalkman¹, Johan H. M. Frijns¹,²

¹ENT-Department, Leiden University Medical Centre, Leiden, 2300 RC, the Netherlands; ²Leiden Institute for Brain and Cognition, Leiden, 2300 RC, the Netherlands

**Correspondence:** Margriet J. van Gendt - m.j.van_gendt@lumc.nl

BMC Neuroscience 2016, 17(Suppl 1):P7

Cochlear implants (CI) rehabilitate hearing impairment through direct electrical stimulation of the auditory nerve. In many modern CIs sound is coded through the continuous interleaved sampling (CIS) strategy. Although many different sound-coding strategies have been introduced in the last decade, no major advances have been made since the introduction of the CIS strategy [1]. New stimulation strategies are commonly investigated by means of psychophysical experiments and clinical trials, which is time-consuming for both patient and researcher. Alternatively, strategies can be evaluated using computational models. In this study a computationally efficient model that accurately predicts auditory nerve responses to CI pulse train input is developed.

The model includes the 3D volume conduction and active nerve model developed in the Leiden University Medical Center [2], and is extended with stochasticity, adaptation and accommodation. This complete model includes spatial as well as temporal characteristics of both the cochlea and the auditory nerve. The stochastic and adaptive auditory nerve model is used to investigate full-nerve responses to amplitude modulated long duration stimulation. Understanding responses to amplitude modulation is important because current speech coding strategies are based on the principle of speech information distribution through amplitude modulation of the input pulse trains. The model is validated by comparison to experimentally measured single fiber action potential (SFAP) responses to pulse trains published in literature [3–6]. The effects of different pulse-train parameters such as pulse rate, pulse amplitude and amplitude modulation are investigated.

The neural spike patterns produced in response to CI stimulation are very similar to spike patterns obtained with single fiber action potential measurements in animal experiments in response to CI stimulation. Besides predicting single fiber responses to constant amplitude pulse trains, the model also very well predicts single fiber responses to amplitude modulated pulse trains. Response alterations seen over the duration of the stimulus are similar to those seen in animal experiments. Modeled effects of stimulus amplitude, pulse rate and amplitude modulation is similar to the effects seen in animal experiments. Adaptation is found to be an important factor in modeling nerve outcomes to amplitude modulated pulse trains and their spatial effects.

The model is shown to accurately predict spike timings in response to long duration pulse trains as observed in animal experiments. The model can be used to predict full auditory nerve responses to electrical pulse trains, and thus to different sound coding strategies. The next step will be to apply this model to evaluate complete auditory nerve responses to different sound coding strategies.

**Acknowledgements:** This study was financially supported by Advanced Bionics Corporation.

**References**

1. Zeng FG, Rebscher S, Harrison WV. Cochlear implants: system design, integration and evaluation. IEEE Rev Biomed Eng. 2008;115-42.

2. Kalkman RK, Briaire JJ, Dekker DMT, Frijns JHM. Place pitch versus electrode location in a realistic computational model of the implanted human cochlea. Hear Res. 2014;315:10-24.

3. Miller CA, Hu N, Zhang F, Robinson BK, Abbas PJ. Changes across time in the temporal responses of auditory nerve fibers stimulated by electric pulse trains. J Assoc Res Otolaryngol. 2008;9:122-37.

4. Litvak L, Delgutte B, Eddington D. Auditory nerve fiber responses to electric stimulation: modulated and unmodulated pulse trains. J Acoust Soc Am. 2001;110:368.

5. Zhang F, Miller CA, Robinson BK, Abbas PJ, Hu N. Changes across time in spike rate and spike amplitude of auditory nerve fibers stimulated by electric pulse trains. JARO J Assoc Res Otolaryngol. 2007;8:356-72.

6. Hu N, Miller CA, Abbas PJ, Robinson BK, Woo J. Changes in auditory nerve responses across the duration of sinusoidally amplitude-modulated electric pulse-train stimuli. J Assoc Res Otolaryngol. 2010;11:641-56.

P160

**Quantitative comparison of graph theoretical measures of simulated and empirical functional brain networks**

Won Hee Lee¹, Sophia Frangou¹

Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA

**Correspondence:** Won Hee Lee - wonhee.lee@mssm.edu

BMC Neuroscience 2016, 17(Suppl 1):P160

Graph theoretical approaches to resting-state fMRI have been widely used to quantitatively characterize functional network organization in the resting brain, but mechanistic explanations for how resting-state brain works are still lacking. Whole-brain computational models have shown promise in enriching our understanding of mechanisms contributing to the formation and dissolution of resting-state functional patterns [1]. It is therefore important to determine the degree to which computational models reproduce the topological features of empirical functional brain networks. Here, we focused on the performance of the Kuramoto model [2] as it is considered most representative model of coupled phase oscillators and is widely used in the literature.

Empirical and simulated functional networks were defined based on 66 brain anatomical regions (nodes). Simulated resting-state functional connectivity (FC) was generated using the Kuramoto model constrained by empirical structural connectivity. The simulated FC matrix was tuned to best fit empirical FC matrix. In order to improve stability and reliability, we simulated 10 runs of fMRI BOLD time series (obtained from 320 s simulations, discarding 20 s initial transients) with varying random initial conditions, and generated the best-fit simulated FC matrix for each run. We applied graph theoretical approaches to optimally simulated FC and empirical FC data to characterize key topological features of brain networks [3]. Finally, we quantified and compared the difference, in terms of relative error, in graph theoretical measures between the simulated and empirical functional networks.

Figure 85 shows the quantitative difference in graph theoretical measures between the empirical FC and the simulated FC over the entire (1–100 %) and selected range of connection densities (37–50 %). The averaged relative differences were found to be 2–77 % over the entire range of connection densities as well as 0.1–22 % over a range of 37–50 % connection densities. We found that simulated functional data can be used with confidence to model graph measures of global and local efficiency, characteristic path length, eigenvector centrality, and resilience to targeted attack and random failure. Our results also highlight the critical dependence of the solutions obtained in simulated data on the specified connection density.

This study demonstrates the value of computational models in assessing whole-brain network connectivity, and provides a method for the quantitative evaluation and external validation of graph theory metrics derived from simulated data that can be used to inform future study designs.

**References**

1. Deco G, Jirsa VK, McIntosh AR: Emerging concepts for the dynamical organization of resting-state activity in the brain. Nat Rev Neurosci. 2011;12(1):43-56.

2. Kuramoto Y. Chemical oscillations, waves, and turbulence. Berlin: Springer; 1984.

3. Rubinov M, Sporns O. Complex network measures of brain connectivity: uses and interpretations. Neuroimage. 2010;52:1059-69.
<figure><img src="image_93.png" /><figcaption>**Fig. 85** Relative error (RE) in percentage between graph theoretical measures of simulated FC versus empirical FC for the entire (1–100 %) and selected range of connection densities (37–50 %). *Bars and error bars* correspond respectively to the averages and standard deviations across the ten RE values. *E<sub>glob</sub>* global efficiency, *E<sub>loc</sub>* local efficiency, *CC* clustering coefficient, *L* characteristic path length, *EC* eigenvector centrality, *PC* participation coefficient, *SW*: small-worldness, *R<sub>f</sub><sup>c</sup>* and *R<sub>f</sub><sup>g</sup>* represent resilience to targeted attack in the size of largest connected component and global efficiency, respectively, *R<sub>f</sub><sup>c</sup>* and *R<sub>f</sub><sup>g</sup>* represent resilience to random failure in the size of largest connected component and global efficiency, respectively</figcaption></figure>

P161

Determining discriminative properties of fMRI signals
in schizophrenia using highly comparative time-series analysis

Ben D. Fulcher¹, Patricia H. P. Tran¹, Alex Fornito¹

¹Monash Institute of Cognitive and Clinical Neurosciences, Monash University, Clayton, Vic 3168, Australia

**Correspondence:** Ben D. Fulcher - ben.fulcher@monash.edu

BMC Neuroscience 2016, 17(Suppl 1):P161

Analysis of fMRI data typically focuses on inter-regional functional con-
nectivity, measured as pairwise correlations, or through multivariate
decompositions (e.g., ICA). Relatively little attention is given to the uni-
variate time-series properties of BOLD signals within a specific brain
region, despite a broad scientific literature on time-series analysis
(including power spectral techniques, information theoretic methods,
model fitting, nonlinear time-series analysis, and fractal scaling). Here
we undertake the largest systematic comparison of over 7000 such
measures of temporal structure to identify the temporal features of
individual BOLD signals, and their locations in the brain, that are most
discriminative of people with schizophrenia.

MRI data were obtained from the open COBRE database [4] for 72 peo-
ple with schizophrenia (SCZ) and 74 healthy controls (CON). For each
subject, we extracted 7779 temporal features from the BOLD time
series recorded in each of 264 brain regions using the publicly avail-
able highly comparative time-series analysis framework, *hctsa* (http://
benfulcher.github.io/hctsa/) [1].

Spatial analysis ROIs that were most discriminative of SCZ versus CON were identified by training a separate linear support vector machine (SVM) classifier for each ROI, across all features, using tenfold cross validation. We identified 23 ROIs with a classification accuracy exceeding chance levels (p < 0.05, FDR-corrected) with some individual ROI accuracies reaching 69.5 %. These discriminative brain regions were mostly located in the frontal and parietal cortices.

Temporal analysis The most discriminative temporal features were deduced using t-tests in each ROI, and then averaging across all ROIs. P values were computed using permutation tests with 1000 shuffles. We

identified over 100 time-series features of the BOLD signal with statis-
tically significant separability between SCZ and CON (p < 0.05). These
features were mostly measures of time series 'predictability', including
autocorrelation, local prediction error (using exponential smoothing,
Gaussian Processes, and AR models), the SD1 measure from the heart
rate variability literature, and low frequency power. This emergent
class of discriminative properties of BOLD dynamics is consistent with
the use of the ALFF metric in existing work using fMRI data [3].
We present the first systematic comparison of thousands of interdis-
ciplinary time-series analysis measures to fMRI data and use machine
learning to uncover characteristic BOLD signatures of schizophrenia,
in both space and time. In a completely data-driven manner, we iden-
tify informative brain regions and time-series analysis techniques that
best discriminate people with schizophrenia from healthy controls,
using just the properties of BOLD signals in individual ROIs. The frame-
work presented here represents a general and powerful data-driven
means of identifying discriminative time-series features from neurosci-
ence data.

**References**

1. Fulcher BD, Little MA, Jones NS. Highly comparative time-series analysis: the empirical structure of time series and their methods. J R Soc Interface. 2013;10:20130048.

2. Power JD, Cohen AL, Nelson SM, Wig GS, Barnes KA, Church JA, Vogel AC, Laumann TO, Miezin FM, Schlaggar BL, Petersen SE. Functional network organization of the human brain. Neuron. 2011;72:665–78.

3. Yu-Feng Z, Yong H, Chao-Zhe Z, Qing-Jiu C, Man-Qiu S, Meng L, Li-Xia T, Tian-Zi J, Yu-Feng W. Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI. Brain Dev. 2007;29:83–91.

4. COBRE database. http://fcon_1000 projects.nitrc.org/indi/retro/cobre. html.

P162

Emergence of narrowband LFP oscillations from completely asynchronous activity during seizures and high-frequency oscillations

Stephen V. Gliske¹, William C. Stacey¹,², Eugene Lim³, Katherine A. Holman⁴, Christian G. Fink³,⁵

¹Department of Neurology, University of Michigan, Ann Arbor, MI 48104, USA; ²Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48104, USA; ³Department of Physics, Ohio Wesleyan University, Delaware, OH 43015, USA; ⁴Department of Physics, Towson University, Towson, MD 21252, USA; ⁵Neuroscience Program, Ohio Wesleyan University, Delaware, OH 43015, USA

**Correspondence:** Christian G. Fink - cgfink@owu.edu

BMC Neuroscience 2016, 17(Suppl 1):P162

Recent experimental studies have demonstrated the emergence of narrowband local field potential oscillations during epileptic seizures in which the underlying neural activity appears to be completely asynchronous [1]. We derive a mathematical model explaining how this counterintuitive phenomenon may occur, showing that a population of independent, completely asynchronous neurons may produce narrowband oscillations if each neuron fires quasi-periodically. This quasi-periodicity can occur through cells with similar frequency-current (f-I) curves receiving a similar, high amount of uncorrelated synaptic noise. Thus, this source of oscillatory behavior is distinct from the usual cases (pacemaker cells entraining a network, or oscillations being an inherent property of the network structure), as it requires no oscillatory drive nor any specific network or cellular properties other than cells that repetitively fire with continual stimulus.

We deduce bounds on the degree of variability in neural spike-timing
which will permit the emergence of such oscillations, both for action
potential- and postsynaptic potential-dominated LFPs. (See Fig. 86
for example voltage traces and energy spectra resulting from asyn-
chronous neural activity, demonstrating how our model naturally
explains why PSPs tend to dominate the LFP at low frequency, while
APs dominate at high frequency.) These results suggest that even an
uncoupled network may generate collective rhythms, implying that
the breakdown of inhibition and high synaptic input often observed
during epileptic seizures may generate narrowband oscillations. We propose that this mechanism may explain why so many disparate epileptic pathologies can produce similar high frequency oscillations [2].

## References

1. Truccolo W, et al. Neuronal ensemble synchrony during human focal seizures. J Neurosci. 2014;34:9927–44.

2. Engel Jr J, Bragin A, Staba R, Modi I. High-frequency oscillations: what is normal and what is not? Epilepsia. 2009;50:598–604.

## P163

### Neuronal diversity in structure and function: cross-validation of anatomical and physiological classification of retinal ganglion cells in the mouse

Jinseop S. Kim<sup>1,2</sup>, Shang Mu<sup>2</sup>, Kevin L Briggman<sup>3</sup>, H. Sebastian Seung<sup>2,4</sup>, and the EyeWirers<sup>5</sup>

<sup>1</sup>Department of Structure and Function of Neural Networks, Korea Brain Research Institute, Daegu 41068, Republic of Korea; <sup>2</sup>Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; <sup>3</sup>Circuit Dynamics and Connectivity Unit, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20824, USA; <sup>4</sup>Computer Science Department, Princeton University, Princeton, NJ 08544, USA; <sup>5</sup>http://eyewire.org

**Correspondence:** Jinseop S. Kim - jinseop.s.kim@kbri.re.kr

BMC Neuroscience 2016, 17(Suppl 1):P163

The neural computation of visual perception begins in the retina. The retinal neural circuits receive inputs from the photoreceptors, spread out along interneurons, and converge to retinal ganglion cells (RGCs). The axons of RGCs are the only output of the retina and carry all the visual information from the retina to the rest of the brain. Each type of RGCs is thought to be associated with one microcircuit and to process distinct visual information. Therefore, classifying the types is an important step towards understanding the neural computation in the retina and retina's role in vision [1, 2].

We anatomically classified roughly 400 RGCs based mainly on dendritic stratification profiles [3]. The RGC dendritic arbors were reconstructed from serial electron microscope (EM) images of a (0.3 mm)<sup>2</sup> slice of the inner plexiform layer of the mouse retina [4]. The reconstruction was carried out on EyeWire, a web-based EM reconstruction pipeline that combines artificial intelligence of deep learning and human intelligence of a community of 'citizen neuroscientists' [5]. This is the first time EM reconstruction was done on a large enough area to potentially sample and identify all RGC types.

For cross-validation of the anatomical classification, we compared it with the visual responses of the same cells recorded by calcium imaging performed before EM preparation. The comparison confirmed that our classification recovered all well-known ganglion cell types including on-off direction selective ganglion cells (DSGCs), sustained/transient On DSGCs, asymmetric Off DSGC types, sustained/transient and On/Off alpha cells, and local edge detectors. We also found orientation selective or direction selective responses in some cell types that were not previously well-characterized or were previously unknown. In all, our classification includes over 40 types of RGCs.

## References

1. Sanes JR, Masland RH. The types of retinal ganglion cells: current status and implications for neuronal classification. Annu Rev Neurosci. 2015;38:221–46.

2. Baden T, Berens P, Franke K, Rosón MR, Bethge M, Euler T. The functional diversity of retinal ganglion cells in the mouse. Nature. 2016;529:345–50.

3. Sümbül U, Song S, McCulloch K, Becker M, Lin B, Sanes JR, Masland RH, Seung HS. A genetic and computational approach to structurally classify neuronal types. Nat Commun. 2014;5:3512.

4. Briggman KL, Helmstaedter M, Denk W. Wiring specificity in the direction-selectivity circuit of the retina. Nature. 2011;471:183–88.

5. Kim JS, Greene MJ, Zlateski A, Lee K, Richardson M, Turaga SC, Purcaro M, Balkam M, Robinson A, Behabadi BF, et al. Space-time wiring specificity supports direction selectivity in the retina. Nature. 2014;509:331–6.

<figure><img src="image_94.png" /></figure>

**Fig. 86** Normalized energy spectra and voltage traces resulting from asynchronous neural activity. **A, B** Results of superimposed, asynchronous action potential waveforms for quasi-periodic frequencies of 100 Hz (**A**) and 200 Hz (**B**). **C, D** Results of superimposed, asynchronous postsynaptic potential waveforms for quasi-periodic frequencies of 100 Hz (**A**) and 200 Hz (**B**). Gray dashed lines represent energy spectra that would result from Poisson process spike trains convolved with AP/PSP waveforms

## P164

### Analysis and modelling of transient firing rate changes in area MT in response to rapid stimulus feature changes

Detlef Wegener<sup>1</sup>, Lisa Bohnenkamp<sup>1,2</sup>, Udo A. Ernst<sup>2</sup>

<sup>1</sup>Brain Research Institute, University of Bremen, 28334 Bremen, Germany;

<sup>2</sup>Institute for Neurophysics, University of Bremen, 28334 Bremen, Germany

**Correspondence:** Detlef Wegener - wegener@brain.uni-bremen.de

BMC Neuroscience 2016, 17(Suppl 1):P164

Neurons in area MT of the primate visual system are strongly tuned to the direction and speed of moving stimuli, and they exhibit pronounced transients in their firing rates after changes in visual stimulation. These transients increase the sensitivity of neurons and they are closely correlated to behavioral performance. For example, arbitrary instantaneous speed changes are associated with transients of different sign and amplitude, which closely correlate with the sign and magnitude of the preceding stimulus change and with behavioral performance [1, 2]. Interestingly, the transients' size cannot be directly referred from the neuron's underlying speed tuning, and is significantly more pronounced if the base speed before the change is far from the neuron's preferred speed. Understanding the neural dynamics shaping these responses, and their effects on information transmission of arbitrary time-varying signals, is key to understanding how the visual system copes with dynamic scenes. We here present a dynamical model for MT neurons that reproduces detailed characteristics of experimentally observed transients (Fig. 87). The model takes the single cell's kinetics and its speed tuning into account. Based on
<figure><img src="image_95.png" /></figure>

divisive inhibition of excitation, it is capable to reproduce and explain the specific transients of single neurons. Single direction column are made up of one excitatory and one inhibitory population, with the inhibitory population providing divisive inhibition onto the excitatory population. By combining multiple direction columns to one hyper-column, the model consists of N × 2 populations, with the excitatory populations receiving different input depending on their tuning parameters and the stimulus, and the inhibitory populations receiving an input averaged over the neighboring columns' input by a Gaussian kernel plus a fixed offset. Using an optimization procedure, the model reliably reproduces MT cell responses to arbitrary accelerations and decelerations of a moving stimulus, starting from both low and high base speeds, reproducing recently unexplained experimental data. If the inhibitory time constant is a multiple of the excitatory time constant, the model is analytically tractable for a piecewise constant input current: The analytical solution allows quantifying the transients' magnitude as a function of general neuron parameters such as response gain and time constants, providing precise predictions for population responses to brief events of arbitrary contrast.

## References

1. Galashan FO, Saßen HC, Kreiter AK, Wegener D. Monkey area MT latencies to speed changes depend on attention and correlate with behavioral reaction times. Neuron. 2013;78(4):740–50.

2. Traschütz A, Kreiter AK, Wegener D: Transient activity in monkey area MT represents speed changes ind is correlated with human behavioral performance. *J Neurophysiol* 2015, 113(3):890-903.

## P165

### Step-wise model fitting accounting for high-resolution spatial measurements: construction of a layer V pyramidal cell model with reduced morphology

Tuomo Mäki-Marttunen¹, Geir Halnes², Anna Devor³,⁴, Christoph Metzner⁵, Anders M. Dale³,⁴, Ole A. Andreassen¹, Gaute T. Einevoll²,⁶
¹NORMENT, Institute of Clinical Medicine, University of Oslo, Norway;

²Department of Mathematical Sciences and Technology, Norwegian University of Life Sciences, Ås, Norway; ³Department of Neurosciences, University of California San Diego, La Jolla, CA, USA; ⁴Department of Radiology, University of California San Diego, La Jolla, CA, USA;

⁵Biocomputation Research Group, University of Hertfordshire, Hatfield, UK; ⁶Department of Physics, University of Oslo, Norway

**Correspondence:** Tuomo Mäki-Marttunen - tuomomm@uio.no

BMC Neuroscience 2016, 17(Suppl 1):P165

Novel imaging methods such as intracellular Ca²⁺ imaging and voltage-sensitive dye measurements provide ever finer spatiotemporal data about single-neuron activity. The challenge for model fitting methods is to incorporate these data in order to describe the neuron behavior in a manner that faithfully preserves the signal propagation and membrane potential dynamics across the neuronal dendrites. A difficulty in this task is the evidently large number of different ion channels residing along the dendritic and perisomatic locations: Unless extra care is taken, the role of specific species of ion channel could be under- or overestimated at the expense of another type of ion channel.

In this work, we propose an automatic step-wise model fitting procedure as a solution to this challenge. Our approach resembles that of [1], but our objective functions are designed to account for correct membrane potentials not only at soma but also along the dendrites. In addition, we replace the need for spatial occlusion of parts of dendrite ("pinching") [2] in the experimental setup by a cumulative use of ion channel blockers.

We apply this procedure to construct a reduced-morphology version of the layer V pyramidal cell model of [3]. We simulated the cumulative blocking of ion channels by setting the corresponding ion channel conductances to zero in the full model, and measured the membrane potential (and Ca²⁺ concentrations when needed) along the soma and dendrites at each step. We then fitted the maximal conductances in the model with reduced morphology in four steps, starting with passive parameters (1st step), continuing with Iₕ current conductances (2nd step), Ca²⁺ dynamics and related conductances (3rd step), and ending with ion channel conductances that are in charge of the spiking behavior (4th step).

We show that our model with reduced morphology correctly reproduces important aspects of the membrane potential dynamics across the neuron, both in the control condition (see Fig. 88), and under the effect of the abovementioned ion channel blockers. In the final step of our study, we present and apply a method for reducing the number of synaptic contacts (from 1000s to a few 100s) yet maintaining the spatio-temporal activation pattern of the neuron. The obtained network model is cost-efficient in terms of both simulation time and memory requirements. Our model is publicly accessible in ModelDB, accession number 187474, as NEURON and NeuroML-2 descriptions (https://senselab.med.yale.edu/ModelDB/showModel.cshtml?model=187474).
<figure><img src="image_96.png" /><figcaption>**Fig. 88** Comparison of model with reduced (red) morphology to the model with full (blue) morphology. The y-axis shows the membrane potential at soma (solid) and apical dendrite (dashed) as a response to a somatic 200-ms DC pulse</figcaption></figure>

## References

1. Bahl A, Stemmler MB, Herz AV, Roth A. Automated optimization of a reduced layer 5 pyramidal cell model based on experimental data. J Neurosci Methods. 2012;210(1):22-34.

2. Bekkers JM, Häusser M. Targeted dendrotomy reveals active and passive contributions of the dendritic tree to synaptic integration and neuronal output. Proc Natl Acad Sci. 2007;104(27):11447-52.

3. Hay E, Hill S, Schürmann F, Markram H, Segev I. Models of neocortical layer 5b pyramidal cells capturing a wide range of dendritic and perisomatic active properties. PLoS Comput Biol. 2011;7:e1002107.

## P166

### Contributions of schizophrenia-associated genes to neuron firing and cardiac pacemaking: a polygenic modeling approach

Tuomo Mäki-Marttunen¹, Glenn T. Lines², Andy Edwards², Aslak Tveito², Anders M. Dale³, Gaute T. Einevoll⁴, Ole A. Andreassen¹

¹NORMENT, Institute of Clinical Medicine, University of Oslo, Norway;

²Simula Research Laboratory and Center for Cardiological Innovation, Oslo, Norway; ³Multimodal Imaging Laboratory, UC San Diego, La Jolla, CA, USA; ⁴Department of Mathematical Sciences and Technology,

Norwegian University of Life Sciences, Ås, Norway

**Correspondence:** Tuomo Mäki-Marttunen - tuomomm@uio.no

BMC Neuroscience 2016, 17(Suppl 1):P166

A recent genome-wide association study (GWAS) of schizophrenia (SCZ) has identified more than a hundred genetic loci exceeding genome-wide significance, confirming the polygenic nature of the disorder [1]. The loci implicate genes that encode numerous ion channel subtypes and calcium transporters, and are major contributors not only to the function of brain cells, but also to the functioning of organs outside the central nervous system, such as heart. Meta-studies have reported a 2.5-fold-threefold increase in mortality rates in schizophrenic patients, and majority of these excess deaths are natural and mostly due to cardiovascular disease [2]. In agreement with this observation, GWASs of cardiac phenotypes, such as electrocardiographic (ECG) measures, highlight a set of genes that overlaps with the one discovered in GWASs of SCZ. Nevertheless, both the genetic and mechanistic connections between cardiac and neural phenotypes in SCZ patients remain poorly understood.

In this work, we use computational modeling to study the contribution of SCZ-associated genes to cardiac and neuronal excitability. We focus our analyses on two central, well-studied cell types, namely, layer V pyramidal cells (L5PCs) in the cortex and sinoatrial node cells

(SANCs) in the myocardium. The apical tuft of an L5PC serves as an integration hub for non-local synaptic inputs, and is considered a biological substrate for cortical associations providing high-level "context" for low-level (e.g., sensory) inputs that arrive to the perisomatic compartment. Therefore, the ability of L5PC to integrate the apical and perisomatic inputs has been proposed as one of the mechanisms that could be impaired in hallucinating patients. The SANCs, in turn, have a key role in controlling the heart rate as the primary pacemakers of the mammalian heart. Both of these cell types are well described in terms of biophysical modeling, and are therefore a suitable target for a detailed computational studies incorporating genetic effects. We apply two recent multicompartmental L5PC models and two recent SANC models to argue for the generality of our findings.

We show that small changes in the parameters governing the voltage-dependence and time constants of activation and inactivation of different ion channels caused observable effects in both L5PC and SANC function. In the case of $Ca^{2+}$ channel gene variants, these changes typically had opposite effects on cell excitability in L5PCs compared to SANCs (higher L5PC firing frequency ↔ lower SANC pacemaking frequency), while in the case of $Na^+$ or HCN channel variants, the effects were mostly similar (higher L5PC firing frequency ↔ higher SANC pacemaking frequency). Furthermore, many of the studied variants showed an impact on signal propagation in a chain of coupled SANCs. Our results may help explain some of the cardiac comorbidity in schizophrenia, and may facilitate generation of effective antipsychotic medications with less arrhythmia side-effects.

## References

1. Ripke S, Sanders AR, Kendler KS, Levinson DF, Sklar P, Holmans PA, Lin DY, Duan J, Ophoff RA, Andreassen OA et al: Genome-wide association study identifies five new schizophrenia loci. Nat Gen. 2011;43:969-76.

2. Laursen TM, Munk-Olsen T, Vestergaard M. Life expectancy and cardiovascular mortality in persons with schizophrenia. Curr Opin Psychiatry. 2012;25(2):83-8.

3. Hay E, Hill S, Schürmann F, Markram H, Segev I: models of neocortical layer 5b pyramidal cells capturing a wide range of dendritic and perisomatic active properties. PLoS Comput Biol. 2011;7:e1002107.

4. Almog M, Korngreen A. A quantitative description of dendritic conductances and its application to dendritic excitation in layer 5 pyramidal neurons. J Neurosci. 2014;34(1):182-96.

5. Kharche S, Yu J, Lei M, Zhang H. A mathematical model of action potentials of mouse sinoatrial node cells with molecular bases. Am J Physiol Heart Circ Physiol. 2011;301(3):H945-63.

6. Severi S, Fantini M, Charawi LA, DiFrancesco D. An updated computational model of rabbit sinoatrial action potential to investigate the mechanisms of heart rate modulation. J Physiol. 2012;590(18):4483-99.

## P167

### Local field potentials in a 4 × 4 mm² multi-layered network model

Espen Hagen¹, Johanna Senk¹, Sacha J van Albada¹, Markus Diesmann¹,²,³
¹Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA BRAIN Institute I, Jülich Research Centre, Jülich, 52425, Germany; ²Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, RWTH Aachen University, Aachen, 52074, Germany; ³Department of Physics, Faculty 1, RWTH Aachen University, Aachen, 52074, Germany

**Correspondence:** Espen Hagen - e.hagen@fz-juelich.de

BMC Neuroscience 2016, 17(Suppl 1):P167

The local field potential (LFP), the low-frequency part of extracellular potentials in neural tissue, is routinely recorded as a measure of population activity. LFPs reflect correlated activity of both local and remote neurons and depend on the anatomy and electrophysiology of neurons near the recording location. While forward models have shed light on various aspects of LFPs, e.g., their spatial reach [1], such models often ignore network interactions. Large-scale network models commonly use point neurons for tractability (see, e.g., [2]). However, predicting the LFP signal from such models is not straightforward, as point neurons do not generate extracellular potentials. In [3] we provided methods to compute extracellular potentials from point-neuron networks incorporating the biophysical principles of LFP generation
<figure><img src="image_97.png" /><figcaption>**Fig. 89** A Instantaneous spiking and LFP in a 4-layer network model covering 4 × 4 mm² at realistic cell and synapse density with distance-dependent connectivity. B Pairwise correlations between spike trains of exc. (E) and inh. (I) layer 5 neurons as function of distance (red: E-E, blue: I-I, black: E-I). C Distance-dependent LFP correlation computed for a 10 × 10 electrode grid in layer 5 (0.4 mm between contacts)</figcaption></figure>

using multicompartment neurons. This hybrid scheme uses spike times of point neurons as spatially dependent synaptic input with layer specificity of connections from anatomical data. The methods were demonstrated using a laterally homogeneous, layered point-neuron network representing 1 mm² of early sensory cortex at full cell and synapse density [4]. Preserving biological cell and connection densities is critical: networks may not be strongly downscaled without affecting correlations [5], and diluted LFP-generating populations fail to preserve the effect of correlations on the LFP [3]. Even small network correlations dominate in the compound LFP spectrum due to the different scaling of average single-cell LFP spectra and average pairwise coherence of single-cell LFP. Here, we extend this work to a network covering 4 × 4 mm² (Fig. 89A) accounting for connection probabilities falling off with lateral distance. Even for low pairwise spike-train correlations (Fig. 89B), the model accounts for highly correlated LFPs across lateral distance (Fig. 89C) as observed experimentally. Further we show that such features strongly depend on network state.

**Acknowledgements:** EU FP7 grant 604102 (HBP); Helmholtz Portfolio Supercomputing and Modeling for the Human Brain (SMHB).

## References

1. Lindén H, Tetzlaff T, Potjans TC, Pettersen KH, Gruen S, Diesmann M, Einevoll GT. Modeling the spatial each of the LFP. Neuron. 2011;72:859–72.

2. Bos H, Diesmann M, Helias M. Identifying anatomical origins of coexisting oscillations in the cortical microcircuit. arXiv:1510.00642 [q-bio.NC] 2016.

3. Hagen E, Dahmen D, Stavrinou ML, Lindén H, Tetzlaff T, van Albada SJ, Grün S, Diesmann M, Einevoll GT. Hybrid scheme for modeling local field potentials from point-neuron networks. arXiv:1511.01681 [q-bio.NC] 2016. http://inm-6.github.io/hybridLFPy.

4. Potjans TC, Diesmann M. The cell-type specific cortical microcircuit: relating structure and activity in a full-scale spiking network model. Cereb Cortex. 2014;24:785–806.

5. Van Albada SJ, Helias M, Diesmann M. Scalability of asynchronous networks is limited by one-to-one mapping between effective connectivity and correlations. PLoS Comput Biol. 2015;11(9):e1004490.

# P168

## A spiking network model explains multi-scale properties of cortical dynamics

Maximilian Schmidt<fnref n="1" />, Rembrandt Bakker<fnref n="1" />,², Kelly Shen³, Gleb Bezgin<fnref n="4" />, Claus-Christian Hilgetag⁵,⁶, Markus Diesmann<fnref n="1" />,<fnref n="7" />,⁸, Sacha Jennifer van Albada<fnref n="1" />

Neural networks in visual cortex are structured into areas, layers, and neuronal populations with specific connectivity at each level. Cortical dynamics can similarly be characterized on different scales, from single-cell spiking statistics to the structured patterns of interactions between areas. A challenge of computational neuroscience is to investigate the relation of the structure of cortex to its dynamics. Network models are promising tools, but for technical and methodological reasons, they have been restricted to detailed models of one or two areas or large-scale models that reduce the internal structure of areas to a small number of differential equations.

We here present a multi-scale spiking network model of all vision-related areas of macaque cortex that represents each area by a full-scale microcircuit with area-specific architecture based on a model of early sensory cortex [1]. The layer- and population-resolved network connectivity integrates axonal tracing data from the CoMac database with recent quantitative tracing data, and is systematically refined using dynamical constraints [2]. Gaps in the data are bridged by exploiting regularities of cortical structure such as the exponential decay of connection densities with inter-areal distance and a fit of laminar patterns versus logarithmized ratios of neuron densities.

Simulations reveal a stable asynchronous irregular ground state with heterogeneous activity across areas, layers, and populations. In the presence of large-scale interactions, the model reproduces longer intrinsic time scales in higher compared to early visual areas, similar to experimental findings [3]. Activity propagates preferentially in the feedback direction, mimicking experimental results associated with visual imagery [4]. Cortico-cortical interaction patterns agree well with fMRI resting-state functional connectivity [5]. The model bridges the gap between local and large-scale accounts of cortex, and clarifies how the detailed connectivity of cortex shapes its dynamics on multiple scales.

**Acknowledgements:** VSR computation time Grant JINB33, Helmholtz Portfolio SMHB, EU Grant 269921 (BrainScaleS), EU Grant 604102 (Human Brain Project, HBP), SFB936/A1, Z1 and TRR 169/A2.

## References

1. Potjans TC, Diesmann M. The cell-type specific cortical microcircuit: relating structure and activity in a full-scale spiking network model. Cereb Cortex. 2014;24:785–806.

2. Schuecker J, Schmidt M, van Albada SJ, Diesmann M, Helias M. Fundamental activity constraints lead to specific interpretations of the connectome. arXiv preprint 2015, arXiv:1509.03162.

3. Murray JD, Bernacchia A, Freedman DJ, Romo R, Wallis JD, Cai X, Padoa-Schioppa C, Pasternak T, Seo H, Lee D, Wang X-J. A hierarchy of intrinsic timescales across primate cortex. Nat Neurosci. 2014;17:1661–3.

4. Dentico D, Cheung BL, Chang J-Y, Guokas J, Boly M, Tononi G, Van Veen B. Reversal of cortical information flow during visual imagery as compared to visual perception. Neuroimage. 2014;100:237–243.

5. Shen K, Bezgin G, Hutchison RM, Gati JS, Menon RS, Everling S, McIntosh AR. Information processing architecture of functionally defined clusters in the macaque cortex. J Neurosci. 2012;32:17465–76.

# P169

## Using joint weight-delay spike-timing dependent plasticity to find polychronous neuronal groups

Haoqi Sun<fnref n="1" />,²,³,⁵, Olga Sourina²,⁵, Guang-Bin Huang³,⁵, Felix Klanner<fnref n="4" />,⁵, Cornelia Denk<fnref n="1" />

<footnote n="1">¹Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6), and JARA BRAIN Institute I, Jülich Research Centre, Jülich, Germany; ²Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, Netherlands; ³Rotman Research Institute, Baycrest, Toronto, Ontario M6A 2E1, Canada;</footnote>

<footnote n="4">⁴McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada; ⁵Department of Computational Neuroscience, University Medical Center Eppendorf, Hamburg, Germany; ⁶Department of Health Sciences, Boston University, Boston, MA, USA;</footnote>

<footnote n="7">⁷Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, RWTH Aachen University, Aachen, Germany; ⁸Department of Physics, Faculty 1, RWTH Aachen University, Aachen, Germany</footnote>

<footnote>**Correspondence:** Maximilian Schmidt - max.schmidt@fz-juelich.de
BMC Neuroscience 2016, 17(Suppl 1):P168</footnote>

<footnote n="1">¹Energy Research Institute @ NTU (ERI@N), Interdisciplinary Graduate School, Nanyang Technological University, Singapore 639798; ²Fraunhofer IDM @ NTU, Nanyang Technological University, Singapore 639798;</footnote>
³School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798; ⁴School of Computer Engineering, Nanyang Technological University, Singapore 639798; ⁵Future Mobility Research Lab, A Joint Initiative of BMW Group & NTU, Nanyang Technological University, Singapore 639798

**Correspondence:** Haoqi Sun - hsun004@entu.edu.sg

BMC Neuroscience 2016, 17(Suppl 1):P169

It is known that polychronous neuronal groups (PNGs), i.e. neuron groups having reproducible time-locked but not synchronous firing patterns, can function as representative entities [1]. They have huge capacity by sharing neurons. They compete between each other to represent sensory inputs. Therefore, PNG is considered as one of the potential, yet elusively difficult to analyze, hypothetical mechanisms of memory in the brain.

In computational models, the difficulties of finding PNGs mainly come from (1) low percentage of spikes from PNGs (about 4 % [1]) when driven by random inputs; and (2) combination explosion to enumerate all possible PNGs for template-matching (possible PNGs triggered by 3 neurons in a 1000-neuron network is 3C1000 = 1.66 × 10⁸).

Here we aim at solving the second difficulty without template-matching by connecting PNG readout neurons with joint weight-delay spike-timing dependent plasticity (joint STDP) to the network. The joint STDP consists of (1) weight STDP with the conventional exponential learning window; and (2) (axonal) delay STDP with learning window of shape te⁻¹/τ, scaled by weight-related gains. The joint STDP strengths and pulls together spikes arriving before postsynaptic firing, on the other hand weakens and postpones spikes after postsynaptic firing. In this way, we can recover the PNG by looking at (1) the strengthened synapses, which tells which neurons belong to the PNG; and (2) the delays of the strengthened synapses, which are complementary to the spike timing inside the PNG, because the presynaptic spike arrival times for the readout neuron (=spike timing + delay) are pulled close to each other.

In the experiment, we repeatedly fed structured inputs to a sparsely connected network of 800 excitatory and 200 inhibitory neurons. There were 150 readout neurons connected to the network with lateral inhibition between them. After 405 s of simulation, we used the incoming weights and delays of the readout neurons to find PNGs (see Fig. 90). It turned out that the readout neurons can learn the subsets of the persistently activated PNGs. The readout neurons do not rely on template-matching. Instead, they become differentiated members of the PNG to indicate the actual activation of its subsets.

<figure><img src="image_98.png" /></figure>

**Reference**

1. Izhikevich EM. Polychronization: computation with spikes. Neural Com- put. 2006;18(2):245–82.

P170

Tensor decomposition reveals RSNs in simulated resting state fMRI

Katharina Glomb¹, Adrián Ponce-Alvarez¹, Matthieu Gilson¹, Petra Ritter²,³,⁴,⁵, Gustavo Deco¹,⁶

¹Center for Brain and Cognition, Universitat Pompeu Fabra, 08018 Barcelona, Spain; ²Minerva Research Group Brain Modes, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany; ³Department of Neurology, Charité - University Medicine, 10117 Berlin, Germany; ⁴Bernstein Focus State Dependencies of Learning & Bernstein Center for Computational Neuroscience, 10115 Berlin, Germany; ⁵Berlin School of Mind and Brain & Mind and Brain Institute, Humboldt University, 10117 Berlin, Germany; ⁶Catalan Institution for Advanced Studies (ICREA), Universitat Barcelona, 08010 Barcelona, Spain

**Correspondence:** Won Hee Lee - katharina.glomb@upf.edu

BMC Neuroscience 2016, 17(Suppl 1):P170

The subject of this study is the temporal dynamics of functional connectivity (FC) in human resting state (RS) as measured with BOLD fMRI. In spite of rising interest in the topic [1], it remains unclear whether observed FC is stationary or if state switching is present, nor is it clear what constitutes these putative states. Modelling is an invaluable tool for answering these questions: here we combine a dynamic mean field model of the cortex with data analysis in order to determine whether and to what extent spatio-temporal FC patterns found in empirical data can be mimicked by a stationary model as described in [2]. To this end, we cast our data into tensor form by computing time-dependent FC inside of sliding windows (dynamic FC, dFC), comparing three methods to compute dFC (two correlation based, and mutual information). We employ canonical polyadic decomposition (also known as parallel factor analysis) with or without non-negativity constraint to decompose the tensors, which allows us to simultaneously consider the temporal and spatial dimensions [3]. First, we decompose such tensors obtained from empirical data of 24 subjects [4] and cluster resulting spatial features (i.e., communities) in order to obtain a small number of templates. These templates are used in a second step to compare to simulated data that is processed in the same way. We find that even on a very low level of spatial resolution (66 cortical regions), and using only the 2 % biggest dFC values in terms of region pairs and time windows, we succeed in extracting communities that generalize across subjects and can be found in the simulated data. Furthermore, we show that using model-based effective connectivity to inform the model [5] leads to more realistic and stable communities than diffusion weighted MRI-based structural connectivity alone. The method shown here is widely applicable to compare patient groups, data obtained from different tasks as well as mental states, and opens the door to understanding the differences between the temporal dynamics of these conditions.

**Acknowledgements:** KG is funded by the Marie Curie Initial Training Network INDIREA, grant agreement no ITN-2013-606901. APA was supported by SEMAINE ERA-Net NEURON Project. MG acknowledges funding from FP7 FET ICT Flagship Human Brain Project (604102). GD was supported by the ERC Advanced Human Brain Project (n. 604102) and the Plan Estatal de Fomento de la investigación Científica y Técnica de Excelencia (PSI2013-42091-P).

**References**

1. Hutchison RM, Womelsdorf T, Allen EA, Bandettini PA, Calhoun VD, Corbetta M, Della Penna S, Duyn JH, Glover GH, Gonzalez-Castillo J, Handwerker DA, Keilholz S, Kiviniemi V, Leopold DA, de Pasquale F, Sporns O, Walter M, Chang C. Dynamic functional connectivity: promise, issues, and interpretations. Neurolmage. 2013;80:360–78.

2. Deco G, Ponce-Alvarez A, Hagmann P, Romani G, Mantini D, Corbetta M. How local excitation–inhibition ratio impacts the whole brain dynamics. *J Neurosci.* 2014;34(23):7886–98.
3. Cichocki A. Tensor decompositions: a new concept in brain data analysis? *arxiv Prepr.* 2013, arXiv1305.0395, 507–17.

4. Schirner M, Rothmeier S, Jirsa VK, McIntosh AR, Ritter P. An automated pipeline for constructing personalized virtual brains from multimodal neuroimaging data. Neurolmage. 2015;117:343–57.

5. Gilson M, Moreno-Bote R, Ponce-Alvarez A, Ritter P, Deco G. Estimation of directed effective connectivity from fMRI functional connectivity hints at asymmetries in cortical connectome. PLoS Comput Biol. 2016.

P171

**Getting in the groove: testing a new model-based method for comparing task-evoked versus resting-state activity in fMRI data on music listening**

Matthieu Gilson¹,†, Maria A. G. Witek²,¹, Eric F. Clarke³, Mads Hansen⁴, Mikkel Wallentin⁵, Gustavo Deco¹, Morten L Kringelbach²,⁵,⁶, Peter Vuust²,⁵
¹Center for Brain Cognition, Universitat Pompeu Fabra, Barcelona, Spain;
²Center for Music in the Brain, Aarhus University & Royal Academy of Music, Aarhus/Aalborg, Denmark; ³Faculty of Music, University of Oxford, UK; ⁴Department of Psychology and Behavioural Sciences, Aarhus University, Denmark; ⁵Center of Functionally Integrative Neuroscience, Aarhus University, Denmark; ⁶Department of Psychiatry, University of Oxford, UK

**Correspondence:** Matthieu.Gilson@upf.edu

BMC Neuroscience 2016, 17(Suppl 1):P171

† Equal contribution.

Much of present neuroimaging studies have used fMRI to simply measure the activity in brain regions and computing the functional connectivity (FC) between regions and behaviour. This has provided important insights into the task-evoked activity compared to rest and thus the flow of information between functional regions (e.g., sensory, multimodal integration, memory). Yet, this does not capture all of the complex spatiotemporal patterns of brain activity and in particular not been the underlying effective connectivity. The present study provides evidence for application of a novel method of determining the functional roadmap of effective connectivity (EC), which measures the strengths of dynamic cortical interactions. We use a whole-brain dynamical model that combines fMRI data with anatomical information obtained using diffusion-tensor imaging (DTI) [1]. Our recently developed method [2] provides estimates for the EC as well as the local excitability and stimulus load in a study of groove-based music. The brain is divided into 90 areas and the input noise is shaped by the EC to generate the FC. This model allows us to explore the role of the network parameters in shaping FC: after constraining the model to reproduce resting-state activity, we examine the effect of an arbitrary change in individual inputs and EC strengths on FC. Our method focuses on spatio-temporal FC, meaning covariances of BOLD signals with possible time shifts. The estimated EC and inputs are taken as fingerprints of the brain dynamics. We analyze fMRI data of participants listening to 15 rhythms with three levels of syncopation: Low, Medium and High (five drum-breaks in each level) [3]. In accordance with other studies, we find behaviourally that the Medium level—with more “groove”—elicits the most pleasure and wanting to move. We tune the model to reproduce the FC recorded for each syncopation level, as well as rest. We analyze significant changes for each groove condition as compared to rest. FC for Medium syncopation exhibits a faster shuffling between successive brain patterns of activity, linked to more metastability and corresponding maybe to the subjective experience of higher pleasure. In addition, our model gives a detailed functional neuroanatomy of dynamical changes in the brain networks. Interestingly, Medium syncopation induces changes in excitability in the basal ganglia, such as the pallidum and the caudate nucleus, which may be related to the increased desire for moving. Interestingly, significant changes are also observed in regions of the orbitofrontal and anterior cingulate cortices, which have been strongly implicated in the pleasure network [4]. Overall, our new method has for the first time allowed us to uncover the network and the corresponding effective connectivity of a highly pleasurable state of groove; possibly even revealing the brain topography of eudaimonia, the sense of well-being.

**Acknowledgements:** MG and GD were supported by the FP7 FET ICT Flagship Human Brain Project (604102). GD was supported by the ERC Advanced Grant: DYSTRUCTURE (n. 295129), by the Spanish Research Project SAF2010-16085. MLK was supported by the ERC Consolidator Grant: CAREGIVING (n. 615539). MLK, MW and PV were supported by the Center for Music in the Brain, funded by the Danish National Research Foundation (DNRF117).

**References**

1. Deco G, Jirsa V, McIntosh A. Emerging concepts for the dynamical organization of resting-state activity in the brain. Nat Rev Neurosci. 2011;12:43–56.

2. Gilson M, Moreno-Bote R, Ponce-Alvarez A, Ritter P, Deco G. Estimation of directed effective connectivity from fmri functional connectivity hints at asymmetries in cortical connectome. PLoS Comput Biol. 2016 (in press).

3. Witek MAG, Clarke E, Wallentin M, Kringelbach ML, Vuust P. Syncopation, body-movement and pleasure in Groove music. PLoS One. 2014;9:e94446.

4. Berridge KC, Kringelbach ML. Pleasure systems in the brain. Neuron. 2015;86:646–64.

P172

**Stochastic engine for pathway simulation (STEPS) on massively parallel processors**

Guido Klingbeil¹, Erik De Schutter¹

¹Computational Neuroscience Unit, Okinawa Institute of Science and Technology, 1919-1 Tancha, Onna-son, Kunigami-gun, Okinawa 904-095, Japan

**Correspondence:** Guido Klingbeil - guido.klingbeil@oist.jp

BMC Neuroscience 2016, 17(Suppl 1):P172

STEPS is a stochastic reaction–diffusion simulator. Its emphasis is on accurately simulating signaling pathways in realistic morphologies [1]. It is becoming apparent that larger computational models are demanded to either capture more such morphologies or to simulate more complex systems. As an example, the dendrite calcium burst model presented by Anwar et al. [2] requires approximately 285,000 sub-volumes with 15 diffusing molecular species and 20 reactions per sub-volume. It required several weeks to compute a simulation of 500 ms.

Thus it is desirable to reduce the computational burden. Accelerators such as graphics processing units (GPU) offer unprecedented computing performance and are now common amongst the fastest super computers [3]. This project enables STEPS to benefit from the computational power of GPUs.

GPUs are massively parallel co-processors aggregating thousands of simplified processing cores onto a single chip. They share many characteristics with vector computers and a key challenge is that the processing cores are not independent. Similar to vector computers an operation is applied to a group of data elements rather than to the individual data element. Furthermore, the programmer has to mitigate the memory hierarchy of GPUs. While memory with a high access latency is, in general, abundant, fast memory space shared between threads is small which may limit the size of the reaction system one is able to simulate.

Previous research has shown that we can exploit the computational power of GPUs to accelerate spatially homogenous stochastic simulations by two orders of magnitude while avoiding the limitation imposed to the size of the reaction system to be simulated by the small fast memory space [4].

STEPS implements a spatial version of Gillespie's stochastic simulation algorithm (SSA) computing reaction–diffusion systems on a mesh of tetrahedral sub-volumes [1, 5]. Currently a parallelised multi-processor version of STEPS is under development. Operator splitting techniques allow to separate the reaction of molecules within a sub-volume from the diffusion of molecules between them. This prevents computationally costly rollbacks in case of molecules diffusing between sub-volumes handled by different processors.

We develop a layered hybrid software architecture using both, the classic central processing unit as well as GPUs, integrated into STEPS applying GPU acceleration at the sub-volume level and
integrating them into a coherent spatial simulation using operator splitting.

Our architecture will be a plug-in solution to STEPS not requiring any changes to the interfaces towards the user or other software systems of STEPS itself.

### References

1. Hepburn I, et al.: STEPS: efficient simulation of stochastic reaction-diffusion models in realistic morphologies. BMC Syst Biol. 2012;6:36.

2. Anwar H, et al.: Stochastic calcium mechanisms cause dendritic calcium spike variability. J Neurosci. 2013;33(40):15848-67.

3. TOP500 Supercomputer Site [http://www.top500.org].

4. Klingbeil, et al. Stochastic simulation of chemical reactions with cooperating threads on GPUs (in preparation).

5. Gillespie DT. Exact stochastic simulation of coupled chemical reactions. J Phys Chem. 1977;81(25):2340-61.

## P173

### Toolkit support for complex parallel spatial stochastic reaction-diffusion simulation in STEPS

Weiliang Chen¹, Erik De Schutter¹

Computational Neuroscience Unit, Okinawa Institute of Science and Technology, Okinawa 904-0411, Japan

**Correspondence:** Weiliang Chen - w.chen@oist.jp

BMC Neuroscience 2016, 17(Suppl 1):P173

The studies of large neuronal pathway models with complex morphologies, such as our previous work on the stochastic effects of calcium dynamics in Purkinje cells [1], present a great challenge to currently available spatial stochastic reaction-diffusion simulators, for example, STEPS [2], as the model scales and complexities quickly surpass the capability any serial simulator can achieve.

One possible solution for this challenge is parallelization. At CNS2015 we reported a parallel implementation of STEPS which demonstrated great speedup when simulating a reduced calcium burst model with a tetrahedral cylinder [3], but it is clear that to explore the full potential of our implementation, a larger scale simulation with more complex geometry is required.

Here we extend our work by simulating the reduced calcium burst model with a reconstructed Purkinje dendrite tree branch mesh. Comparing to previous simulations with regular cylinder meshes, the simulation with dendrite tree mesh requires several new support routines from the simulator. First of all, the simple axis based partitioning approach used in the cylinder simulations is no longer a good partitioning solution due to the complex tree structure of the mesh. A sophisticated mesh partitioning and validation solution is therefore necessary for the new simulation. Second, the new simulation demands good regional annotation and data collection support as calcium concentration, the main focus of the simulation, varies both spatially and temporally. The parallel environment further increases the difficulty of such support as the simulation is distributed over a massive number of processors, and each annotated region may not be completely simulated within a single processor. Furthermore, it is necessary to minimize the user interface difference between serial and parallel STEPS solvers and extend the STEPS visualization toolkit to facilitate comparison with results from previous work.

In this poster we demonstrate the general procedure of converting a serial STEPS simulation to its parallel counterpart, using the reduced calcium burst model with complex tree mesh as example, and showcase new supporting toolkits developed for the procedure. We believe that the presented procedure and toolkits will be helpful to STEPS users in their future research.

### References

1. Anwar H, Hepburn I, Nedelescu H, Chen W, De Schutter E. Stochastic calcium mechanisms cause dendritic calcium spike variability. J Neurosci. 2013;33(40):15488-867.

2. Hepburn I, Chen W, Wils S, De Schutter E. STEPS: efficient simulation of stochastic reaction-diffusion models in realistic geometries. BMC Syst Biol. 2012;6:36.

3. Chen W, Hepburn I, De Schutter, E. Implementation of parallel spatial stochastic reaction-diffusion simulation in STEPS. BMC Neurosci. 2015;16(Suppl 1):P54.

## P174

### Modeling the generation and propagation of Purkinje cell dendritic spikes caused by parallel fiber synaptic input

Yunliang Zang¹, Erik De Schutter¹

Computational Neuroscience Unit, Okinawa Institute of Science and Technology Graduate University, Onna-son, Okinawa, Japan

**Correspondence:** Yunliang Zang - yunliang.zang@oist.jp

BMC Neuroscience 2016, 17(Suppl 1):P174

The dendrite of Purkinje cell (PC) has been shown to express different types of voltage gated ion channels. After strong parallel fiber (PF) stimulus, calcium currents can cause dendritic spikes to occur in the spiny dendrite [1]. Different with climbing fiber caused calcium signals that propagate throughout the dendritic tree, PF caused dendritic spikes are local. The elevated calcium concentration due to the local dendritic spike may trigger local synaptic plasticity, possibly playing a significant role in information processing. However, until now, how these dendritic spikes originate and propagate is not well understood. In this work, we built a new PC dendrite model, which can generate local dendritic calcium spikes. The generated spike by model shows similar properties to experimental observations [1], including spike threshold, amplitude and latency. We identify the role of PF type Ca²⁺ current, A type K⁺ current, high threshold K⁺ current (Kv3), calcium activated K⁺ current and axial current on the depolarization and repolarization of the spike. In the model, the required threshold synaptic input to trigger local dendritic spikes decreases with distance from soma, which facilitates the occurrence of spikes in the spiny dendrite by PF synaptic input. This model can also successfully replicate the failure of propagation of PF caused dendritic spikes at the parent branch point. By analyzing the spatial spread of the dendritic spikes and EPSP signal to soma, we identify the relative contribution of active currents and impedance mismatch on the signal decay. Because dendritic spikes can robustly propagate over an entire branchlet in the direction away from the soma, dendritic branchlets may be the basic organization unit for integrating synaptic input [2, 3].

### References

1. Rancz EA, Hausser M: Dendritic calcium spikes are tunable triggers of cannabinoid release and short-term synaptic plasticity in cerebellar Purkinje neurons. J Neurosci. 2006;26(20):5428-37.

2. De Schutter E, Bower JM. Simulated responses of cerebellar Purkinje cells are independent of the dendritic location of granule cell synaptic inputs. PNAS. 1994;91(11):4736-40.

3. Branco T, Hausser M. The single dendritic branch as a fundamental functional unit in the nervous system. Curr Opin Neurobiol. 2010;20(4):494-502.

## P175

### Dendritic morphology determines how dendrites are organized into functional subunits

Sungho Hong¹, Akira Takashima¹, Erik De Schutter¹

¹Computational Neuroscience Unit, Okinawa Institute of Science and Technology Graduate University, Onna-son, Okinawa 904-0495, Japan

**Correspondence:** Sungho Hong - shhong@oist.jp

BMC Neuroscience 2016, 17(Suppl 1):P175

Studies have established that dendrites are not simple cables that deliver synaptic inputs to a spike initiation zone in a neuron but can also perform active transformation, which is termed “dendritic computation” [1]. In particular, it has been claimed that individual dendritic branch should function as a local computational subunit [2] and therefore single neurons (especially pyramidal neurons) can act like two-layer neural networks [3]. Evidence supporting these hypotheses is largely based on existence of active membrane mechanisms in dendrites that give rise to their rich computational capabilities (e.g. [4]) and independent operations [5]. However, dendritic morphology
is also known to play a significant role: For example, spike backpropa-
gation is effectively prevented in the cerebellar Purkinje cells mostly
due to morphology, even when artificial active mechanisms support-
ing propagation are embedded in simulations [6]. Nevertheless, to our
best knowledge, there has been only few studies that quantified how
the real morphological structure can control the functional properties
of dendrites by forming subunits.

Here we address this question by combining a data-driven statistical
analysis and computational modeling approach: First, we simulated
central neurons of diverse morphological types with the passive mem-
branes where localized inputs were injected. Response patterns in the
dendritic membrane were collected as “features” corresponding to
the input sites. Then, our dimensionality reduction/clustering proce-
dure grouped them into clusters, which we call “subunits”. We found
that those subunits usually consist of a few nearby branches in many
neuron types, containing 2.12 ± 0.13 dendritic terminals per subunit
(mean ± SEM), whereas they consist of one or more branchlets in the
cerebellar Purkinje cells (12.9 ± 0.82 terminals). We also found that the
subunits are comparable with other functional properties such as sub-
linear summation of multiple synaptic inputs and spreading of a den-
dritic spike.

**Conclusions** The morphological branching pattern of a neuronal den-
dritic tree determines how dendrites are organized into functional
subunits. This implies that principles governing synaptic integration
and active events, such as dendritic spiking, can widely vary depend-
ing on the morphological type of the neuron.

References

1. London M, Häusser M. Dendritic computation. Annu Rev Neurosci. 2005;28:503–32.

2. Branco T, Häusser M. The single dendritic branch as a fundamental func-
tional unit in the nervous system. Curr Opin Neurobiol. 2010;20:494–502.

3. Poirazi P, Brannon T, Mel BW. Pyramidal neuron as two-layer neural net-
work. Neuron. 2003;37:989–99.

4. Branco T, Clark BA, Häusser M. Dendritic discrimination of temporal input
sequences in cortical neurons. Science. 2010;329:1671–5.

5. Behabadi BF, Mel BW. Mechanisms underlying subunit independence in pyramidal neuron dendrites. Proc Natl Acad Sci USA. 2014;111:498–503.

6. Vetter P, Roth A, Häusser M. Propagation of action potentials in dendrites
depends on dendritic morphology. J Neurophysiol. 2001;85:926–37.

P176

A model of Ca²⁺/calmodulin-dependent protein kinase II activity
in long term depression at Purkinje cells

Criseida Zamora¹, Andrew R. Gallimore¹, Erik De Schutter¹
Computational Neuroscience Unit, Okinawa Institute of Science
and Technology Graduate University, Okinawa 904-0895, Japan

**Correspondence:** Won Hee Lee - criseida.chimal@oist.jp
**BMC Neuroscience 2016, 17(Suppl 1):P176**

Cerebellar long-term depression (LTD) is a form of synaptic plasticity involved in motor learning. It is characterized as a robust and persistent decrease in the synaptic transmission between parallel fibers (PF) and Purkinje cells (PC), which is expressed as a reduction in the number of synaptic AMPA receptors (AMPAR). LTD signaling network includes a PKC-ERK-cPLA₂ positive feedback loop and mechanism of AMPAR trafficking. Previous studies suggest that Ca²⁺/calmodulin-dependent protein kinase II (CaMKII) is required for the LTD induction [1]. However, the molecular mechanism of how CaMKII contributes to LTD is not fully understood. Noise in the signaling networks plays an important role in cellular processes. LTD models including the CaMKII pathway have been developed [2], but they have not included the intrinsic stochasticity of molecular interactions.

Our lab recently developed a stochastic model of the LTD signaling net-
work including a PKC-ERK-cPLA₂ feedback loop and AMPAR trafficking
[3]. In this work, we have extended the model by adding the molecular
network regulating CaMKII activity, which is known to influence LTD.
This new model was solved stochastically by STEPS (STochastic Engine
for Pathway Simulation) to simulate the influence of noise in the LTD

signaling network [4]. Some of the most important new components
of this network include phosphatase 2A (PP2A), phosphodiesterase 1
(PDE1), cGMP/protein kinase G (PKG) and nitric oxide (NO) pathway.
Through stochastic modeling we showed that the requirement of
CaMKII activity for LTD induction is controlled by its indirect inhibi-
tion of PP2A activity, with PP2A markedly suppressing the activation
of LTD when CaMKII activity is decreased. The impairment of LTD could
be rescued by the additional PDE1 reduction when CaMKII is reduced.
In addition, the cGMP/PKG pathway supports LTD through its activa-
tion by NO. These results are congruent with previous studies of CaM-
KII activity [2] and make our stochastic model a potential tool to study
the effects of CaMKII, phosphatases and phosphodiesterases in LTD
molecular network.

**References**

1. Hansel C, de Jeu M, Belmeguenai A, Houtman SH, Buitendijk GH, Andreev D, De Zeeuw CI, Elgersma Y. αCaMKII is essential for cerebellar LTD and motor learning. Neuron. 2006;51:835–43.

2. Kawaguchi SY, Hirano T. Gating of long-term depression by Ca²⁺/calmo-
dulin-dependent protein kinase II through enhanced cGMP signalling in
cerebellar Purkinje cells. J Physiol. 2013;591(7):1707–30.

3. Antunes G, De Schutter E. A stochastic signaling network mediates the probabilistic induction of cerebellar long-term depression. J Neurosci. 2012;32(27):9288–300.

4. Hepburn I, Chen W, Wils S, De Schutter E. STEPS: efficient simulation of stochastic reaction–diffusion models in realistic morphologies. BMC Syst Biol. 2012;6:36.

P177

Reward-modulated learning of population-encoded vectors
for insect-like navigation in embodied agents

Dennis Goldschmidt¹, Poramate Manoonpong², Sakyasingha Dasgupta³,⁴
¹Champalimaud Neuroscience Programme, Champalmaud Center
for the Unknown, Lisbon, Portugal; ²Center of Biorobotics, Mærsk
Mc-Kinney Møller Institute, University of Southern Denmark, Odense,
Denmark; ³Riken Brain Science Institute, 2-1 Hirosawa, Wako, Saitama,
Japan; ⁴IBM, IBM Research - Tokyo, Tokyo, 103-8510, Japan

**Correspondence:** Dennis Goldschmidt - dennis.goldschmidt@neuro.fchampalmaud.org

BMC Neuroscience 2016, 17(Suppl 1):P177

Many insects exhibit robust and efficient visual-based navigation in
complex environments [1]. Specifically, behavioral studies on ants and
bees showed that they are guided by orientation vectors based on a
process called path integration. This process allows them to estimate
their current location by integrating cues from odometry and a sun-
based compass. While it is mainly applied to return back to the nest, it
also guides learning of so-called vector memories for subsequent for-
aging [2, 3]. Vector memories can be anchored globally to the nest or
locally to landmarks. Recent neurophysiological studies revealed that
the central complex, an insect neuropil, contains neural representa-
tions of compass [4] and odometric cues [5]. However, it is still unclear,
how these representations are involved in path integration and vec-
tor memories, and how they produce goal-directed navigation. Com-
putational modeling has been powerful in testing hypotheses about
the underlying neural substrates and their generated behavior, and to
predict further experimental data. Previous models [6, 7] sufficiently
produced insect-like vector navigation, but they neglected biologi-
cally plausible explanations about underlying neural mechanisms that
could generate this behavior.

We present here a novel computational model of neural mechanisms
in closed-loop control for vector navigation in embodied agents. It
consists of a path integration mechanism, reward-modulated learning
of global and local vectors, random search, and action selection. The
path integration mechanism computes a vectorial representation of the
agent's current location. The vector is encoded in the activity pattern of
circular arrays, where the angle is population-coded and the distance is
rate-coded. We apply a reward-modulated learning rule for global and
local vector memories, which associates the local food reward with the
path integration state. A motor output is computed based on the com-
bination of vector memories and random exploration. We show that the
modeled neural mechanisms enable robust homing and localization in
a simulated agent, even in the presence of external sensory noise. The
proposed learning rules produce goal-directed navigation and route
formation under realistic conditions. This provides an explanation for,
how view-based navigational strategies are guided by path integration.
As such, the model is the first to link behavioral observations to their
possible underlying neural substrates in insect vector navigation.

**Acknowledgements:** We thank Florentin Wörgötter at the Department of Computational Neuroscience in Göttingen, where most of this work was conducted. SD acknowledges funding from the RIKEN Brain Science Institute.

**References**

1. Wehner R. Desert ant navigation: how miniature brains solve complex tasks. J Comp Physiol A. 2003;189(8):579-88.

2. Collett M, Collett TS, Bisch S, Wehner R. Local and global vectors in desert ant navigation. Nature. 1998;394(6690):269-72.

3. Collett TS, Collett M. Route-segment odometry and its interactions with global path-integration. J Comp Physiol A. 2015;201(6):617-30.

4. Seelig JD, Jayaraman V. Neural dynamics for landmark orientation and angular path integration. Nature. 2015;521(7551):186-91.

5. Martin JP, Guo P, Mu L, Harley CM, Ritzmann RE. Central-complex control of movement in the freely walking cockroach. Curr Biol. 2015;25(21):2795-803.

6. Cruse H, Wehner R. No need for a cognitive map: decentralized memory for insect navigation. PLoS Comput Biol. 2011;7(3):e1002009.

7. Kubie JL, Fenton AA. Heading-vector navigation based on head-direction cells and path integration. Hippocampus. 2009;19(5):456-79.

P178

**Data-driven neural models part II: connectivity patterns of human seizures**

Philippa J. Karoly¹, Dean R. Freestone¹,², Daniel Soundry², Levin Kuhlmann³, Liam Paninski², Mark Cook¹

¹Department of Medicine, The University of Melbourne, Parkville VIC, 3010, Australia; ²Department of Statistics, Columbia University, New York, NY, USA; ³Swinburne University of Technology, Hawthorn, VIC 3122, Australia

**Correspondence:** Philippa J. Karoly - pkaroly@student.unimelb.edu.au

BMC Neuroscience 2016, 17(Suppl 1):P178

Here we present a model-based estimation framework for electrocor-
ticography (ECOG) data that provides insight into mechanisms of sei-
zures; and can be used as a clinical tool to monitor and design new
treatment strategies on a patient-specific basis.

Seizures are brief periods of abnormal, hypersynchronous neural fir-
ing that spreads across multiple cortical regions. People with epi-
lepsy experience recurrent seizures, which are often untreatable and
of unknown cause. The data-driven estimation framework, shown in
Fig. 91, describes dynamic neural connectivity patterns during patient
seizures. Data were obtained from a clinical trial for an implantable
seizure warning device [1], which captured thousands of seizures. We
estimated mean membrane potentials and connectivity strengths
between excitatory, inhibitory and pyramidal populations using a non-
linear, assumed density filter for the neural mass equations [2].

Estimated parameters provide insights into the mechanisms of the seizure, which are not apparent from ECOG alone. For instance, Panel E shows the seizure is preceded by a focal decrease in inhibition compared to the surrounding channels, with widespread disinhibition during the seizure. Joint state and parameter estimation was repeated for every seizure, and the results showed consistent, stereotypical effective connectivity patterns that differed between short (<20 s) and long seizures. This is an important finding, as understanding the regulatory factors implicated in stopping seizures can guide new pharmaceutical treatments and electrical counter-stimulation strategies. The successful application of the neural mass model to study epileptic seizures supports the use of data-driven estimation for the clinical management of epilepsy.

<figure><img src="image_99.png" /><figcaption>**Fig. 91** Example estimation of a seizure recording. **A** Sixteen channel electrocortography (ECOG) of seizure (red lines indicate start and end points). **B** The ECOG channels are modelled as cortical regions, each with three coupled populations. **C–G** Estimation results of coupling strength (proportional to color) between neural populations for 16 cortical regions (vertical axis), over the time span of the seizure (horizontal axis)</figcaption></figure>

**References**

1. Cook MJ, et al. Prediction of seizure likelihood with a long-term, implanted seizure advisory system in patients with drug-resistant epi-lepsy: a first-in-man study. Lancet Neurol. 2013;12(6):563-71.

2. Freestone et al.: Data-driven neural models part I CNS 2016 abstract submission.

P179

**Data-driven neural models part I: state and parameter estimation**

Dean R. Freestone¹, Philippa J. Karoly¹,², Daniel Soundry³, Levin Kuhlmann⁴, Mark Cook¹

¹Department of Medicine, The University of Melbourne, Parkville VIC, 3010, Australia; ²Department of Electrical and Electronic Engineering, The University of Melbourne, Parkville VIC, 3010, Australia; ³Department of Statistics, Columbia University, New York, NY, USA; ⁴Swinburne University of Technology, Hawthorn, VIC 3122, Australia

**Correspondence:** Dean R. Freestone - deanrf@unimelb.edu.au

BMC Neuroscience 2016, 17(Suppl 1):P179

This work describes a novel algorithm for inferring neural activity and effective cortical connectivity from neuroimaging data. The ability to infer cortical network structure from data is an important step towards understanding and treating neurological disorders, such as epilepsy. However, statistical measures for correlation in neuroimaging data are ambiguous and bear little or no relation to physiology. On the other hand, estimating physiologically realistic connectivity is highly challenging due to the complex, non-linear dynamics of the brain. The algorithm we present overcomes this challenge by providing an exact solution to non-linear inversion for a class of biologically inspired neural network models.
The presented algorithm performs joint state and parameter estimation for a class of neural model that represents interacting cortical regions as coupled nodes (shown in Fig. 92). The states of the model represent mean cortical activity [population membrane potentials, v(t)], and the parameters are the effective connectivity (synaptic gain kernels, αᵢⱼ). The output voltage, vₙ(t), represents the electrophysiology recording, which is inverted using a novel formulation of the Kalman filter equations for neural models [1]. The novelty of this method is the derivation of an exact solution to the integral over the distribution of hidden model states conditioned on previous data. We provide results showing that the new algorithm demonstrates higher estimation accuracy and greater computational tractability than existing inference methods for neural models. We also show example estimation results from an electrical recording of a human seizure (shown in Fig. 92). This new method for data-driven inference represents an important contribution to online diagnostic applications, in particular for the treatment of epilepsy [2].
<figure><img src="image_100.png" /></figure>

**Fig. 92** Data-driven model estimation. **A** The basic unit of a neural model is described by the mean membrane potential, $v_n(t)$, of a neural ensemble and synaptic inputs. **B, C** Pre-synaptic firing rates are convolved with the excitatory/inhibitory kernel to generate membrane potential fluctuations. **D** The resulting membrane potential is converted to an output firing rate via a sigmoidal transform. **E** Electrical recording of a seizure. **F** Estimated gain parameters during seizure

## References

1. Freestone DR, et al. Estimation of effective connectivity via data-driven neural modeling. Front Neurosci. 2014;8:383

2. Karoly, et al. Data-driven neural models part II: connectivity patterns of human seizures. CNS 2016 abstract.

## P180

### Spectral and spatial information processing in human auditory streaming

Jaejin Lee¹, Yonatan I. Fishman², Yale E. Cohen¹

¹Department of Otorhinolaryngology - Head and Neck Surgery, University of Pennsylvania, Philadelphia, PA 19104, USA; ²Department of Neurology, Albert Einstein College of Medicine, Bronx, NY 10461, USA

**Correspondence:** Jaejin Lee - jaejin@mail.med.upenn.edu

BMC Neuroscience 2016, 17(Suppl 1):P180

The purpose of auditory system is to transform acoustic stimuli from the external environment to sound perception. To achieve this goal, the auditory system needs to analyze a mixture of stimuli that originate from independent sources and distinguish individual sound sources in the auditory scene. It is believed that the auditory system groups and segregates auditory stimuli based on their regularities, but the neural basis of how regularities relate to sound perception is not well known. The ventral pathway in the brain is involved in auditory perception whereas the dorsal pathway is involved in spatial processing and audiometer processing. We are interested in how the spatial information is represented in the ventral pathway during perceptual auditory streaming tasks that use spatial information. We first developed a novel task based on [1] in which human listeners can segregate streams using spectral or spatial information and detect the deviant tone. An array of 13 free field speakers with different spatial distributions were used to play the stimuli. The frequency difference between streams and the spatial separation were varied to explore how the spectral and spatial information interplay in the auditory streaming task. We also manipulated other acoustic features of the stimuli to understand how different acoustic cues can affect the auditory streaming performance. We found that the ability to segregate the streams is vastly improved when there is spatial information available in addition to spectral information. Also, we further analyzed the behavioral data to get psychophysical kernels and fit the data to variants of sequential sampling models related to the drift diffusion model(DDM) [2] to quantify the effects of sequence coherence on the decision making process.

## References

1. Sussman E, Steinschneider M. Attention effects on auditory scene analysis in children. Neuropsychologia. 2009;47:771–85.

2. Liu A, Tsunada J, Gold J, Cohen Y. Temporal integration of auditory information is invariant to temporal grouping cues. eNeuro. 2015;2(2):e0077-14.

## P181

### A tuning curve for the global effects of local perturbations in neural activity: mapping the systems-level susceptibility of the brain

Leonardo L. Gollo¹, James A. Roberts¹, Luca Cocchi¹

¹Systems Neuroscience Group, QIMR Berghofer Medical Research Institute, Herston, QLD 4006, Australia

**Correspondence:** Leonardo L. Gollo - leonardo.l.gollo@gmail.com

BMC Neuroscience 2016, 17(Suppl 1):P182

The activity of the human brain in a state of rest exhibits a defined pattern of functional connectivity, and a small set of functional networks, which comprise regions that are highly correlated and are mostly distinctive from one another [1]. However, despite recent efforts [2, 3], the effects of local perturbations into endogenous whole brain dynamics are not yet clearly understood [4]. To gain insights into the global effects of a focal perturbation, we simulate the human brain dynamics using a weighted high-resolution connectome of 513 cortical regions [5]. The cortical dynamics is modelled by a canonical oscillatory model, introducing heterogeneous dynamics between cortical regions as a function of the anatomical nodal strength (sum of weights). Such heterogeneity leads to a hierarchy of time scales of cortical regions recapitulating the known anatomical hierarchy, with peripheral regions having fast time scales and core regions with slow time scales [6]. Results showed that nodal diversity is not just a crucial element to improve the model's performance [7, 8], but also to reproduce the experimental data of variations in functional connectivity following local inhibitory transcranial magnetic stimulation (TMS). We find a large variation in the overall effect of functional connectivity following local stimulation. Specifically, the inhibition of hub nodes causes increased anti-correlated activity, whereas inhibition of peripheral nodes caused increased correlated activity with the rest of the brain. The intensities of the variations in functional connectivity with respect to baseline were also highly variable and stronger for intermediary nodes that were not hubs or peripheral regions. Moreover, depending on the weights of the cortical regions, changes in functional connectivity form a tuning curve (Fig. 93). Overall, our findings suggest a key role of local temporal dynamics to explain the widespread effects of focal perturbations in neural activity.

## References

1. Power JD, Cohen AL, Nelson SM, Wig GS, Barnes KA, Church JA, Vogel AC, Laumann TO, Miezin FM, Schlaggar BL, Petersen SE. Functional network organization of the human brain. Neuron. 2011;72:665–78.

<figure><img src="image_101.png" /></figure>

**Fig. 93** Changes in functional connectivity with respect to baseline after inhibitory stimulation as a function of cortical weight of the structural connectivity matrix. Red line: mean uniform bins curve smoothed; dashed line: mean weight
2. Cocchi L, Sale MV, Lord A, Zalesky A, Breakspear M, Mattingley JB. Dissociable effects of local inhibitory and excitatory theta-burst stimulation on large-scale brain dynamics. J Neurophysiol. 2015;113:3375–85.

3. Kunze, T, Hunold A, Haueisen J, Jirsa V, Spiegler A. Transcranial direct current stimulation changes resting state functional connectivity: a large-scale brain network modeling study. Neurolmage. doi:10.1016/j.neuroimage.2016.02.015.

4. Sale MV, Mattingley JB, Zalesky A, Cocchi L. Imaging human brain networks to improve the clinical efficacy of non-invasive brain stimulation. Neurosci Biobehav Rev. 2015;57:187–98.

5. Roberts JA, Perry A, Lord AR, Roberts G, Mitchell PB, Smith RE, Calamante F, Breakspear M. The contribution of geometry to the human connectome. Neurolimage. 2016;124:379–93.

6. Gollo LL, Zalesky A, Hutchison RM, van den Heuvel M, Breakspear M. Dwelling quietly in the rich club: Brain network determinants of slow cortical fluctuations. Philos Trans R Soc B Biol Sci. 2015;370:20140165.

7. Mejias JF, Longtin A. Optimal heterogeneity for coding in spiking neural networks. Phys Rev Lett. 2012;108:228102.

8. Gollo LL, Copelli M, Roberts JA. Diversity improves performance in excitable networks. arXiv preprint arXiv:1507.05249. 2015 Jul 19.

P182

Diverse homeostatic responses to visual deprivation mediated by neural ensembles

Yann Sweeney¹, Claudia Clopath¹

¹Department of Bioengineering, Imperial College London, UK

**Correspondence:** Yann Sweeney - y.sweeney@imperial.ac.uk

BMC Neuroscience 2016, 17(Suppl 1):P182

Visual deprivation paradigms provide crucial insight into the homeostatic response in visual cortex. We explore how neurons within functional ensembles may exhibit correlated homeostatic responses to visual deprivation, and how the source of common inputs to these ensembles determine the extent of their homeostatic recovery. We hypothesise that common inputs from non-visual stimuli are responsible for driving recovery from visual deprivation.

We simulate development during spontaneous and evoked activity in a recurrent network model of visual cortex in which Hebbian and homeostatic synaptic plasticity is implemented. This leads to the emergence of highly interconnected ensembles of neurons driven by either common visual or common non-visual inputs. When we then deprive the developed network of visual input, the homeostatic response is a strengthening of activity within ensembles which share common non-visual inputs. A broad reduction in inhibition across the network is also observed. Interestingly, the magnitude of the homeostatic response depends on the size of these ensembles, with larger ensembles more likely to fully recover from visual deprivation. Our results demonstrate the importance of investigating functional plasticity of ensembles triggered by sensory deprivation paradigms.

**Acknowledgements:** This research was supported by the Engineering and Physical Sciences Research Council (EPSRC), the Leverhulme Trust and Google Faculty Award.

P183

Opto-EEG: a novel method for investigating functional connectome in mouse brain based on optogenetics and high density electroencephalography

Sohyun Lee¹,³, Woo-Sung Jung¹,², Jee Hyun Choi³

¹Department of Physics, POSTECH, Pohang, 37673, South Korea;

²Department of Industrial and Management Engineering, POSTECH, Pohang, 37673, South Korea; ³Center for Neuroscience, KIST, Seoul, 02792, South Korea

**Correspondence:** Jee Hyun Choi - jeechoi@kist.re.kr

BMC Neuroscience 2016, 17(Suppl 1):P183

Connectome, comprehensive structural description of the network of elements and connections forming the brain [1], is fundamental for understanding the brain functions. Recent advances in optical imaging techniques allow us to be feasible to structural connectivity. But differently from structural connectivity, the functional connectivity is

altered by condition such as brain states, input types and pathological conditions. To construct functional connectome, the techniques to map individual functional circuit and control specific neuronal activity have been needed. However, the current functional brain mapping techniques have limitations to obtain the map of the functionally correlated brain activity in freely moving mouse model. Here, we introduce novel functional brain mapping technique for mouse model by high density electroencephalography [2] under optogenetic stimulus, which we referred as opto-EEG. Opto-EEG tool enables us to investigate the functionally connected neuronal circuit with high spatial and temporal resolution. We stimulated ventral posterioromedial thalamic nucleus (VPM) with various frequencies for verifying different frequency dependency of functional connectome. Stimulation of VPM induced sequential activations of ipsilateral somatosensory cortex (S1) followed by ipsilateral motor cortex (M1), contralateral M1 and contralateral S1. The power based analysis result showed information flow between S1 and M1 was maximized under beta frequency stimulus. On the other hand, latency-based result showed minimized interhemispheric transfer latency under gamma band (Fig. 94). This example indicates that opto-EEG makes it possible to be used to characterize the functional connectivity under temporally precise control of specific neuronal circuits, provide new insights into brain exploration capabilities of functional connectome, and be applied to discover neuromodulation method for treatment of disease or pathologies.

References

1. Sporns O, Tononi G, Kotter R. The human connectome: A structural description of the human brain. PLoS Comput Biol. 2005;1(4):e42.

2. Lee M, Kim D, Shin HS, Sung HG, Choi JH. High-density EEG recordings of the freely moving mice using polyimide-based microelectrode. J Vis Exp. 2011;(47).

P184

Biphasic responses of frontal gamma network to repetitive sleep deprivation during REM sleep

Bowon Kim¹,², Youngsoo Kim³, Eunjin Hwang¹, Jee Hyun Choi¹,²

¹Center for neuroscience, Korea Institute of Science and Technology, Seoul, South Korea; ²Department of Neuroscience, University of Science and Technology, Daejon, South Korea; ³Department of Psychiatry, VA Boston Healthcare System & Harvard Medical School, Brockton, MA, USA

**Correspondence:** Jee Hyun Choi - jeechoi@kist.re.kr

BMC Neuroscience 2016, 17(Suppl 1):P184

Prefrontal cortex has been known to be less activated [1] and decoupled from the other cortical area in REM sleep [2]. In our previous study of chronic sleep deprivation (SD) in mice model, we observed that the 5 successive days of SD (SD 1–5, 18 h sleep deprivation in each day)

<figure><img src="image_102.png" /><figcaption>**Fig. 94** Propagation patterns of optical stimulation at each frequency in thalamocotical circuit. Beta frequency stimulus propagated S1–M1 strongly, but gamma frequency case, contralateral propagation is dominant. *Blue bars* indicate optical stimulus in left VPM</figcaption></figure>
<figure><img src="image_103.png" /><figcaption>**Fig. 95** The pairs with statistically significantly increased (red) or decreased (blue) PSI of gamma oscillations (Student t test, p < 0.05). Only the pairs from the prefrontal cortex were depicted here. SD and R stand for sleep deprivation and recovery days, respectively</figcaption></figure>

induced a monotonic increase of the prefrontal gamma oscillation (30–40 Hz) in REM sleep as the sleep pressure increased. However, the functional role of this increased gamma oscillation was not answered. Here, we investigated the functionality of the increased prefrontal gamma in sleep deprived nights by calculating the connectivity between the prefrontal cortex and the other cortical regions [3]. Phase synchrony index (PSI) was employed to minimize the volume conduction in high density EEG microarray. In the first day of the sleep deprivation (SD 1), we observed statistically significant increases in gamma connectivity within the bilateral prefrontal regions and between prefrontal and ipsilateral somatosensory cortex. However, as the sleep deprivation continued, an opposite response of prefrontal-somatosensory gamma connectivity was observed in a way that the PSI between these two areas become insignificant in SD 3 and statistically significantly decreased in the SD 5, which remained even after the 3rd day of recovery after the sleep deprivation (R 3). The area of decreased gamma connectivity became broader as well. On the other hand, the intracortical connectivity within prefrontal connectivity remained elevated throughout the sleep deprivation and recovery days. This result implies that the increased prefrontal gamma oscillation due to the homeostatic response of REM sleep does not participate in the information transfer from prefrontal to the other cortical area (Fig. 95).

**Acknowledgements:** This research was supported by the Korean National Research Council of Science and Technology (No. CRC-15-04-KIST).

## References

1. Maquet P, et al. Functional neuroanatomy of human rapid-eye-movement sleep and dreaming. Nature. 1996;383(6596):163-6.

2. Castro S, et al. Coherent neocortical 40-Hz oscillations are not present during REM sleep. Eur J Neurosci. 2013;37(8):1330-9.

3. Hwang E, McNally JM, Choi JH. Reduction in cortical gamma synchrony during depolarized state of slow wave activity in mice. Front Syst Neurosci. 2013;7.

## P185

### Brain-state correlate and cortical connectivity for frontal gamma oscillations in top-down fashion assessed by auditory steady-state response

Younginha Jung<sup>1,2</sup>, Eunjin Hwang<sup>1</sup>, Yoon-Kyu Song<sup>2</sup>, Jee Hyun Choi<sup>1,3</sup>
<sup>1</sup>Center for Neuroscience, Korea Institute of Science and Technology, Seoul 02792, Korea; <sup>2</sup>Program in Nano Science and Technology, Seoul National University, Seoul 08826, Korea; <sup>3</sup>Department of Neuroscience, University of Science and Technology, Daejon 34113, Korea

**Correspondence:** Jee Hyun Choi - jeechoi@kist.re.kr

BMC Neuroscience 2016, 17(Suppl 1):P185

Cortical gamma rhythm, particularly in the frequency range of 30–50 Hz, has received intensive attention as neural correlates of cognitive process [1]. On the other hand, diminished cognitive flexibility, one of the typical symptoms in psychiatric disorders, is closely associated with disturbances in neural oscillations, specifically gamma band [2]. To quantify gamma-band oscillation, auditory steady-state

response (ASSR) evoked by repetitive auditory stimulus given at a rate of 40 Hz has been used as a prominent approach which reflects neural efficiency for maintaining gamma oscillation [3]. Despite its diagnostic advantages, there is less discussion whether ASSRs are modulated by endogenous top-down effect. The present research attempts to investigate top-down influences on ASSR by analyzing in vivo mouse data. Experimental data in this study were obtained from 38-channel mouse epidural electroencephalogram during auditory steady-state stimulus. Interestingly, there were two distinctive topographic maps of EEG spectral power and the notable difference between topographies was the presence or absence of frontal responses. By comparing topographic results, we hypothesized that frontal ASSRs reflect top-down functioning. The analytic approaches taken in this work are based on brain-state alteration and regional connectivity. The first research question in the data analysis is that frontal ASSRs switch states of arousal via top-down control. Video-based behavior analysis was adapted to classify arousal states into wakefulness and drowsiness and the proportion of arousal behavior in two topographies were determined. In addition, comparison of delta spectral power for topographic patterns could explain frontal engaged sleep state modulation. The second study question is about early stages of auditory ascending pathway in each topographic pattern. Magnitude and latency of auditory evoked potentials and gamma spectral power were analyzed in inferior colliculus and primary auditory cortex. The third question in data analysis is functional connectivity among cortical regions and, in detail, phase-locking value and directed phase lag index were calculated in frontotemporal and inter-frontal coupling. Overall, the current results show that frontal lobe contributes substantially to ASSR and imply that it is important to consider the frontal involvement in auditory steady-state signal processing. Together, these methodologies could provide important insights to clinical research by demonstrating top-down modulation. Investigating gamma oscillatory activity across cortical regions potentially provides deeper understanding for dysfunction in neurological disorders and furthermore gives clues to determine neural circuit disruption.

## References

1. Fries P, Reynolds JH, Rorie AE, Desimone R: Modulation of oscillatory neuronal synchronization by selective visual attention. Science. 2001;291(5508):1560-3.

2. Kwon JS, O'Donnell BF, Wallenstein GV, Greene RW, Hirayasu Y, Nestor PG, Hasselmo ME, Potts GF, Shenton ME, McCarley RW. Gamma frequency-range abnormalities to auditory stimulation in schizophrenia. Arch Gen Psychiatry. 1999;56(11):1001-5.

3. Picton TW, John MS, Dimitrijevic A, Purcell D. Human auditory steady-state responses. Int J Audiol. 2003;42(4):177-219.

## P186

### Neural field model of localized orientation selective activation in V1

James Rankin<sup>1</sup>, Frédéric Chavane<sup>2</sup>

<sup>1</sup>Center for Neural Science, New York University, 4 Washington Place, 10003 New York, NY, USA; <sup>2</sup>Institut de Neurosciences de la Timone (INT), CNRS & Aix-Marseille University, 27 Boulevard Jean Moulin, 13005 Marseille, France

**Correspondence:** James Rankin - james.rankin@nyu.edu

BMC Neuroscience 2016, 17(Suppl 1):P186

Voltage imaging experiments in primary visual cortex [1] have shown that local, oriented visual stimuli elicit stable orientation-selective activation within the stimulus retinotopic footprint. The cortical activation dynamically extends far beyond the retinotopic footprint, but the peripheral spread stays non-selective—a surprising finding given a number of studies showing the orientation specificity of long-range connections, e.g. [2]. We study the dynamics of these input-driven localized states in a planar neural field model building on an earlier theoretical study using radially symmetric inputs [3]. Here we use a new anatomically-motivated connectivity profile and extend the model to multiple sub-populations encoding orientation. For canonical choices of connectivity profile (such as a radial difference of
Gaussians), localized orientation selectivity arises. However, unlike the experimental observations, the selective activation is unstable during transient dynamics. In the new connectivity profile defined in our study, the range of excitatory and inhibitory connections and the orientation selectivity of those connections are controlled with separate parameters. We demonstrate how peaks in the number of excitatory connections at each hyper-column distance [4] are crucial in stabilizing the transient, local orientation selective activation. If these peaks in excitation are non-realistically exaggerated, we demonstrate that spurious selectivity (not matching preference map) could arise in the peripheral spread. Furthermore, although orientation selectivity of connections increases accuracy of the selective activation within the retinotopic footprint, it can also lead to orientation selective activation in the periphery. Our parameter exploration shows that with a balance in the sharpness of peaks in long-range excitatory connections and the selectivity of these connections, we can capture the correct localized selective activation, the non-selective peripheral spread and the stable transient dynamics.

**Conclusions** Typical choices of connectivity profile in planar models of cortex fail to produce important aspects of the observed cortical spread of activation. We developed a more realistic connectivity profile inspired by anatomical data that, used in conjunction with our planar multiple sub-population model, captures all key spatial and temporal aspects of the cortical spread of activation. For the first time, our study shows that the unexpected experimental findings of [1] can be accounted for with a realistic balance between the sharpness of peaks in long-range excitation and orientation selectivity of connections.

## References

1. Chavane F, Sharon D, Jancke D, Marre O, Frégnac Y, Grinvald A. Lateral spread of orientation selectivity in V1 is controlled by intracortical cooperation. Front Syst Neurosci. 2011;5:4

2. Bosking WH, Zhang Y, Schofield B, Fitzpatrick D. Orientation selectivity and the arrangement of horizontal connections in tree shrew striate cortex. J Neurosci. 1997;17:2112–27.

3. Rankin J, Avitabile D, Baladron J, Faye G, Lloyd DJ. Continuation of localized coherent structures in nonlocal neural field equations. SIAM J Sci Comput. 2014;36:870–93.

4. Buzás P, Eysel U, Adorján P, Kisvárday Z. Axonal topography of cortical basket cells in relation to orientation, direction, and ocular dominance maps. J Comp Neurol. 2001;437:259–85.

## P187

### An oscillatory network model of Head direction and Grid cells using locomotor inputs

Karthik Soman¹, Vignesh Muralidharan¹, V. Srinivasa Chakravarthy¹
¹Department of Biotechnology, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India

**Correspondence:** V. Srinivasa Chakravarthy - schakra@iitm.ac.in

BMC Neuroscience 2016, 17(Suppl 1):P187

The model (Fig. 96A) takes proprioceptive inputs coming from the joint angles of the two limbs. Locomotor rhythms are modeled as two sinusoidal oscillators whose amplitudes are modulated by the curvature and the speed of simulated animal. These inputs are gated using a leaky integrate and fire (LIF) neuron that spikes at a fixed frequency so that the curvature and speed information from respective limb oscillations are extracted out and given to two oscillatory neural networks separately. The oscillatory neural networks are modeled as Kuramoto networks in which the phase is modulated by the curvature of the path traversed by the simulated animal. Synchrony between the two clusters of oscillators is quantified in terms of phase coherence and phase difference. The synchrony parameters are further used to train a one dimensional self organizing map (SOM), whose neurons display head direction-tuned responses. Each HD response is transformed to a cosine response which is further given to path integration (PI) layer. PI layer is again a network of Kuramoto oscillators whose phase is integrated as a function of HD responses. PCA is done on the PI values. The top few principal components (PC) corresponding to the largest Eigen values are rearranged in increasing order and taken as the weight

connections from the PI layer to an outer 1-D layer of neurons. While remapping the neural response of the third and fourth PCs, square grid fields were observed while the fifth and sixth PCs gave We present a model of head direction (HD) and grid cells formed purely from idiothetic (locomotor) inputs. Grid cells are a class of spatial cells located in the medial Entorhinal Cortex which is assumed to perform path integration and characterized by its unique hexagonal firing fields [1]. Empirically it is proven that HD cells, another class of spatial cells which encode the heading direction of an animal, form the major input to the grid cell. Existing computational models of grid cells make artificial assumptions like existence of HD cells with a phase differences that are integral multiples of 60° [2]. The aim of the study is to model grid cell firing without imposing these special constraints. Hexagonal grid fields (Fig. 96B). Further analysis showed that PCs were sinusoidal vectors. Investigation of the correlation values between the adjacent rows of the covariance matrix of PI pointed out its similarity to circulant matrices. This was in corroboration with the circulant matrix theorem which states that a circulant matrix of any size gives rise to sinusoidal Eigen vectors. Hence this is a generalized model which provides a theoretical basis for the formation of both hexagonal and non hexagonal grid fields and possibly other spatial cells which are actually projections of the PI values onto sinusoidal orthonormal basis vectors.

## References

1. Hafting T, Fyhn M, Molden S, Moser M-B, Moser El. Microstructure of a spatial map in the entorhinal cortex. Nature. 2005;436(7052):801–6.

2. Burgess N, Barry C, O'Keefe J. An oscillatory interference model of grid cell firing. Hippocampus. 2007;17(9):801–12.

<figure><img src="image_104.png" /></figure>
P188

**A computational model of hippocampus inspired by the
functional architecture of basal ganglia**

Karthik Soman¹*, Vignesh Muralidharan¹, V. Srinivasa Chakravarthy¹
¹Department of Biotechnology, Indian Institute of Technology Madras,
Chennai, Tamil Nadu- 600036, India

**Correspondence:** V. Srinivasa Chakravarthy - schakra@iitm.ac.in
**BMC Neuroscience** 2016, **17**(Suppl 1):P188

* Both authors have equal contribution.

We present a networkmodel of hippocampus (HC) inspired by the
functional architecture of the basal ganglia (BG). The model describes
the role of hippocampus in spatial navigation and is cast in reinforce-
ment learning (RL) framework (Fig. 97A). There is a corpus of litera-
ture which states that hippocampus is a key player in spatial learning
because of the enriched sensory information that arrives at the por-
tals of HC, the entorhinal cortex (EC), from the sensory cortical areas
[1]. The model simulates the Morris water maze task wherein a virtual
agent navigates inside a circular pool to find an invisible platform
using the spatial context from the environment.

In order to model the ability of HC to learn spatial context, we simu-
late a circular pool surrounded by six distinguishable poles of equal
heights. As the agent/animal navigates, the size of retinal image
of each pole varies with distance between the agent and the pole.
Reward is given to the agent when it reaches the platform. The
abstract form of the visual input is given to EC which has afferent and
efferent projections from ventral tegmental area (VTA), one of the
dopamine centers in the mid brain. Temporal difference (TD) error
generated from VTA is used to update the synaptic weights for value
computation of the sensory input in EC. Additionally an action vector
defining the direction of the agent's next step also forms a feedback
input to the EC. We describe the functional anatomy of HC in terms
of two pathways: a direct pathway between EC and CA1 and an indi-
rect pathway between EC and CA1 via dentate gyrus (DG), and CA3.
A quantity known as Value difference, that represents afferent dopa-
mine signals in EC, is thought to control switching between these
pathways. Desynchronized activity generated by the DG–CA3 loop in
the indirect pathway aids the agent to explore the space. Direct path-
way facilitates the agent’s navigation. The difference in the responses
from these two pathways is computed in CA1 and is relayed to sub-
iculum (Sbc) which computes the direction of the next step. Output

of Sbc is communicated to higher motor areas (MC), modeled as a 1-D
Continuous attractor neural network, via deeper layers of EC. MC also
receives direct inputs from sensory areas so that the output of MC is
the weighted sum of responses from the sensory cortical areas and HC
respectively. MC response is used to update the next step. Cortico-cor-
tical pathway (CCP) connections are updated using the TD error as well
as the velocity generated from HC as a target. As the value function
matures (Fig. 97B), contribution from HC declines; the CCP connec-
tions gain the upper hand and the agent reaches the platform faster
(Fig. 97C). Thus, after training, the CCP can drive navigation without
the involvement of HC. Analysis of CA3 activity shows desynchroniza-
tion during active exploration and synchronizationupon reaching the
platform (Fig. 97D). This resonates with experimental results suggest-
ing that low-amplitude theta waves correspond to synchronized
activity during exploration, whereas the sharp waves during non-
exploratory states correspond to synchronized activity [2].

**References**

1. Sukumar D, Rengaswamy M, Chakravarthy VS. Modeling the contribu-
tions of Basal ganglia and Hippocampus to spatial navigation using
reinforcement learning. PloS One. 2012;7(10):e47467.

2. Buzsáki G. Theta oscillations in the hippocampus. Neuron.
2002;33(3):325-40.

P189

A computational architecture to model the microanatomy of the striatum and its functional properties

Sabyasachi Shivkumar¹, Vignesh Muralidharan¹, V. Srinivasa Chakravarthy¹
¹Department of Biotechnology, Indian Institute of Technology Madras,
Chennai, Tamil Nadu, India-600036

**Correspondence:** V. Srinivasa Chakravarthy - schakra@iitm.ac.in
**BMC Neuroscience** 2016, **17**(Suppl 1):P189

We propose a computational model of the functional architecture of
the striatum. Anatomical and physiological evidence suggests that
the microstructure of the striatum maps the sensory-motor informa-
tion from the cortex in complex patterns. The dorsal striatum can be
differentiated into centre surround regions called striosomes and
matrisomes [1]. In the proposed striatum model, striosomes map the
state space and the matrisomes map the action space. The model con-
sists of a hierarchical two-level self organizing map (SOM), wherein
the higher level SOM is trained on the state values and a sub-SOM
layer containing multiple smaller SOMs are trained on action values.
Neurons of 'action SOMs' are activated by neurons of 'state SOMs'. The
scheme of mapping of state space and action space onto the pro-
posed architecture is given in Fig. 98A where the red area represents
the striosomes and green area represents the matrisomes. We have
also shown previously that such feature representation in a SOM
layer can be used to develop value functions for sensory state spaces
[2]. Thus to compute the state and action values, the activities of the
respective SOMs were mapped to individual neurons by state and
action weight vectors respectively. These weights were trained by the

<figure><img src="image_105.png" /><figcaption>**Fig. 97** The model architecture (A) used to simulate the water maze task indicating the notion of a direct and an indirect pathway. The value function (B) developed after training the agent for 10 trials, the value peaks near to the platform. The escape latency (C) through trials shows that the agent has learnt the task with increased hippocampal dependence in the earlier stages and cortical dependence in the later stages of learning. The spectrogram (D) of the activity of CA3 as the function of time shows desynchronization while active exploration of the maze (0–45 s) and synchronized activity upon reaching the platform (45–65 s)</figcaption></figure>

<figure><img src="image_106.png" /><figcaption>**Fig. 98** A Centre surround mapping in striosomes and matrisomes. The striosomes highlighted by red, map the states and the matrisomes highlighted by green, map the actions. B Value function map in the multiple context setting where the reward is present at the top left corner and the bottom right corner. C Switching of the modules based on the environmental contexts. The reward changes every 1000 episodes and the corresponding change in module with episode is shown</figcaption></figure>
temporal difference error which represents the dopamine signals from the Substantia Nigra pars compacta (SNC) based on the reward from the environment. Action selection was performed by using the action values with exploration.

The model was further extended to reflect striatal modularity, which could also be exploited to solve modular RL tasks with varying contexts [3]. This is done by using multi-SOMs, where multiple SOMs compete with each other to represent the input space. Biologically, this competition between different local striatal maps can be thought to be carried out by striatal interneurons. Using the above described striatal model as a single module, multiple modules were created. The higher level SOMs in these modules generate a responsibility signal, which represent the ability of the module to best represent that context. This was used to select the modules, a selection process which is probably carried out by the tonically active neurons (TANs). To validate this overall architecture, we tested this on the gridworld problem with a 10 × 10 grid and 4 actions. The reward is present at the corner of the grid in the first case and in subsequent case of modular RL framework, the reward is placed at one of two opposite corners. The value function map is built between the two modules and their switching at regular intervals is given in Fig. 9B, C respectively.

## References

1. Graybiel A, Flaherty A, Gimenez-Amaya J-M. Striosomes and matrisomes. In: The basal ganglia III edn. Berlin: Springer; 1991. p. 3–12.

2. Krishnan R, Ratnadurai S, Subramanian D, Chakravarthy VS, Rengaswamy M. Modeling the role of basal ganglia in saccade generation: Is the indirect pathway the explorer? Neural Networks. 2011;24(8):801–13.

3. Amemori K-I, Gibb LG, Graybiel AM. Shifting responsibly: the importance of striatal modularity to reinforcement learning in uncertain environments; 2011.

## P190

### A scalable cortico-basal ganglia model to understand the neural dynamics of targeted reaching

Vignesh Muralidharan¹, Alekhya Mandali¹, B. Pragathi Priyadharsini¹, Hima Mehta¹, V. Srinivasa Chakravarthy¹

¹Department of Biotechnology, Indian Institute of Technology Madras, Chennai, Tamil Nadu-600036, India

**Correspondence:** V. Srinivasa Chakravarthy - schakra@iitm.ac.in

BMC Neuroscience 2016, 17(Suppl 1):P190

We present a scalable network model of the basal ganglia (BG) to highlight its role in performing simple reaching movements. The model consists of the following components: a 2-joint arm model (AM), a layer of motor-neurons in the spinal cord (MN), the proprioceptive cortex (PC), the motor cortex (MC), the prefrontal cortex (PFC) and the BG (Fig. 9A). The arm model has two joints each consisting of an agonist and an antagonist muscle pair innervated by a pair of motor neurons; the muscles in turn control the position of the arm in 2D space. The PC receives information about the muscle length and tension, thought to be originating from muscle spindles and Golgi tendon organs of the muscle. The MC then uses the sensory map information from the PC to develop a motor map of the arm. The MC activity is also modulated by the BG which uses reward information to make the arm learn to reach the target. The MC then sends these signals to respective muscles of the arm via the motor neurons (MN) to perform the movement. Since the existence of maps has been well established in the cortex, the sensory map of the PC, and the map from PC to MC were modelled using the self-organizing map (SOM) algorithm [1]. The motor command is thought to arise from the PFC, which specifies the goal to be reached. The MC therefore combines inputs from three sources: the PC, the prefrontal cortex (PFC), and the BG (from GPi via the thalamus). To enable this summation dynamically, MC was modelled as a continuous attractor neural network (CANN), wherein stable activity in CANN space corresponds to an equilibrium position of the arm in the workspace.

Training of the model proceeds as follows. A target is chosen by activating corresponding neurons in the PFC. The arm makes exploratory movements driven by the Indirect Pathway of BG and gets rewarded when it reaches the target. Now BG uses this reward information and

the corresponding arm position to transform it into a value profile over the arm's workspace such that the trained value peaks at the target positions. As the training of model proceeds, the arm reaches the goal position faster and faster as BG stochastically climbs over the trained value function [2]. Furthermore, the connections from PFC and MC are also trained on successful reach, so that the motor command can directly activate the motor cortex thereby producing rapid movement avoiding the slow search conducted by the BG. The model exhibits all stages of motor learning i.e., slow movements dominated by the BG during early stages and cortically driven fast movements at later stages. The simulation results show PD symptoms like tremor which could be attributed to synchronized oscillations in STN-GPe (Fig. 9B).

<figure><img src="image_107.png" /><figcaption>**Fig. 99** The model architecture (A) with the different modules aiding in reaching movements. The comparison of controls and PD's approach to a target (B) and the appearance of PD symptoms including tremor and rigidity as a function of distance to the target</figcaption></figure>

## References

1. Chen Y, Reggia JA. Alignment of coexisting cortical maps in a motor control model. Neural Comput. 1996;8(4):731–55.

2. Magdoom K, Subramanian D, Chakravarthy VS, Ravindran B, Amari S-I, Meenakshisundaram N. Modeling basal ganglia for understanding parkinsonian reaching movements. Neural Comput. 2011;23(2):477–516.

## P191

### Emergence of radial orientation selectivity from synaptic plasticity

Catherine E. Davey¹, David B. Grayden¹,², Anthony N. Burkitt¹

¹Department of Electrical and Electronic Engineering, University of Melbourne, Victoria, 3010, Australia; ²Centre for Neural Engineering, University of Melbourne, Victoria, 3010, Australia

**Correspondence:** Catherine E. Davey - cedavey@unimelb.edu.au

BMC Neuroscience 2016, 17(Suppl 1):P191

The ability to learn and recall are primary functions of the brain. Synaptic plasticity is one of the key mechanisms by which we learn and adapt to our environment, and describes the process by which neuronal connection strengths are modified in response to environmental inputs [1]. There has been significant research effort invested into identifying the general principles of plasticity in neural networks, in order to garner insight into the learning process. The ability for animals to see and hear prior to birth is evidence of learning before exposure to ongoing external sensory signals. Consequently, cortical structure can be created, to some extent, in the absence of structured input. In a three-paper series, Linsker outlined a process by which cortical learning may occur prior to birth [2–4]. Linsker's model identified particular spatial distributions of synaptic connectivity that are sufficient to induce the development of circularly symmetric cells in a system driven only by noisy input [2]. Furthermore, Linsker [3] revealed that orientation selective cells may develop by the sixth layer of processing. However, the resulting preferred orientation was a random function of stochastic weight initialisations [5].

Radial selectivity describes a tendency for cells to have the preferred orientation biased towards a central point, and has been observed in several cortical structures, including the visual cortex [6] and the auditory cortex [7]. In this study we reveal the mechanism by which radial orientation selectivity can emerge from synaptic plasticity in the absence of structured input. Linsker's model assumed that cells
within a laminar had an identical distribution of synaptic connection densities. This assumption is modified in this study to allow synaptic connection densities to change as a function of a cell's radial distance to the centre of the laminar. The proposed network provides for spatially larger receptive fields as cells become progressively distal in the laminar, which is in keeping with electrophysiological and anatomical results. We show, both analytically and computationally, that this slightly modified network prompts the evolution of orientation selective cells with a predictable radial preference, in the third layer of neural processing. Importantly, this proposal maintains Linsker's intent for a minimal set of model assumptions, ensuring that the resulting structure is robust to details and parameter values of the model used, and that general principles of plasticity are established. Consequently, our results are applicable to cortical learning generally. The mechanisms developed in this study could play a central role in the development of radial orientation selectivity in the visual cortex.

**Acknowledgements:** This research was supported under Australian Research Council's Discovery Projects funding scheme (Project Number DP140102947).

## References

1. Hughes JR. Post-tetanic potentiation. Phys Rev. 1958;38(1):91–113.

2. Linsker R. From basic network principles to neural architecture: emergence of spatial-opponent cells. Proc Natl Acad Sci USA. 1986;83:7508–12.

3. Linsker R. From basic network principles to neural architecture: emergence of orientation-selective cells. Proc Natl Acad Sci USA. 1986;83:8390–4.

4. Linsker R. From basic network principles to neural architecture: emergence of orientation columns. Proc Natl Acad Sci USA. 1986;83:8779–83.

5. Domany E, Hemmen JL van, Schulten K: Models of neural networks III. Berlin: Springer; 2012.

6. Schall JD, Vitek DJ, Leventhal AG. Retinal constraints on orientation specificity in cat visual cortex. J Neurosci. 1986;6(3):823–36.

7. Wang Y, Brzozowska-Prechtl A, Karten HJ. Laminar and columnar auditory cortex in avian brain. Proc Natl Acad Sci USA. 2010;107:12676–81.

## P192

### How do hidden units shape effective connections between neurons?

Braden A. W. Brinkman<sup>1,2</sup>, Tyler Kekona<sup>1</sup>, Fred Rieke<sup>2,3</sup>, Eric Shea-Brown<sup>1,2,4</sup>, Michael Buice<sup>4</sup>

**Correspondence:** Braden A. W. Brinkman - bradenb@uw.edu

BMC Neuroscience 2016, 17(Suppl 1):P192

A major challenge in neuroscience is understanding how “hidden units”—neurons or other influences not observed in an experiment— influence the behavior of the observed neurons. Much work has been done on the inferring network interactions from data [1, 2], but it remains unknown how hidden neurons shape the network interactions inferred. Using techniques from non-equilibrium statistical physics, we have developed a theoretical framework to predict how effective connections in subsampled networks depend on the true connections in the full network. Beyond calculating effective connections, this approach can be systematically expanded to study how hidden units generate effective noise in subsampled networks.

As an example, we apply this framework to a network of three spiking neurons described by a generalized linear model (GLM) with rates driven by a neuron's own filtered spiking activity and those from which it receives input. By approximating the subsampled network as a GLM with effective spike-filters corrupted by Gaussian noise, we can analytically calculate how hidden units transform the filters (Fig. 100) and give rise to correlations in the effective noise (not shown). Based on our 3-neuron results, we conjecture that for general networks within this framework the filter between neurons *i* and *j* is modified by

<figure><img src="image_108.png" /><figcaption>**Fig. 100** A Self-history filters (diagonal) and directed coupling filters between neurons (off-diagonal) in the full 3-neuron network. Neuron 1 is excitatory and its couplings to the other neurons are strictly positive. Neurons 2 and 3 are inhibitory and make strictly negative couplings to other neurons. There is no coupling from neuron 2–3. **B** Effective self-history filters (diagonals) and coupling filters (off-diagonals) when neuron 3 is hidden. The *bottom row* is unaltered because neuron 2 makes no coupling to neuron 3. The filters in the *top row* are changed due to the influence of signals neuron 1 sends to itself through neuron 3 and to neuron 2 through neuron 3. Although neuron 1's true self-history filter and coupling from neuron 2 are negative the effective filters change sign. **C** The effective self-history filter of neuron 1 when both neurons 2 and 3 are hidden. Times and filter amplitudes are given in arbitrary units (a.u.)</figcaption></figure>

corrections from every path that neuron *i* can send a signal to neuron *j* through hidden units.

**Acknowledgements:** Support provided by the Sackler Scholar Program in Integrative Biophysics (BAWB), CRCNS grant DMS-1208027 (ESB, FR), NSF-DMS-1056125 (ESB), NIH grant EY11850 (FR), HHMI (FR). ESB and MB thank the Allen Institute founders, Paul G. Allen and Jody Allen, for their vision, encouragement and support.

## References

1. Pillow JP, Shlens J, Paninski L, Sher A, Litke AM, Chichilnisky EJ, Simoncelli EP. Spatio-temporal correlations and visual signalling in a complete neuronal population. Nature. 2008;454:995–9.

2. Pillow JW, Latham P. Neural characterization in partially observed populations of spiking neurons. Adv Neural Inf Process Syst. 2007;32:561–8.

## P193

### Characterization of neural firing in the presence of astrocyte-synapse signaling

Maurizio De Pittà<sup>1,2</sup>, Hugues Berry<sup>2,3</sup>, Nicolas Brunel<sup>1,3</sup>

**Correspondence:** Maurizio De Pittà - maurizio.depitta@gmail.com

BMC Neuroscience 2016, 17(Suppl 1):P194

We study analytically the dynamics of neural activity in the presence of synaptic inputs modulated by astrocyte-released neurotransmitters (i.e. the so-called “gliotransmitters”). We start with the simple scenario of gliotransmitter-mediated modulation of synaptic release probability at *N* excitatory synapses impinging on a single postsynaptic neuron as well as on the same astrocyte domain. In this scenario, release from pre-synaptic terminals leads to activation of the astrocyte, that in turn modulates synaptic release through gliotransmitter release. In the limit of *N* → ∞ synapses, we derive equations relating gliotransmitter release to the instantaneous presynaptic rate, identify conditions for co-existence of multiple states of synaptic release, and study their stability. In the bistable regime, long-lasting potentiation of synaptic release by gliotransmission accounts for the emergence of persistent postsynaptic firing. Analysis of the coefficient of variation (CV) of the ensuing interspike interval distribution reveals increased

<footnote n="1"><sup>1</sup>Department of Applied Mathematics, University of Washington, Seattle, WA 98195, USA; <sup>2</sup>Department of Physiology and Biophysics, University of Washington, Seattle, WA 98195, USA; <sup>3</sup>Howard Hughes Medical Institute, University of Washington, Seattle, WA 98195, USA; <sup>4</sup>Allen Institute for Brain Science, Seattle, WA, 98109, USA</footnote>

<footnote n="1"><sup>1</sup>Department of Neurobiology, University of Chicago, Chicago, IL 60637, USA; <sup>2</sup>Project-Team BEAGLE, INRIA Rhône-Alpes, Villeurbanne, F-69603, France; <sup>3</sup>Department of Statistics, University of Chicago, Chicago, IL 60637, USA</footnote>
firing variability following stimulation and in the presence of gliotransmission, in close analogy with increased CV values experimentally observed during the delay period in working-memory related tasks. We then extend our analysis to the scenario of a balanced neural network coupled with a network of astrocytes, and demonstrate the existence of an analogous mechanism for persistent neural firing by mean field theory. Taken together, our analysis suggests a novel astrocyte-based mechanism for persistent activity, and provides experimentally testable hypotheses on the possible involvement of astrocytes in cognitive tasks related to working memory.

P194

Metastability of spatiotemporal patterns in a large-scale network model of brain dynamics

James A. Roberts¹, Leonardo L Gollo¹, Michael Breakspear¹
¹Systems Neuroscience Group, QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia

**Correspondence:** James A. Roberts - james.roberts@qimrberghofer.edu.au

BMC Neuroscience 2016, 17(Suppl 1):P194

Advances in mapping the human connectome have yielded increasingly-detailed descriptions of large-scale brain networks, prompting growing interest in the dynamics that emerge from this structural connectivity. Moreover, there is a desire to move beyond simple static functional connectivity measures to better describe and understand the more complex repertoire of brain dynamics, which unfolds on multiple time scales. Here, we analyze the dynamics that emerge from a neural mass model [1, 2] with network connectivity derived from densely-seeded probabilistic tractography on human diffusion imaging data [3]. We find a rich array of three-dimensional wave patterns, including traveling waves, spiral waves, sources, and sinks (Fig. 101). These patterns are metastable, with the dynamics cycling between several relatively long-lived states. Varying the overall coupling strength and coupling delay reveals a complex parameter space, with other emergent patterns such as cycling between strongly-correlated clusters and multistability between different regimes (as distinct from metastability within a single regime). These dynamics accord with empirical data from multiple imaging modalities, including

observations of electrical waves in cortical tissue [4] and the presence of sequential spatiotemporal patterns in resting state MEG data [5]. By characterizing the dynamic states and time scales in our simulated data, we demonstrate the richness of dynamics that emerge from the human connectome. This work lays a platform for detailed analyses of large-scale functional neuroimaging data and their mechanistic underpinnings.

References

1. Breakspear M, Terry JR, Friston KJ: Modulation of excitatory synaptic coupling facilitates synchronization and complex dynamics in a biophysical model of neuronal dynamics. Network. 2003;14:703–32.

2. Gollo LL, Zalesky A, Hutchison RM, van den Heuvel M, Breakspear M. Dwelling quietly in the rich club: Brain network determinants of slow cortical fluctuations. Philos Trans R Soc Lond B Biol Sci. 2015;370:20140165.

3. Roberts JA, Perry A, Lord AR, Roberts G, Mitchell PB, Smith RE, Calamante F, Breakspear M. The contribution of geometry to the human connectome. Neurolmage. 2016;124:379–93.

4. Townsend RG, Solomon SS, Chen SC, Pietersen AN, Martin PR, Solomon SG, Gong P. Emergence of complex wave patterns in primate cerebral cortex. J Neurosci. 2015;35:4657–62.

5. Baker AP, Brookes MJ, Rezek IA, Smith SM, Behrens T, Smith PJ, Woolrich M. Fast transient networks in spontaneous human brain activity. eLife. 2014;3:e01867.

P195

Comparison of three methods to quantify detection and discrimination capacity estimated from neural population recordings

Gary Marsat¹, Jordan Drew¹, Phillip D. Chapman¹, Kevin C. Daly¹, Samual P. Bradley¹

¹Department of Biology, West Virginia University, Morgantown, WV 26506, USA

**Correspondence:** Gary Marsat - gary.marsat@mail.wvu.edu

BMC Neuroscience 2016, 17(Suppl 1):P195

The responses of sensory neurons carry information about the presence and the identity of relevant external events [1]. The pattern of activity in populations of such neurons must be decoded by post synaptic networks for the information to contribute to the elaboration of behavioral responses. For two stimuli to be discriminated, or a stimulus discriminated from background (i.e. detected), the patterns of activity it elicits in the encoding neural population must be different enough that target decoders are activated differentially [2]. In this research we used recording from electrosensory neurons in Gymnotid fish [3] and recordings from the antennal lobe of moth [4] to compare three methods that can be used to quantify how accurately the neural responses can support detection and discrimination tasks. The first two methods, namely Euclidian distances [5] and spike metrics distances [3], have traditionally been used by neurophysiologist to characterize the information carried by spike trains and to compare the responses to different stimuli. The third method we explored relies on the clustering neural network provided in Matlab toolboxes that uses unsupervised learning. Neural networks of this type are widely used by engineers to perform practical tasks but are rarely used by neuroscientist to study actual neural systems. The clustering tool relies on a quantification similar in many ways to Euclidian distances or spike distance metrics. However, since it learns to weight the inputs to allow optimal clustering, the weight patterns of networks that cluster accurately can reveal features that actual neural networks ought to have (including synaptic facilitation and depression or amount of convergence of inputs). We show that, both for the olfactory system of moth and the electrosensory system of fish, the neural network decoder outperforms the other two decoding analyses by weighting more heavily information rich inputs and more weakly noisy ones. We argue that this tool can be advantageously used to quantify neural coding, can make testable prediction regarding the characteristics of the decoding network, and, most importantly, can be easily used and implemented by researchers who have little training in neural modeling.

<figure><img src="image_109.png" /></figure>
**Acknowledgements:** This work was supported by NSF Grant IOS-1557846 to G.M.

**References**

1. Ollerenshaw DR, Zheng HJV, Millard DC, Wang Q, Stanley GB. The adaptive trade-off between detection and discrimination in cortical representations and behavior. Neuron. 2014;81(5):1152–64.

2. Clemens J, Ronacher B. Feature extraction and integration underlying perceptual decision making during courtship behavior. J Neurosci. 2013;33(29):12136–45.

3. Marsat G, Maler L. Neural heterogeneity and efficient population codes for communication signals. J Neurophysiol. 2010;104(5):2543–55.

4. Staudacher EM, Huetteroth W, Schachtner J, Daly KC. A 4-dimensional representation of antennal lobe output based on an ensemble of characterized projection neurons. J Neurosci Methods. 2009;180(2):208–23.

5. Daly KC, Bradley S, Chapman PD, Staudacher EM, Tiede R, Schachtner J. Space takes time: concentration dependent output codes from primary olfactory networks rapidly provide additional information at defined discrimination thresholds. Front Cell Neurosci. 2016;9.

P196

Quantifying the constraints for independent evoked and spontaneous NMDA receptor mediated synaptic transmission at individual synapses

Sat Byul Seo¹, Jianzhong Su¹, Ege T. Kavalali², Justin Blackwell¹

¹Department of Mathematics, University of Texas at Arlington, Arlington, TX 76019, USA; ²Department of Neuroscience, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA

**Correspondence:** Sat Byul Seo - satbyul.seo@mavs.uta.edu

BMC Neuroscience 2016, 17(Suppl 1):P196

Presynaptic terminals release neurotransmitters either in response to action potentials or spontaneously independent of presynaptic activity. In the case of glutamate, released neurotransmitters activate N-methyl-D-asparate (NMDA) receptors within a single postsynaptic site and give rise to miniature postsynaptic currents. In this study, we used a mathematical model to simulate spontaneous and evoked neurotransmission processes resulting from glutamate release within a synapse and evaluate the quantitative constraints that determine their degree of overlap independent signaling mediated by spontaneous and evoked release events. First we simulated isotropic diffusion of 4000 glutamates molecules release from a point source. We then simulated release of the glutamate molecules through a vesicle by addition of two compartments that one modeled the vesicle and the other represented the fusion pore. After we obtains the glutamate concentration from the standard heat equation then determine the opening probability of individual receptor using a state model (3C2O). Those two problems in MATLAB are solved.

If we assume a fivefold–tenfold ratio as a good indicator for independent currents, then we cannot assure independency with the structure for medium and small synapses in our current hypothesis. Figure 102 shows that small synapse (200 nm × 200 nm) might not have independent signaling when glutamate release instantaneously because evoked and spontaneous receptors are not far away from each other and thus not far from the release site in either evoked or spontaneous releases, the ratio of open probability is close to 1. The open probability is consistent up to 90 nm far from the release site as in Fig. 102. However for small synapses, as glutamate release through 10 and 2 nm vesicle fusion pore, the open probability ratio decreases more drastically and become close to zero, and in 2 nm pore, the ratio achieves 10-fold reduction at 90 nm distance, giving plausibility for independent signaling.

**Conclusion** From the results we conclude that peak open value is most sensitive to the distance from the receptor to the release site. Glutamate release speed or fusion pore size is relevant but to a lesser degree. The calculation was first performed for a large synapse of 0.36 µm² (with R6 near the center for evoked neurotransmission, and R16 for spontaneous neurotransmission) which established in theory that two non-overlapping domains that give rise to independent signaling in large synapses [1]. Then calculations in medium size 0.16 µm²

and small size 0.04 µm² push the biophysical envelope for independent currents, as the degree of independence decreases when the size of synapse gets smaller, or the distances from evoked and spontaneous receptors to the release site are closer together.

**Reference**

1. Atasoy D, Ertunc M, Moulder KL, et al. Spontaneous and evoked glutamate release activates two populations of NMDA receptors with limited overlap. J Neurosci. 2008;28:10151–166.

P199

Gamma oscillation via adaptive exponential integrate-and-fire neurons

LieJune Shiau¹, Laure Buhry², Kanishka Basnayake³

¹Department of Mathematics, University of Houston, Clear Lake, Houston, TX, 77059, USA; ²Department of Computational Neurosciences, University of Lorraine, Nancy, 54600, France; ³Computational Neurosciences Laboratory, Ecole Polytechnique Federale de Lausanne, CH-1015, Switzerland

**Correspondence:** LieJune Shiau - shiau@uhcl.edu

BMC Neuroscience 2016, 17(Suppl 1):P199

Coherent oscillation of neuronal spiking in the brain is known related to cognitive functions, including perception, attention, and memory. It is therefore important to determine the properties of neurons and network architectures in emerging the coherent activities that influence the network collective behaviors. It is known that, in local cortical circuits, the probability for any pair of pyramidal cells to be connected is low and about 0.1–0.2 [1]. Wang and Buzsaki [2] numerically demonstrate that, in a heterogeneous and inhibitory network with sparse and random connection, the minimal connection required per neuron to observe coherence oscillation is approximately 60 with Hodgkin–Huxley (H–H) type interneurons in certain parameter regime. More importantly, this minimal number is relatively independent to the network size. In contrast, Golomb and Hanel [3] theoretically show that,

<figure><img src="image_110.png" /><figcaption>**Fig. 102** In small synapses (200 nm × 200 nm), Ratios of maximum NMDA receptor opening probabilities as functions of receptor distance for different release speed (slow, 2 nm fusion pore—triangle, regular, 10 nm fusion pore—asterisk, and instantaneous—circle) of glutamate vesicle release. The open probabilities were calculated by the kinetics equation, when glutamates are released above the center location</figcaption></figure>
identical inhibitory neurons in a sparse and random network, the minimal connection required per neuron to exhibit coherence oscillation is about 360 with integrate-and-fire (IF) neurons. The minimal connection required in either study depends on the intrinsic and synaptic properties of the neurons.

It is shown that hippocampal CA1 neurons make an average of about 60 contacts to other neurons within a spatial span of approximately 500 µm [4]. Hence, to study rhythmic oscillation in hippocampal networks, H-H type neurons, instead of IF neurons, could produce Gamma rhythm through sparsely connected network in a population of interneurons. These findings are believed to address the importance of detailed physiological properties of single neurons in determining collective network behaviors.

We adopt an increasingly popular two-dimensional *adaptive exponential integrate-and-fire* (aEIF) model [5] which is equipped with a *subthreshold adaptation* coupling the voltage and a slow current, and a *spike-triggered adaptation* regulated through each spike. To demonstrate that aEIF neurons can provide adequate networks in inducing Gamma frequency as in hippocampus effectively, we establish the minimal synaptic contacts required in sparse and random networks of aEIF neurons to exhibit coherent oscillations, and the impacts of neuronal and synaptic properties have on the minimal value. The aEIF neuron provides more physiological neuronal details than IF neuron, but much less than the H-H neurons. Intuitively, it may be anticipated that the minimal synaptic contacts of aEIF required in such networks lies somewhere between that of the IF and H-H neurons.

We demonstrate that the minimal synaptic contacts required in such networks of aEIF neurons to exhibit Gamma rhythm is also surprisingly low with an approximation of 60 in certain parameter regime. More specifically, the minimal connection required per neuron for the onset of network synchrony is not a faction of the total number of network neurons. It either remains constant or only depends weakly on the total network neurons. This study indicates that the inclusion of sub-threshold and spike-triggered adaptations provides aEIF neuron with features to compensate for the lack of physiological details, as supposed to its H-H neuron counterpart, in studying Gamma rhythm in the brain. Our result is very encouraging in building neural network studies through a simple two-dimensional model.

## References

1. Song S, Sjostrom P, Reigl M, Nelson S, Chklovskii D. Highly nonrandom features of synaptic connectivity in local cortical circuits. PLoS Biol. 2005;3(3):0507-19.

2. Wang X, Buzsaki G. Gamma oscillation by synaptic inhibition in a hippocampal interneuronal network model. J Neurophysiol. 1996;20:6402-13.

3. Golomb D, Hansel D. The number of synaptic inputs and the synchrony of large sparse neuronal networks. Neural Comput. 1999;12(5):1095-1139.

4. Sik A, Penttonen M, Ylinen A, Buzsaki G. Hippocampal CA1 interneurons: an in vivo intracellular labeling study. J Neurosci. 1999;24:49-65.

5. Brette R, Wolfram G. Adaptive exponential integrate-and-fire model as an effective description of neuronal activity. J Neurophysiol. 2005;94:3637-42.

## P200

### Visual face representations during memory retrieval compared to perception

Sue-Hyun Lee<fnref n="1" />,², Brandon A. Levy<fnref n="3" />, Chris I. Baker<fnref n="3" />

**Correspondence:** Sue-Hyun Lee - suelee@kaist.ac.kr

BMC Neuroscience 2016, 17(Suppl 1):P200

In our daily life, we can easily discriminate and recognize familiar faces. Much evidence suggests that the fusiform face area (FFA) and the occipital face area (OFA) are involved in face processing [1-3].

However, it remains unclear how individual face information is represented in the visual cortex during retrieval compared to perception. To address this question, we performed an event-related functional magnetic resonance imaging (fMRI) experiment, comprising separate perception, learning and retrieval sessions. During the perception session, which took place inside the scanner, participants were presented with fixed pairings of six auditory cues (pseudowords) with six face images, and six auditory cues with six shoe images. During the learning session, which took place on a separate day outside the scanner, participants were trained to memorize the pseudoword-image associations for about 1 h. Finally, 1 day after the learning session, participants were scanned again and instructed to retrieve each image in response to auditory presentation of the paired pseudoword cue. To test the veracity of the retrieved visual information, participants were asked to perform forced-choice tests after the retrieval scan session, in which they heard one of the pseudoword cues and chose the paired category or image. Every participant showed near perfect performance in the forced-choice test. We focused on the patterns of response in face-selective cortical areas. Using multivoxel pattern analyses, we found that FFA showed more discriminative patterns of response to individual faces during retrieval compared to those elicited during perception. In contrast object-selective areas, which respond well to images of shoes, did not show any significant difference between perception and retrieval for individual shoe images. To determine whether the increased discrimination reflected a difference between perceived and retrieved face information and not an effect of learning, we conducted a similar fMRI experiment in which the second session was also perception and not retrieval. Importantly, there was no difference in face discrimination between the first and second perception sessions in FFA. Taken together, these results suggest that retrieval of face information generates more discriminative neural responses for individual faces than that evoked by perception of the very same faces.

**Acknowledgements:** This work was supported by the US National Institutes of Health Intramural Research Program of the National Institute of Mental Health, and a NARSAD Young Investigator Grant from the Brain & Behavior Research Foundation.

## References

1. Kanwisher N, McDermott J, Chun MM. The fusiform face area: a module in human extrastriate cortex specialized for face perception. J Neurosci. 1997;17:4302-11.

2. Tarr MJ, Gauthier I. FFA: a flexible fusiform area for subordinate-level visual processing automatized by expertise. Nat Neurosci. 2000;3:764-9.

3. Kanwisher N, Yovel G. The fusiform face area: a cortical region specialized for the perception of faces. Philos Trans R Soc Lond B Biol Sci. 2006;361:2109-28.

## P201

### Top-down modulation of sequential activity within packets modeled using avalanche dynamics

Timothée Leleu<fnref n="1" />, Kazuyuki Aihara<fnref n="1" />

¹Institute of Industrial Science, the University of Tokyo, Tokyo, Japan

**Correspondence:** Timothée Leleu - timothee@sat.t.u-tokyo.ac.jp

BMC Neuroscience 2016, 17(Suppl 1):P201

Recent experiments show that short activity packets are triggered by external stimuli or internal spontaneous events during which the temporal order of spikes is only partially stereotypical [1]. Moreover, it has been suggested that the timing of neurons during these packets depends on top-down modulatory inputs that "gate" the sensory information and represents either the replay of previously stored patterns or information about ongoing external stimuli [1]. Finally, it has been observed that spontaneous activity consists in the superposition of multiple overlapping packets [1]. We propose a simple model of cortical neural networks that reproduces these experimental observations [1] and an analytical description of the top-down modulation of packets using avalanche dynamics [2]. The proposed theory allows predicting the average size of packets using the synaptic weight matrix and vice versa.

<footnote n="1">¹Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea; ²Program of Brain and Cognitive Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea;</footnote>

<footnote n="3">³Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD 20892, USA</footnote>