or learning rule. Our results imply that the various types of memory
may be originated from a small difference in the shape of STDP kernel.

**References**

1. Bliss TV, Collingridge GL. A synaptic model of memory: long-term potentiation in the hippocampus. Nature. 1993;361(6407): 31-9.

2. Gütig R, Aharonov R, Rotter S, Sompolinsky H. Learning input correlations through nonlinear temporally asymmetric Hebbian plasticity. J Neurosci. 2003;23(9):3697-3714.

P88

Effects of time-periodic coupling strength on the first-spike
latency dynamics of a scale-free network of stochastic Hodgkin-
Huxley neurons

Ergin Yilmaz¹, Veli Baysal¹, Mahmut Ozer²

¹Department of Biomedical Engineering, Bülent Ecevit University,
Zonguldak 67100, Turkey; ²Department of Electrical and Electronics
Engineering, Bülent Ecevit University, Zonguldak 67100, Turkey

**Correspondence:** Ergin Yilmaz - erginyilmaz@yahoo.com

BMC Neuroscience 2016, 17(Suppl 1):P88

It is still not known clearly which encoding mechanism that neurons
utilize for coding of sensory information. One of the proposed encod-
ing mechanism is latency coding which suppose that first-spike latency
conveys much of the information about the stimulus. In this context,
Pankratova et al. [1] studied the effects of noise on the first-spike latency
dynamics of stochastic the Hodgkin–Huxley (HH) neuron, and obtained a
bell-shaped dependence of mean response time of the neuron on noise
intensity, emerging a phenomenon called “noise delayed decay” (NDD).
Later, this finding have been extensively studied by using complex neu-
ronal networks [2]. On the other hand, neurons exchange information via
coupling at the special location called synapse. Thus, coupled neurons
in networks play a decisive role on the phenomenon occurring in neu-
ronal networks. Majority of the studies examining the NDD effect assume
that coupling strength among coupled neurons is constant and the fact
that synapses are plastic, that is, coupling strength among neurons can
change with time, have been neglected. To present the effects of plastic-
ity or time-varying-coupling strength Birzu et al. [3] studied the effects
of time-periodic coupling strength (TPCS) on the firing dynamics of a
globally coupled array of FHN neurons. Here, our aim is to present the
effects of the frequency of TPCS on the NDD phenomenon in a scale-free
network of HH neurons. We construct the network with N = 200 neu-
rons modeled by a stochastic HH equation including ion channel noise,
and average degree of k<sub>avg</sub> = 4. We consider that the coupling strength
among coupled units changes with time-periodic fashion as proposed in
[3]. To measure the mean latency and jitter of the network, the first-spike
times of each neuron are recorded. For the comparison purpose, we give
the constant coupling strength effect on the latency dynamics of scale-
free network. Obtained result are depicted in Fig. 55.

**Conclusions** It is seen that mean latency and jitter of the first-spike times exhibit a damped sine wave dependence on the frequency of TPCS, indicating that TPCS can significantly increase or decrease the latency time which passes until sensing of the suprathreshold stimulus by each neuron at fixed intensity of channel noise (S = 100 µm²). The frequencies of TPCS greater than ω = 2 m s⁻¹ are not significantly affect the mean latency and jitter of the network, as compared to the constant coupling strength. As a result, with finely tuned values of the frequency of TPCS input signal detection performance of the scale-free network can be prominently increased by mitigating the response time of the each neuron.

**References**

1. Pankratova EV, Polovinkin AV, Mosekilde E. Resonant activation in a stochastic Hodgkin–Huxley model: interplay between noise and suprathreshold driving effects. Eur Phys J B. 2005;45:391–7.

2. Yilmaz E. Impacts of hybrid synapses on the noise-delayed decay in scale-free neural networks. Chaos Solitons Fractals. 2014;66:1–8.

3. Birzu A, Krischer K. Resonance tongues in a system of globally coupled FitzHugh-Nagumo oscillators with time-periodic coupling strength. Chaos. 2010;20(4):043114.

<figure><img src="image_60.png" /><figcaption>**Fig. 55** The statistics of the first-spike occurrence times (amplitude of TPCS ε₀ = 0.2, cell size S = 100 µm², frequency of suprathreshold signal f = 20 Hz and amplitude of it A = 4µA/cm²). **A** Mean latency of the network, **B** jitter of the network</figcaption></figure>

P89

Spectral properties of spiking responses in V1
and V4 change within the trial and are highly relevant
for behavioral performance

Veronika Koren¹,², Klaus Obermayer¹,²

¹Institute of Software Engineering and Theoretical Computer Science, Technische Universitaet Berlin, Berlin, 10587, Germany; ²Bernstein Center for Computational Neuroscience Berlin, Humboldt-Universitaet zu Berlin, Berlin, 10115, Germany

**Correspondence:** Veronika Koren - veronika.koren@bccn-berlin.de

BMC Neuroscience 2016, 17(Suppl 1):P89

Linking sensory coding and behavior is a fundamental question in neuroscience. We have addressed this issue in behaving monkey visual cortex (areas V1 and V4) while animals were trained to perform a visual discrimination task in which two successive images (target and test stimuli, with a delay period in between) were either rotated with respect to each other or were the same. We hypothesized that animal's performance in the visual discrimination task depends on the quality of stimulus coding in visual cortex. We tested this hypothesis by investigating the power spectral density of spiking signal from single neurons (spectra) and of pairs of neurons (cross-spectra) in relation to correct and incorrect behavioral responses. Our analysis shows that spectral properties systematically change with behavioral performance. Correct responses are associated with significantly higher spectra during the delay period. Cross-spectra of correct responses are significantly lower during the target period but significantly higher afterwards (delay period and test period). Spectral properties of single neurons and even more of pair-wise interactions therefore change within the trial, presumably following functional demands of stimulus processing in different epochs of the trial. Interestingly, differential dynamics in visual cortex sustains successful versus unsuccessful behavioral performance.

Preprocessing methods are used in order to avoid biases due to limited measurement time. The spike train is multiplied with Hanning window for low frequencies up to 22 Hz and with Slepian multitapers for frequencies between 24 and 140 Hz [1]. We use 300 ms window of sustained activity during stimulus periods and 500 ms window of activity during the delay period. Spectrum and cross spectrum are computed with Fast Fourier transform (Matlab, Mathworks). The cross-spectrum being a complex function, we consider its absolute value. Spectra are averaged in bins of 6 Hz. The variance and the covariance of the spiking signal are computed as sums over frequencies of auto and cross-spectra, respectively, up to the cut-off frequency (140 Hz).
<table><caption>Table 1 Estimated parameters for one-spike burster STG cell</caption><thead><tr><th>fPar</th><th>C</th><th>g<sub>Na</sub></th><th>E<sub>Na</sub></th><th>g<sub>Kd</sub></th><th>E<sub>Kd</sub></th><th>g<sub>A</sub></th><th>E<sub>A</sub></th><th>g<sub>Ca</sub></th><th>Ca<sub>0</sub></th><th>Ca<sub>f</sub></th><th>Ca<sub>t</sub></th><th>g<sub>KCa</sub></th><th>E<sub>KCa</sub></th><th>g<sub>leak</sub></th><th>E<sub>leak</sub></th></tr></thead><tbody><tr><td>Min</td><td>0.1</td><td>0</td><td>0</td><td>0</td><td>-100</td><td>0</td><td>-100</td><td>0</td><td>0.01</td><td>14</td><td>20</td><td>0</td><td>-100</td><td>0</td><td>-100</td></tr><tr><td>Max</td><td>10</td><td>800</td><td>100</td><td>200</td><td>0</td><td>75</td><td>0</td><td>5</td><td>0.1</td><td>16</td><td>250</td><td>300</td><td>0</td><td>1</td><td>0</td></tr><tr><td>Real</td><td>0.628</td><td>50</td><td>50</td><td>100</td><td>-80</td><td>5</td><td>-80</td><td>4</td><td>0.05</td><td>14.96</td><td>200</td><td>250</td><td>-80</td><td>0.01</td><td>-50</td></tr><tr><td>Estim.</td><td>0.613</td><td>62.07</td><td>39.05</td><td>95.77</td><td>-76.09</td><td>4.622</td><td>-88.59</td><td>3.912</td><td>0.0438</td><td>15.59</td><td>194.7</td><td>246.2</td><td>-80.22</td><td>0.0106</td><td>-50.15</td></tr></tbody></table>

Auto spectra are significantly higher in correct compared to incorrect trials in most of frequency bands in both V1 and V4 areas (p < 0.05 in 21 and 22 out of 24 frequency bands in V1 and V4, respectively, one-tailed sign-rank test). Consistently, the variance is significantly higher for correct responses (p < 10⁻⁴ in V1 and p = 0.0014 in V4. Cross-spectra are lower in correct trials during target period (18 and 10 out of 24 frequency bands are significant in V1 and V4, respectively, no significant effect in remaining bands) but higher in delay period (11 and 20 bands are significant in V1 and V4, respectively, no effect in remaining bands) and test period (19 and 13 significant bands in V1 and V4, respectively, no effect in remaining bands). Consistently, the covariance is significantly lower for correct responses during target stimulus (p = 0.0003, p = 0.0002 in V1 and V4) and higher during the delay (p < 10⁻⁴ in V1 and V4) and the test stimulus (p < 10⁻⁴ in V1 and V4). Our results show that spectra and cross spectra change during behavioral task and that spectral information in visual cortex might be highly relevant for behavioral performance.

### References

1. Fries P, Womelsdorf T, Oostenwald R, Desimone R. The effects of visual stimulation and selective visual attention on rhythmic neuronal synchronization in Macaque area V4. J Neurosci. 2008;28(18):4823-35.

2. Womelsdorf T, Schoffelen JM, Oostenveld R, Singer W, Desimone R, Engel AK, Fries P. Modulation of neuronal interactions through neuronal synchronization. Science. 2007;316:1609-12.

3. Appel W. Mathematics for physics & physicists. Oxfordshire: Princeton University Press; 2007.

4. Gutnisky DA, Dragoi V. Adaptive coding of visual information in neural populations. Nature. 2008;452(7184):220-4.

## P90

### Methods for building accurate models of individual neurons

Daniel Saska¹, Thomas Nowotny¹

¹School of Engineering and Informatics, Sussex Neuroscience, University of Sussex, Falmer, Brighton BN1 9QJ, UK

Correspondence: Daniel Saska - research@saska.io

BMC Neuroscience 2016, 17(Suppl 1):P90

Formulating predictive models of single neuron dynamics has become a challenge taken up by many researchers ever since Hodgkin and Huxley published their widely accepted phenomenological model of electrophysiological dynamics of the squid giant axon [1]. Advances include, amongst others, modelling complex cells (such as cells of the stomatogastric ganglion (STG) in lobster and crab [2]) or increasingly automated modelling methods [3]. However, for each problem solved, new ones emerge. One such problem has been pointed out by Golowasch et al. [4] who discovered that averaging multiple measurements from the same cell type can produce models that fail to reproduce the behaviour of the target cells. This issue does not only affect methods that rely on averaging to achieve better signal-to-noise ratio but more generally all methods that examine ion channels in separate preparations. This includes classical voltage clamp, in which different ionic conductances are measured in separate individual cells because many pharmacological blockers cannot be fully reversed.

We here propose a different approach for parameter estimation aiming to build a model based on data from a single, individual cell. The proposed method consists of the consecutive use of a voltage clamp like protocol and parameter estimation in a current clamp mode. For

the 'voltage clamp protocol' we use genetic algorithms (GA) to evolve a set of 'highlighting' voltage waveforms and specific observation windows, so that the resulting currents within the windows depend on a highlighted parameter but not so much on the values of other parameters. These parameter-specific waveforms are then applied to a live neuron (so far in simulation) and the resulting currents are observed and fitted with another GA, focusing on the highlighted parameters for each of the voltage waveforms. The resulting model is then transferred into current clamp, where the parameters are again estimated using a GA. The neuron models in the GA population are coupled to the observed cell to achieve a degree of synchronization and so smooth the error landscape. The coupling is reduced adiabatically until the model neurons and experimental cell remain synchronized with (virtually) no coupling.

We found that combining voltage and current clamp works particularly well since the fitness landscape in voltage clamp has few local minima but is fairly shallow whereas the opposite is true for the current clamp mode. We can hence find approximate parameter values from arbitrary initial guesses in our 'voltage clamp' mode and once the parameters are in the right area, they can be refined in current clamp. Our method can produce accurate models of cells in the crab STG for the cases of one, two, three and four-spike bursters. Table 1 shows the resulting parameter values for the example of a one-spike burster cell illustrated in Fig. 56.

### References

1. Hodgkin AL, Huxley AF. A quantitative description of membrane current and its application to conduction and excitation in nerve. J Physiol. 1952;117(4):500-44.

2. Liu Z, Golowasch J, Marder E, Abbott LF. A model neuron with activity-dependent conductances regulated by multiple calcium sensors. J Neurosci. 1998;18(7):2309-20.

3. Willms AR. NEUROFIT: software for fitting Hodgkin-Huxley models to voltage-clamp data. J Neurosci Methods. 2002;121(2):139-50.

4. Golowasch J, Goldman MS, Abbott LF, Marder E. Failure of averaging in the construction of a conductance-based neuron model. J Neurophysiol. 2002;87(2):1129-31.

<figure><img src="image_61.png" /><figcaption>Fig. 56 One-spike burster and estimated model as in Table 1</figcaption></figure>
P91

**A full size mathematical model of the early olfactory system of honeybees**

Ho Ka Chan¹, Alan Diamond¹, Thomas Nowotny¹

¹School of Engineering and Informatics, University of Sussex, Falmer, Brighton, BN1 9QJ, UK

**Correspondence:** Ho Ka Chan - hc338@sussex.ac.uk

BMC Neuroscience 2016, 17(Suppl 1):P91

Experimental measurements often can only provide limited data from animals' sensory systems. As a result, data driven models are similarly limited. However, in order to make biologically relevant predictions, it is important to consider inputs representative of the full sensory input space. Here we present a full size model of the early olfactory system of honeybees that extrapolates inputs from the limited subset of available experimental observations.

Our model comprise olfactory receptor neurons (ORNs), local neurons (LNs) and projection neurons (PNs) organized in 160 glomeruli. The ORN response patterns are generated using a set of ordinary differential equations describing the binding and activation of receptors as in [2]. The parameters for these processes are chosen to match the statistical distribution of experimental observed quantities in [3, 4] as well as the statistics of asymptotic responses to time-invariant odours at high concentration observed in calcium imaging of glomeruli with bath-applied Ca dyes [5]. To generate the PN responses, we considered a network in which PNs and LNs both receive excitatory input from ORNs in the same glomerulus and inhibitory input from LNs in all glomeruli. The connectivity between PNs and LNs is based on the correlation between the activities of their respective glomeruli as in [5]. A rate model, derived using the leaky integrate-and-fire model with the assumption of constant input, is used to determine the input-output relationship.

We tested our ORN model with continuous stimuli and short pulses. The average normalized ORN responses to a chemical stimulus are qualitatively similar to that of biological ORNs measured by electro-antennogram recordings [4] as shown in Fig. 57, except that the time scale of response latency is a little smaller in the model. This can be explained by the lack of temporal filtering of input conductance to output spiking in our rate model. The responses of PNs driven by ORN activity can be compared to calcium imaging data with back-filled PNs [6], which confirms that the responses of our model PNs can replicate key features of those of biological PNs. With appropriate data, our model can be generalized to the early olfactory systems of other insects. It hence provides a possible basis for future numerical studies of olfactory processing in insects.

**Acknowledgements:** This work was supported by HFSP, RGP0053/2015 and EPSRC, EP/J019690/1.

<figure><img src="image_62.png" /></figure>

References

1. Galizia CG, Sachse S, Rappert A, Menzel R. The glomerular code for odor representation is species specific in the honeybee *Apis mellifera*. *Nat Neurosci.* 1999;2(5):473–8.

2. Grémiaux A, Nowotny T, Martinez D, Lucas P, Rospars J-P. Modelling the signal delivered by a population of first-order neurons in a moth olfactory system. Brain Res. 2012;1434:123–35.

3. Rospars J-P, Lansky P, Chaput M, Duchamp-Viret P. Competitive and non-competitive odorant interactions in the early neural coding of odorant mixtures. J Neurosci. 2008;28(10):2659–66.

4. Szyszka P, Gerkin RC, Galizia CG, Smith BH. High-speed odor transduction and pulse tracking by insect olfactory receptor neurons. Proc Natl Acad Sci USA. 2014;111(47):16925–30.

5. Linster C, Sachse S, Galizia CG. Computational modeling suggests that response properties rather than spatial position determine connectivity between olfactory glomeruli. J Neurophysiol. 2005;93(6):3410–17.

6. Ditzen M. Odor concentration and identity coding in the antennal lobe of the honeybee *Apis mellifera* (PhD thesis). Berlin: Freie Universität Berlin; 2005.

P92

**Stimulation-induced tuning of ongoing oscillations in spiking neural networks**

Christoph S. Herrmann¹, Micah M. Murray², Silvio Ionta², Axel Hutt³, Jérémie Lefebvre⁴

¹Research Center Neurosensory Science, Carl-von-Ossietzky University Oldenburg, Oldenburg, Germany; ²The Laboratory for Investigative Neurophysiology (The LINE), Department of Clinical Neurosciences and Department of Radiology, University Hospital Center and University of Lausanne, Lausanne 1011, Switzerland; ³Deutscher Wetterdienst, 63067 Offenbach, Germany; ⁴Krembil Research Institute, University Health Network, Toronto, Ontario M5T 2S8, Canada

**Correspondence:** Jérémie Lefebvre - jeremie.lefebvre@uhnresearch.com
BMC Neuroscience 2016, 17(Suppl 1):P92

Rhythmic neural activity is believed to play a central role in neural computation. Oscillatory brain activity has been associated with myriad functions such as homeostasis, attention, and cognition as well as neurological and psychiatric disorders, including Parkinson's disease, schizophrenia, and depression [1]. Numerous studies have shown that that non-invasive stimulation, such as repetitive transcranial magnetic stimulation (rTMS) and Transcranial Alternating Direct Current Stimulation (TACS), provide the means of modulating large-scale oscillatory brain dynamics by perturbing and/or entraining both resting state and task activity [2]. These stimulation-induced perturbations of neural oscillations have been shown to alter cognitive performance and perception, effects that are further known to depend on brain state prior and during stimulation [3]. Yet, the surge of interest in these approaches is compromised by the existence of complex interference patterns between exogenous and endogenous dynamics.
To better understand oscillatory responses evoked during rhythmic stimulation, we simulated a spiking cortical network built of excitation and inhibitory cells, expressing resting state alpha synchrony and subjected to pulsatile forcing at frequencies in the range of 1–100 Hz. Varying stimulation parameters—such as frequency and amplitude—we evaluated the influence of stimulation on the spectral properties of the network’ global neuroelectric output. The network was composed of recurrently connected Poisson neurons with propagation delays, linear adaptation, spatially profiled and sparse synaptic connections and noisy inputs. To model exogenous influences, we used continuous trains of phasic pulses and stimulated the network globally (all neurons identically), to mimic TMS-like signals. For every stimulation condition, we also measured the neurons mean firing rate, the mean network spike coherence and non-linearity metric. Multiple spectral patterns could be observed in the network’s responses, both in the power and frequency domains, indicating a plurality of responses to shifts in stimulation frequency and/or amplitude. Network responses to slower/weaker stimulation were expectedly found to be shaped by entrainment and resonance: resonance curves defining the amplitude of the system’s responses were revealed, alongside the characteristic Arnold tongues, where stimulus-locking can be achieved. The
individual firing rates of the neurons and resulting spike coherence (assessing the degree of spiking synchronization) were both strongly tied to the stimulation forcing. In contrast, for stimulation frequencies higher than 50 Hz, a different mechanism was found to dominate the network dynamics: stimulation pulses shaped the system's response via a non-linear acceleration (NLA) on ongoing oscillatory activity. The network peak frequency was gradually shifted, leading to a transition from the alpha to the beta band, and for forcing parameters that did not recruit neither resonance nor entrainment. Also, NLA led the network in a state of weak oscillatory power, where individual neurons were found in a state of intense, irregular spiking. By investigating closely the network non-linear interactions for each stimulation conditions, we found that high-frequency forcing induces synergetic and non-linear, large-scale effects [4]. Our results provide new computational perspectives about the response of synchronous spiking neural networks in which firing rates, spike coherence and emergent oscillatory activity can be exogenously modulated using dynamic inputs. Taken together, our results suggest that the action of forcing on oscillating neural systems must be regarded as strongly non-linear, and input features must be considered as control parameters.

## References

1. Wang X-J. Neurophysiological and computational principles of cortical rhythms in cognition. Physiol Rev. 2010;90:1195-1268.

2. Thut G, et al. The functional importance of rhythmic activity in the brain. Curr Biol. 2012;22:658-63.

3. Neuling T, et al. Orchestrating neuronal networks: sustained after-effects of transcranial alternating current stimulation depend upon brain states. Front Hum Neurosci. 2013;7:161.

4. Lefebvre J, et al. Stimulus statistics shape oscillations in non-linear recurrent neural networks. J Neurosci. 2015;35(7):2895-903.

## P93

### Decision-specific sequences of neural activity in balanced random networks driven by structured sensory input

Philipp Weidel¹, Renato Duarte¹,⁴,⁵, Abigail Morrison¹,²,³,⁴

¹Institute of Advanced Simulation (IAS-6) & Institute of Neuroscience and Medicine (INM-6) & JARA BRAIN Institute I, Jülich Research Center, 52425 Jülich, Germany; ²Institute of Cognitive Neuroscience, Faculty of Psychology, Ruhr-University Bochum, 44801 Bochum, Germany;

³Simulation Laboratory Neuroscience – Bernstein Facility for Simulation and Database Technology, Institute for Advanced Simulation, Jülich Aachen Research Alliance, Jülich Research Center, Jülich, Germany;

⁴Bernstein Center Freiburg, Albert-Ludwig University of Freiburg, Freiburg im Breisgau, 79104, Germany; ⁵Institute for Adaptive and Neural Computation, School of Informatics, University of Edinburgh, EH8 9AB, UK

**Correspondence:** Philipp Weidel - p.weidel@fz-juelich.de

BMC Neuroscience 2016, 17(Suppl 1):P93

Perceptual decision-making is an intricate process implicating the coordinated activity of multiple brain areas [1, 2]. Recent experimental studies demonstrate the existence of a complex interplay between decision-related neural events and transient working memory processes [1], implemented by distributed circuits where specific subpopulations appear to be differentially involved in the evidence accumulation process and subsequent behavioral outcomes [2]. This results in observable divergences in choice-specific neuronal dynamics, unfolding as reproducible trajectories throughout the network's state-space [1] and hinting at the dissipative nature of the underlying dynamical system, which executes cognitively relevant processing through transient trajectories.

Despite this evidence, the majority of modeling studies addressing reward-modulated decision-making tend to simplify the formalization of environmental representations in the cortex as stable, attractor states corresponding to discrete environmental states [3]. Even models involving transient-based computations often simplify sensory stimuli to a discrete set of inputs transduced as stochastic point processes [4]. These simplifications potentially draw an incomplete picture of neural dynamics and therefore provide limited insights into the true nature of computation in neural circuits.

To overcome this issue, we take one step towards realistic in silico experimental settings by using structured virtual environments to obtain rich sensory input to drive model neural systems using the ROS-MUSIC toolchain [5]. It allows us to simulate robotic agents in virtual 3D environments performing a realistic perceptual decision task, which can be directly equated to experimental data. The robotic simulation generates realistic and structured sensory data which is encoded to spiking neural activity using a nonlinear encoding process, as formalized in [6]. The encoded sensory data is then used as input to a balance recurrent neural circuit.

In this study, we investigate the emergent dynamical features of neural activity when the agent is navigating a virtual T-maze. We observe decision-specific sequences of neural activity akin to experimental evidence [1], revealing possible processing strategies employed by the neural substrate. Furthermore, we investigate the role of different adaptation/plasticity mechanisms in shaping the system's dynamics. In order to equate our results with those of other studies, we attempt to partition the network state-space into discrete activity clusters, which carry relevant information that could potentially be used to drive reinforcement learning algorithms.

**Acknowledgements:** We acknowledge partial support the Helmholtz Alliance through the Initiative and Networking Fund of the Helmholtz Association and the Helmholtz Portfolio theme "Supercomputing and Modeling for the Human Brain", EuroSPIN and the German Federal Ministry for Education and Research (BMBF Grant 01GQ1343).

## References

1. Harvey CD, Coen P, Tank DW. Choice-specific sequences in parietal cortex during a virtual-navigation decision task. Nature. 2012;484(7392):62-8.

2. Shadlen MN, Kiani R. Decision making as a window on cognition. Neuron. 2013;80(3):791-806.

3. Jitsev J, Morrison A, Tittgemeyer M. Learning from positive and negative rewards in a spiking neural network model of basal ganglia. In: The 2012 international joint conference on neural networks (IJCNN). IEEE; 2012.

4. Duarte R, Morrison A. Dynamic stability of sequential stimulus representations in adapting neuronal networks. Front Comput Neurosci. 2014;8(124).

5. Weidel P, Duarte R, Djurfeldt M, Morrison A. ROS-MUSIC toolchain (in preparation).

6. Eliasith C, Anderson CH. Neural engineering: computation, representation, and dynamics in neurobiological systems. MIT Press; 2004.

## P94

### Modulation of tuning induced by abrupt reduction of SST cell activity

Jung H. Lee¹, Ramakrishnan Iyer¹, Stefan Mihalas¹

¹Allen Institute for Brain Science, Seattle, WA 98109, USA

**Correspondence:** Jung H. Lee - jungl@alleninstitute.org

BMC Neuroscience 2016, 17(Suppl 1):P94

Inhibitory interneurons have been considered pivotal in orchestrating pyramidal neurons. Indeed, the optogenetic perturbation of inhibitory cell types confirmed its validity. Recent studies [1, 2] have found that the optogenetic stimulation of somatostatin positive (SST) interneurons, one of the three major inhibitory types, sharpens the tuning of visual neurons, but its effect was conspicuous only when the optogenetic activation of SST cells was turned off abruptly. Specifically, with 4-s presentation of visual stimuli, the 1-s activation of SST cells resulted in a sharper tuning, whereas 4-s activation did not induce significant sharpening [2], which leads to a question: "Why does the length of optogenetic stimulation render such a striking difference?" Lee et al. suggested that the 1-s activation sharpens the tuning curve due to the rebound activity of PV cells, and El-Boustani et al. suggested the reduction of co-activation between PV and Pyr cell activity; see Ref. [2] for the details.

In our study, we investigate the potential mechanisms underlying the disparate effects between short and long activations of SST cells by using the firing rate equations that expresses the interactions among Pyr, SST and PV cells conveyed via cell-type specific connections reported by Pfeffer et al. [3]. Our model consists of five populations:
two pyramidal populations (Pyr1, 2), two PV cell populations (PV1, 2) and SST cell population. We assume that Pyr1 and Pyr2 in close proximity respond to preferred and non-preferred stimuli, respectively. The two pyramidal populations excite the shared SST cell population which sends inhibition back to them. Since SST cells are known to be connected to distant presynaptic pyramidal cells via long-horizontaI connections [4], the two SST populations in close proximity would receive (almost) identical inputs, making the two SST populations redundant. PV1 and PV2, which receive identical external background inputs, interact with Pyr1 and Pyr2, respectively. Pyr1 and Pyr2 are not directly connected, but they can indirectly interact with each other through SST cell population.

Our model replicates the paradoxical finding that not 4-s activa-
tion of SST cells but 1-s activation leads to the sharper responses of
V1 neurons. In our model, PV cells provide synchronized inhibition to
pyramidal cells despite their distinctive receptive fields when SST cell
activation is abruptly turned off. If SST cells are stimulated during the
entire period of simulations (4 s), the induced synchronous inhibi-
tion from PV cells to pyramidal cells is not strong enough to induce
sharper responses. We also found that this synchronous inhibition can
be induced by the activation of VIP cells, raising the possibility that VIP
cells regulate V1 neural responses with the proposed mechanism.

**Acknowledgements:** We wish to thank the Allen Institute found-
ers, Paul G. Allen and Jody Allen, for their vision, encouragement and
support.

References

1. Wilson NR, Runyan CA, Wang FL, Sur M. Division and subtraction by distinct cortical inhibitory networks in vivo. Nature. 2012;488(7411):343–8. doi:10.1038/nature11347.

2. Lee S-H, Kwan AC, Dan Y. Interneuron subtypes and orientation tuning. Nature. 2014;508(7494):E1–2. doi:10.1038/nature13128.

3. Pfeffer CK, Xue M, He M, Huang ZJ, Scanziani M. Inhibition of inhibition in visual cortex: the logic of connections between molecularly distinct interneurons. Nat Neurosci. 2013;16(8):1068–76. doi:10.1038/nn.3446.

4. Adesnik H, Bruns W, Taniguchi H, Huang ZJ, Scanziani M. A neural circuit for spatial summation in visual cortex. Nature. 2012;490(7419):226–31. doi:10.1038/nature11526.

P95

The functional role of VIP cell activation during locomotion

Jung H. Lee¹, Ramakrishnan Iyer¹, Christof Koch¹, Stefan Mihalas¹

¹Allen Institute for Brain Science, Seattle, WA 98109, USA

Correspondence: Jung H. Lee - jungl@alleninstitute.org

BMC Neuroscience 2016, 17(Suppl 1):P95

Vasoactive intestinal polypeptide positive (VIP) inhibitory interneu-
rons are commonly found in the superficial layers of cortices [1]. They
are distinct from other major cortical inhibitory cell types in terms of
connectivity and cellular mechanisms and exclusively inhibit somato-
statin (SST) cells in visual cortex. This property is consistent with the
interneuron-selective interneuron group, which has been proposed
recently [2]. Indeed, bitufted cells in this group express VIP. Moreover,
VIP cells have nicotinic receptors rarely found in SST and parvalbumin
positive (PV) cells [3]. These recent studies lead to the hypothesis that
VIP cells play unique functions in cortical areas, which can be sup-
ported with evidence. The optogenetic activation of cingulate cortex
of mouse elicited a strong response in VIP cells of V1, suggesting the
central roles of VIP cells in top-down gain control [4]. Also, VIP cells are
nonspecifically depolarized when a mouse runs [5].

VIP cells disinhibit pyramidal cells by suppressing SST cell activity. That is, when VIP cells are activated, pyramidal cell activity increases due to reduction of inhibition from SST cells, which accounts for the gain modulation. However, the advantage of VIP cell activation induced by locomotion is not clear. We hypothesized that VIP cell activation leads to better perception of moving objects since all visual objects would appear to be in motion when a mouse runs. The strong surround sup- pression could prevent visual neurons from responding to those effec- tive movements. In this sense, VIP cell activation may be beneficial to

the mouse running, as the enhanced VIP cell activity reduces surround
suppression.

Here we use a computational model of V1 consisting of multiple
cortical columns to address if VIP cell activation can enhance per-
ception on moving objects. Our computational model is based on
an earlier multiple column model [6], and we refined it by incor-
porating VIP, SST and PV cells into the superficial layers of the
model. To build this refined model, we used two strategies. First,
we inferred the time course of synaptic events and the number of
connections from both experimental data [7] and parameters from
the earlier model. Second, we identified the minimal set of interco-
lumnar connections necessary for reproducing lateral interactions
observed in visual cortex [8]. To examine whether the enhanced VIP
cells could enhance responses to moving objects, we simulated a
single moving object by stimulating the columns sequentially in the
model. Our simulation results support our hypothesis: the column
responses during sequential stimulation increase as we increase
inputs to VIP cells.

**Acknowledgements:** We wish to thank the Allen Institute found-
ers, Paul G. Allen and Jody Allen, for their vision, encouragement and
support.

References

1. Rudy B, Fishell G, Lee S, Hjerling-Leffler J. Three groups of interneurons account for nearly 100 % of neocortical GABAergic neurons. Dev Neurobiol. 2011;71:45–61.

2. Jiang X, Shen S, Cadwell C, Berence P, Sinz F, Ecker AS, et al. Principles of connectivity among morphologically defined cell types in adult neocortex. Science. 2015;350:62–4.

3. Kepecs A, Fishell G. Interneuron cell types are fit to function. Nature. 2014;505:318–26.

4. Zhang S, Xu M, Kamigaki T, Hoang Do JP, Chang W-C, Jenvay S, et al. Long-range and local circuits for top-down modulation of visual cortex processing. Science. 2014;345:660–5.

11. Fu Y, Tucciarone JM, Espinosa JS, Sheng N, Darcy DP, Nicoll RA, et al. A cor-
tical circuit for gain control by behavioral state. Cell. 2014;156:1139–52.

6. Wagatsuma N, Potjans TC, Diesmann M, Sakai K, Fukai T. Spatial and feature-based attention in a layered cortical microcircuit model. PLoS One. 2013;8:e80788.

7. Pfeffer CK, Xue M, He M, Huang ZJ, Scanziani M. Inhibition of inhibition in visual cortex: the logic of connections between molecularly distinct interneurons. Nat Neurosci. 2013;16:1068–76.

8. Xu S, Jiang W, Poo M-M, Dan Y. Activity recall in a visual cortical ensemble. Nat Neurosci. 2012, 15:449–455.

P96

Stochastic inference with spiking neural networks

Mihai A. Petrovicli¹,†, Luziwei Leng¹,†, Oliver Breitwieser¹,†, David Stöckel¹,†,
Ilja Bytschok¹, Roman Martel¹, Johannes Bill¹, Johannes Schemmel¹,
Karlheinz Meier¹

Kirchhoff-Institute for Physics, University of Heidelberg, Germany

Correspondence: Mihai A. Petrovicli - mpedro@kip.uni-heidelberg.de

†

Authors with equal contributions

BMC Neuroscience 2016, 17(Suppl 1):P96

Brains are adept at creating an impressively accurate internal model of their surrounding based on incomplete and noisy sensory data. Understanding this inferential prowess is not only interesting for neuroscience, but may also inspire computational architectures and algorithms for solving hard inference problems. Here, we give an overview of our work on probabilistic inference with brain-inspired spiking networks, their advantages compared to classical neural networks and their implementation in neuromorphic hardware.

In the neural sampling framework, we interpret spiking activity as sam-
pling from distributions over binary random variables. By exploiting
the dynamics of spiking neurons with conductance-based synapses,
we have shown that their activation function can become symmetric in
the high-conductance state, which in turn enables Glauber-like dynam-
ics in ensembles of noise-driven LIF networks [1, 2]. This allows the
straightforward construction of LIF networks that sample from previously defined probability distributions.

When the parameters of the distribution are not well-defined, they need to be learned from data. Due to their analogy to classical neural networks such as Boltzmann machines, LIF networks are amenable to the same learning algorithms and can be shown to match the performance of their equally-sized abstract counterparts when trained on classical machine-learning datasets such as MNIST. However, spiking neural networks endowed with short-term plasticity can travel more efficiently through their associated state space, allowing them to simultaneously become good generative and discriminative models of learned data, which is notoriously difficult with conventional techniques such as Gibbs sampling. This finding points towards a distinct advantage of spike-based computation and communication, which is relevant in any scenario where spiking neural networks need to be able to escape local attractors. This computational advantage of spiking sampling networks can be further bolstered by emulation on an accelerated neuromorphic substrate. The core idea behind these devices is the direct emulation of biological neuronal dynamics in VLSI circuits. Such hardware can far surpass simulators running on conventional computing architectures both in terms of speed and power consumption, but with the caveat of having limited parameter precision, as well as other sources of disruptive noise [3, 4]. With some additional modifications, we have shown how LIF networks can become robust to certain types of parameter noise—both during training and during operation— thereby making them amenable to a neuromorphic implementation with an acceleration factor of 10⁴ compared to biological real-time.

An even more compelling argument for neuromorphic spike-based inference can be made when considering that learning (in particular, the simulation of synaptic plasticity) is by far the most time-consuming factor in simulations. In an effort to make expectation-maximization learning compatible with existing neuromorphic devices, we have developed a network model that can use double-exponential STDP with 4–6 bit weight resolution for learning and spike-based homeostasis for stabilization and robustness.

This research was supported by EU grants #269921 (BrainScaleS), #237955 (FACETS-ITN), #604102 (Human Brain Project) and the Manfred Stärk Foundation.

## References

1. Petrovici MA, Bill J, Bytschok I, Schemmel J, Meier K. Stochastic inference with deterministic spiking neurons. arXiv preprint arXiv:1311.3211, 2013.

2. Petrovici MA, Bytschok I, Bill J, Schemmel J, Meier K. The high-conductance state enables neural sampling in networks of LIF neurons. BMC Neurosci. 2015;16(Suppl. 1):O2.

3. Petrovici MA, Vogginger B, Müller P, Breitwieser O, Lundqvist M, Muller L, Ehrlich M, Destexhe A, Lansner A, Schüffny R, et al. Characterization and compensation of network-level anomalies in mixed-signal neuromorphic modeling platforms. PloS One. 2014;9(10):e108590.

4. Pfeil T, Grübl A, Jeltsch S, Müller E, Müller P, Petrovici MA, Schmuker M, Brüderle D, Schemmel J, Meier K. Six networks on a universal neuromorphic computing substrate. Front Neurosci. 2013;7:11. ISSN 1662-453X. doi:10.3389/fnins.2013.00011.

## P97

### Modeling orientation-selective electrical stimulation with retinal prostheses

Timothy B. Esler¹, Anthony N. Burkitt¹, David B. Grayden¹, Robert R. Kerr², Bahman Tahayori³, Hamish Meffin⁴

¹NeuroEngineering Laboratory, Electrical & Electronic Engineering, The University of Melbourne, Parkville VIC 3010, Australia; ²IBM Research, Melbourne, Australia; ³Monash Institute of Medical Engineering, Monash University, Melbourne, Australia; ⁴National Vision Research Institute, Melbourne, Australia

**Correspondence:** Timothy B. Esler - tesler@student.unimelb.edu.au

BMC Neuroscience 2016, 17(Suppl 1):P97

One present challenge in electrical stimulation for epiretinal prostheses is how to avoid stimulating axons of passage in the nerve fiber layer (NFL) that originate from distant regions of the ganglion cell layer

(GCL). Co-stimulation of target retinal ganglion cells and overlying axons results in irregular visual percepts, which can significantly limit perceptual efficacy [1, 2]. This research explores how the characteristic distributions of fiber orientation in different retinal layers result in differences between the activation of the axon initial segment and axons of passage. Specifically, axons of passage of retinal ganglion cells are characterized by a narrow distribution of fiber orientations, dominated by the direction of passage towards the optic disk. In contrast, proximal axons in the GCL tend to have a wider distribution of orientations. A model of extracellular stimulation that captures the effects of neuromorphic orientation has been developed using a modified version of the standard volume conductor model, known as the cellular composite model [3], embedded in a four layer model of the retina. The cellular composite model is used in this analysis as it addresses a number of limitations of conventional volume conductor models and more accurately captures the spatiotemporal properties of neural tissue.

By generalizing the model to allow for analysis of fibers with arbitrary orientations, simulations have been conducted to investigate the interaction of neural tissue orientation, electrode placement, and stimulation pulse duration and amplitude.

Through an exhaustive parameter search, a set of stimulation pulse durations, amplitudes and electrode positions are proposed to achieve selective activation of axon initial segments. Using appropriate multiple electrode configurations and higher frequency stimulation, preferential activation of the axon initial segment is shown to be possible for a range of realistic electrode-retina separation distances (Fig. 58).

These results establish a quantitative relationship between the time-course of stimulation and physical properties of the tissue, such as fiber orientation.

## References

1. Fried SI, Lasker ACW, Desai NJ, Eddington DK. Axonal sodium-channel bands shape the response to electric stimulation in retinal ganglion cells. J Neurophysiol. 2009;101(4):1972–87.

<figure><img src="image_63.png" /><figcaption>**Fig. 58** A Simulation geometry showing the four modeled layers: insulator (glass), vitreous, NFL, and GCL. Distance from membrane threshold in mV for B parallel axons in a plane in the NFL and C perpendicular axon initial segments in a plane in the GCL, when stimulated with a 300 µs biphasic pulse with electrode-retina separation of 400 µm. Dotted contour marks the threshold level</figcaption></figure>
2. Rattay F, Resatz S. Effective electrode configuration for selective stimulation with inner eye prostheses. IEEE Trans Biomed Eng. 2004;51(9):1659–64.

3. Meffin H, Tahayori B, Sergeev EN, Mareels IMY, Grayden DB, Burkitt AN. Modelling extracellular electrical stimulation: III. Derivation and interpretation of neural tissue equations. J Neural Eng. 2014;11(6):065004.

P98

**Ion channel noise can explain firing correlation in auditory nerves**

Bahar Moezzi¹, Nicolangelo Iannella¹,², Mark D. McDonnell¹
¹Computational and Theoretical Neuroscience Laboratory, School of Information Technology and Mathematical Sciences, University of South Australia, Australia; ²School of Mathematical Sciences, University of Nottingham, Nottingham, UK

**Correspondence:** Bahar Moezzi - bahar.moezzi@unisa.edu.au

BMC Neuroscience 2016, 17(Suppl 1):P98

Neural spike trains are commonly characterized as a Poisson point process. However, the Poisson assumption is a poor model for spiking in auditory nerve fibers because it is known that interspike-intervals display positive correlation over long time scales and negative correlation over shorter time scales. It has been suggested that ion channel opening and closing might not be well described by Markov models. Instead, fractal ion channel gating could be used to take into account the involvement of proteins in the conformational changes of sub-states in the channel gating kinetics. Using a detailed biophysical model, we tested the hypothesis that fractal ion channel gating is responsible for short and long term correlations in the auditory nerve spike trains.

We developed a biophysical model based on the well-known Meddis model of the peripheral auditory system [1]. We introduced biophysically realistic ion channel noise to an inner hair cell membrane potential model that includes (i) fractal fast potassium channels, (ii) deterministic slow potassium channels, and (iii) a stochastic Markov model for noisy calcium channels. We used Fano factor as a measure of firing correlation.

We showed that the resulting simulated Fano factor time curves have all the common attributes of the Fano factor of experimentally recorded spike trains in the auditory nerve fibers, except the time scale of corelation. Our model thus replicates macro-scale stochastic spiking statistics in the auditory nerve fibers due to modeling stochasticity at the micro-scale of potassium and calcium ion channels.

**Reference**

1. Meddis R. Auditory-nerve first-spike latency and auditory absolute threshold: a computer model. J Acoust Soc Am. 2006;119(1):406–17.

P99

**Limits of temporal encoding of thalamocortical inputs in a
neocortical microcircuit**

Max Nolte¹, Michael W. Reimann¹, Eilif Muller¹, Henry Markram¹
¹Blue Brain Project, École Polytechnique fédérale de Lausanne (EPFL), Geneva, Switzerland

**Correspondence:** Max Nolte - max.nolte@epfl.ch

BMC Neuroscience 2016, 17(Suppl 1):P99

During naturalistic whisker motion, subsets of neurons in the same barrelloid of the rat ventroposterior medial thalamus (VPM) respond synchronously with temporal precision to different kinetic features of whisker movement (spike-time coding) [1]. Multiple synchronously firing VPM cells can trigger temporally precise responses in the somatosensory cortex, such as those observed during full whisker deflection or active touch, but the minimum number of synchronously firing VPM cells needed to reliably drive the spiking of cortical cells is not known.

In this study, we use the Blue Brain Project's digital reconstruction of a somatosensory microcircuit of a juvenile rat [2] to characterize how many synchronously firing VPM cells are needed to reliably drive individual cells of different morphological types in the rat somatosensory

cortex. We activate an increasing number of synchronously firing VPM fibers (with in vivo VPM spike trains from experiments published in [1]) in both simulations of single cells, and simulations of the whole reconstructed microcircuit with only a small number of active VPM fibers. We find that inhibitory neurons in layers 3 and 4 quickly approach maximum spike-timing reliability when receiving input from 10 to 15 synchronously firing VPM neurons. Excitatory neurons in layers 3 and 4 require substantially more synchronous VPM fibers, but less than excitatory neurons in layers 5 and 6 (see Fig. 59). With an average of eight synapses per connection, these numbers are significantly higher than what has been observed in a previous in silico study in the cat visual cortex [3]. In addition to the difference in animal and sensory system, we show that this decrease of reliability can be partly explained by a lower synaptic release probability in vivo than in vitro caused by a lower extracellular calcium concentration in vivo [4], which is taken into account in our simulations [2].

Finally, we describe how the requirement for synchronous, redundant VPM inputs limits the maximum amount of asynchronous, temporally precise VPM activity (in subsets of synchronous VPM neurons) that can be reliably encoded in a neocortical microcircuit.

**Acknowledgements:** This work was supported by funding from the ETH Domain for the Blue Brain Project (BBP). The BlueBrain IV IBM BlueGene/Q system is financed by ETH Board Funding to the Blue Brain Project and hosted at the Swiss National Supercomputing Center (CSCS). We thank M. Bale and R. Petersen for providing the VPM spike trains.

**References**

1. Bale MR, Ince RAA, Santagata G, Petersen RS. Efficient population coding of naturalistic whisker motion in the ventro-posterior medial thalamus based on precise spike timing. Front Neural Circuits. 2015;50.

2. Markram H, et al. Reconstruction and simulation of neocortical microcircuitry. Cell. 2015;163(2):456–92.

3. Wang H, Spencer D, Fellous J, Sejnowski T. Synchrony of thalamocortical inputs maximizes cortical reliability. Science. 2010;328:106–9.

4. Borst JGG. The low synaptic release probability in vivo. Trends Neurosci. 2010;33(6):259–66.

P100

**On the representation of arm reaching movements: a
computational model**

Antonio Parziale¹, Rosa Senatore, Angelo Marcelli¹
Department of Information and Electrical Engineering, University of Salerno, 84084, Fisciano (SA), Italy

**Correspondence:** Antonio Parziale - anparziale@unisa.it

BMC Neuroscience 2016, 17(Suppl 1):P100

Experimental studies on the spinal cord (SC) have shown that SC is not a simple relay station for transmitting information to and from supraspinal centers but "it is a highly evolved and complex part

<figure><img src="image_64.png" /></figure>
of the CNS that has considerable computational ability" [1]. Limb movements are planned and initiated by the brain but they cannot be performed without a spinal cord and the intricate feedback systems that reside within it [2]. In the last years, computational models have been devised in order to explain the role of the spinal cord in the translation from motor intention to motor execution [3], in sensorimotor control and learning of movements [4], in investigating how the supraspinal centers can control the cord [5], for providing evidence that CNS can plan and control movements without a representation of complex bodily dynamics because the creation and coordination of dynamic muscle forces is entrusted to the spinal feedback mechanisms [6], for investigating how the central nervous system coordinates the activation of both α and γ motoneurons during movement and posture [7].

Here we propose a computational model of the local interneuron networks within SC to evaluate how spinal and supraspinal centers can interact for performing a movement. We model a one-degree of freedom system representing an arm learning and executing reaching movements. The model incorporates the key anatomical and physiological features of the neurons in SC, namely interneurons la, Ib and PN and Renshaw cells, and their interconnections [2]. The model envisages descending inputs coming from both rostral and caudal M1 motor cortex and cerebellum (through the rubro- and reticulo-spinal tracts), local inputs from both Golgi tendon organs and spindles, and its output is directed towards α motoneurons, which also receive descending inputs from the cortex and local inputs from spindles. The model envisages virtual muscle [8] for modeling musculoskeletal mechanics and proprioceptors.

Our simulations show that the CNS may produce elbow flexion movements with different properties by adopting different strategies for the recruitment and the modulation of interneurons and motoneurons. One interesting result is that the speed-accuracy tradeoff predicted by the Fitts' law [9] does not follow from the structure of the system, that is capable of performing fast and precise movements, but arises from the strategy adopted to produce faster movements, by starting from a pre-learned set of motor commands useful to reach the target position and by modifying only the activations of the PN and α neurons.

Other simulations show that when a suddenly variation of the target position happens after the onset of a learned movement, the descending inputs from the cerebellum can be exploited for the online correction of the movement trajectory by regulating the activity of PN cells. This result agrees with the experimental studies suggesting that the CNS modulates interneurons networks to execute a visually guided online correction.

## References

1. Burke RE. Spinal cord. *Scholarpedia*. 2008;3(4):1925.

2. Pierrot-Deseilligny E, Burke DJ. The circuitry of the human spinal cord: neuroplasticity and corticospinal mechanisms. Cambridge: Cambridge University Press; 2012.

3. Bullock D, Grossberg S. VITE and FLETE: Neural modules for trajectory formation and tension control. Volitional Action. 1989;253-97.

4. Tsianos GA, Goodnes J, Loeb GE. Useful properties of spinal circuits for learning and performing planar reaches. J Neural Eng. 2014;11:1-21.

5. Raphael G, Tsianos GA, Loeb GE. Spinal-like regulator facilitates control of a two-degree-of-freedom wrist. J Neurosci. 2010;30:9431-44.

6. Buhrmann T, Di Paolo EA. Spinal circuits can accommodate interaction torques during multijoint limb movements. Front Comput Neurosci. 2014;8:1-18.

7. Li S, Hao M, He X, Marquez JC, Niu CM, Lan N. Coordinated alpha and gamma control of muscles and spindles in movement and posture. Front Comput Neurosci. 2015;9:1-15.

8. Cheng EJ, Brown IE, Loeb GE: Virtual Muscle: a computational approach to understanding the effects of muscles properties on motor control. J Neurosci Methods. 2000;101:117-30.

9. Fitts PM. The information capacity of the human motor system in controlling the amplitude of movement. J Exp Psychol. 1954;47(6):381-91.

## P101

### A computational model for investigating the role of cerebellum in acquisition and retention of motor behavior

Rosa Senatore¹,², Antonio Parziale¹, Angelo Marcelli¹

¹Department of Information and Electrical Engineering and Applied Mathematics, University of Salerno, Fisciano (SA), 81100, Italy; ²Laboratory of Neural Computation, Istituto Italiano di Tecnologia, Rovereto (TN), 38068, Italy

**Correspondence:** Rosa Senatore - rsenatore@unisa.it

BMC Neuroscience 2016, 17(Suppl 1):P101

Experimental studies on the cerebellum (CB) have provided a large body of knowledge about its anatomical and physiological features, the neural processes and the phenomena of synaptic plasticity occurring within both the cerebellar cortex and nuclei [1]. The emerging picture is that the CB plays a crucial role in the acquisition and/or retention of motor behaviors and is involved in several cognitive functions [2, 3], therefore several CB models and simulations of its neural processes have been proposed [3-5]. Here we investigated, through a modeling approach, the role of the CB in three different behaviors: vestibulo-ocular reflex (VOR) adaptation, motor learning, and eyeblink conditioning. Different cerebellar areas are involved in these functions: the control of the amplitude and timing of the VOR involves the vestibulocerebellum, learning novel limb movements involves the lateral cerebellar cortex and its connections to the dentate nucleus and acquisition of the eyeblink conditioned responses involves cerebellar cortex areas (lobule HVI) connected to the interposed nucleus[1, 3]. It is noteworthy that the CB is characterized by the remarkable regularity and geometrical structure of its circuits: cerebellar neurons are arranged in a highly regular manner as repeating units, the cerebellar microcomplexes [1]. Therefore the uniform structure of the CB and the contribution of different cerebellar areas to specific behaviors raise the possibility that different behaviors are based on a common 'neural computation within the cerebellum'.

We developed a model (using the Leabra framework in emergent neural simulation software [6]) that incorporates the key anatomical and physiological features of the cerebellar microcomplex, whose behavior was analyzed for investigating the neural processes occurring during the acquisition of novel motor behaviors, classically conditioned responses and VOR adaptation. Since the neural circuits involved in these behaviors present some differences, in terms of the input/out-put areas sending signals to or receiving signals from the CB, we developed three models, which share the same core network, made up of a set of cerebellar microcomplexes (comprising cerebellar cortex neurons and their connections to nuclear and olivary neurons), but which include different anatomical connections from/to different extra cerebellar regions: (a) "VOR model", comprising the vestibulocerebellum (flocculus and vestibular nucleus) and its connections with the dorsal cap region of the inferior olive and oculomotor nuclei; (b) "Motor model", comprising the lateral cerebellum (lateral cerebellar cortex and dentate nucleus) and its anatomical connections with the inferior olive, thalamus and motor cortex; (c) "Conditioning model", comprising the lobule HVI of the cerebellar cortex and its connections to the interposed nucleus, and their external connections with the dorsal accessory olive, red nucleus and oculomotor nuclei.

Our simulations suggest that the CB performs the same computational operation on whichever afferent information it receives, that the appearance of the 'teaching' signal conveyed by the climbing fibers could be the explanation for functional differentiation and that different types and sites of synaptic plasticity are involved in different behaviors.

## References

1. Ghez C, Thach WT. The cerebellum. In: Kandel ER, Schwartz JH, Jessell TM, editors. Principles of neural science. McGraw-Hill; 2000. p. 832-52.

2. Koziol LF, Budding D, Andreasen N, D'Arrigo S, Bulgheroni S, Imamizu H, Ito M, Manto M, Marvel C, Parker K, et al. Consensus paper: the cerebellum's role in movement and cognition. Cerebellum. 2014;13(1):151-77.
3. Manto M, Bower JM, Conforto AB, Delgado-Garcia JM, da Guarda SN, Gerwig M, Habas C, Hagura N, Ivry RB, Marien P, et al. Consensus paper: roles of the cerebellum in motor control—the diversity of ideas on cerebellar involvement in movement. Cerebellum. 2012;11(2):457–87.

4. Houk JC, Buckingham JT, Barto AG. Models of the cerebellum and motor learning. Behav Brain Sci. 1996;19(3):368–83.

5. Medina JF, Mauk MD. Computer simulation of cerebellar information processing. Nat Neurosci. 2000;3(Suppl.):1205–11.

6. Aisa B, Mingus B, O'Reilly R. The emergent neural modeling system. Neural Netw. 2008;21(8):1146–52.

P102

The emergence of semantic categories from a large-scale brain network of semantic knowledge

K. Skiker¹, M. Maouene²

¹LIST Laboratory, FST, Abdelmalek Essaadi's University, Tangier, Morocco;

²Department of computer science, ENSAT, Abdelmalek Essaadi's University, Tangier, Morocco

**Correspondence:** K. Skiker - skiker.kaoutar85@gmail.com

BMC Neuroscience 2016, 17(Suppl 1):P102

In cognitive neuroscience, the issue of how semantic categories (e.g. animals, tools, fruits/vegetables) are organized in the brain is still debated (Caramazza and Mahon 2006). Some authors postulate that semantic categories are explicitly represented in specific brain areas developed through evolutionary pressure for rapid classification and categorization of animals, tools and foods (Caramazza and Shelton 1998). Other researches argue that semantic categories are not explicitly represented, instead emerge from distributed semantic knowledge (Martin 2007; Tyler and Moss 2001). However, a little is known about how semantic knowledge is structured within the brain for fast and efficient emergence of semantic categories. In this paper, we hypothesize that semantic knowledge is supported by a large-scale brain network that shows the properties of segregation and integration. To test this hypothesis, we first examine where semantic knowledge is nested in the brain; we present functional neuroimaging studies suggesting that semantic knowledge (e.g. visual, auditory, tactile; action, olfactory/gustatory) is grounded in modality specific association brain areas (e.g. visual association areas, auditory association areas, somatosensory association areas) (Barsalou 2008; Goldberg et al. 2006). Then, we derive the connectivity between brain areas where semantic knowledge is nested from Hagmann's connectivity matrix (Hagmann et al. 2008) freely available. Finally, we examine the properties of the connectivity matrix using graph measures including clustering coefficient and characteristic path length. Our findings show that a large-scale brain network of features exhibit the small world property with high clustering coefficient (C = 0.48) and low path length (L = 2.49). These properties indicate a balance between segregation (high clustering) and integration (low path length) that are essential for the fast and efficient emergence of semantic categories from distributed semantic knowledge.

References

1. Barsalou, Lawrence W. Grounded cognition. Annu Rev Psychol. 2008;59: 617–45.

2. Caramazza A, Mahon BZ. The organisation of conceptual knowledge in the brain: the future's past and some future directions. Cogn Neuropsychol. 2006;23(1):13–38.

3. Caramazza A, Shelton JR. Domain-specific knowledge systems in the brain the animate-inanimate distinction. J Cogn Neurosci. 1998;10(1):1–34.

4. Goldberg RF, Perfetti CA, Schneider W. Perceptual knowledge retrieval activates sensory brain regions. J Neurosci. 2006;26(18):4917–21.

5. Hagmann P, Cammoun L, Gigandet X, Meuli R, Honey CJ, Wedeen VJ, Sporns O. Mapping the structural core of human cerebral cortex. PLoS Biol. 2008;6(7):e159.

6. Martin A. The representation of object concepts in the brain. Annu Rev Psychol. 2007;58:25–45.

7. Tyler LK, Moss HE. Towards a distributed account of conceptual knowledge. Trends Cogn Sci. 2001;5(6):244–52.

P103

Multiscale modeling of M1

multitarget pharmacotherapy for dystonia

Samuel A. Neymotin ¹,², Salvador Dura-Bernal¹, Alexandra Seidenstein ¹,³,
Peter Lakatos⁴, Terence D Sanger⁵,⁶, and William W Lytton ¹,⁷

¹Department Physiology & Pharmacology, SUNY Downstate, Brooklyn, NY 11203, USA; ²Department Neuroscience, Yale University School of Medicine, New Haven, CT, USA; ³Departmentt of Chemical and Biomedical Engineering, Tandon School of Engineering, NYU, Brooklyn, NY, USA; ⁴Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA; ⁵Department Biomedical Engineering, University of Southern California, Los Angeles, CA, USA; ⁶Div Neurology, Child Neurology and Movement Disorders, Children's Hospital Los Angeles, LA, CA, USA; ⁷Department Neurology, Kings County Hospital Center, Brooklyn, NY 11203, USA

**Correspondence:** Samuel A. Neymotin - samn@neurosim.downstate.edu

BMC Neuroscience 2016, 17(Suppl 1):P103

Dystonia is a movement disorder that produces involuntary sustained muscle contractions. Different types of dystonia likely involve primary or induced pathologies across multiple brain areas including basal ganglia, thalamus, cerebellum, and sensory and motor cortices. Due to lack of therapeutic alternatives, much current treatment involves paralyzing affected muscles directly with painful injections of botulinum toxin. Primary motor cortex (M1) represents a potential target for therapy. M1 pathological dynamics in some forms of dystonia include hyperexcitability and altered beta oscillations. In order to further develop understanding of motor cortex involvement in this disease and to look at potential drug cocktails (multitarget polypharmacy), we developed a multiscale model of M1 across spatial scales, ranging from molecular interactions, up to cellular and network levels. The model contains 1715 compartmental model neurons with multiple ion channels and intracellular molecular dynamics [1, 2]. Wiring and arrangements of cellular layers of the model was based on previously recorded electrophysiological data obtained from mouse M1 circuit mapping experiments. Simulations were run in the NEURON simulator and intracellular dynamics utilized the reaction–diffusion module [3]. The chemophysiological component of the simulation focused on calcium (Ca) handling, and Ca regulation of hyperpolarization-activated cyclic nucleotide-gated (HCN) channels. The Ca signaling was modeled in conjunction with intracellular cytosolic and endoplasmic reticulum (ER) volumes, inositol triphosphate (IP3) production via a metabotropic glutamate receptor signaling cascade, and ER IP3 and ryanodine receptors (RYR) which release ER Ca into the cytosol. The model reproduced the pathological dynamics providing hyperexcitability and synchronous beta oscillations across cortical layers. We applied independent random variations to multiple ion channel densities (multiple cell membrane channels: HCN, channels for Na, K, Ca; RYR, IP3 channels in ER), to identify pathological and physiological simulation sets. Experiments with these models demonstrated degeneracy, with multiple routes that produced the pathological syndrome. In most cases, there was no single parameter alteration which would induce the change from pathological to physiological dynamics. We used support vector machines to assess the high dimensional parameter space to provide overall direction for passage from an overall pathological to an overall physiological region of parameter space, enabling prediction of multitarget drug cocktails that would be likely to move the system from dystonic to physiological dynamics.

**Acknowledgements:** Research supported by NIH grant R01 MH086638, NIH grant U01 EB017695, NIH grant R01 NS064046, NIH grant R01 DC012947.

References

1. Neymotin SA, McDougal RA, Bulanova AS, Zeki M, Lakatos P, Terman D, Hines ML, Lytton WW. Calcium regulation of HCN channels supports persistent activity in a multiscale model of neocortex. Neuroscience. 2016;316:344–66.

2. Neymotin SA, McDougal RA, Sherif MA, Fall CP, Hines ML, Lytton WW. Neuronal calcium wave propagation varies with changes in
endoplasmic reticulum parameters: a computer model. Neural Comput. 2015;27:898-924.

3. McDougal RA, Hines ML, Lytton WW. Reaction–diffusion in the NEURON simulator. Front Neuroinform. 2013;7:28.

P104

Effect of network size on computational capacity

Salvador Dura-Bernal¹, Rosemary J. Menzies², Campbell McLauchlan²,
Sacha J. van Albada³, David J. Kedziora², Samuel Neymotin¹, William W.
Lytton¹, Cliff C. Kerr²

¹Department of Physiology & Pharmacology, SUNY Downstate Medical Center, Brooklyn, NY 11023, USA; ²Complex Systems Group, School of Physics, University of Sydney, Sydney, NSW 2006, Australia; ³Institute of Neuroscience and Medicine (INM-6), Jülich Research Centre and JARA, Jülich, Germany

**Correspondence:** Cliff C. Kerr - cliff@thekerrlab.com

BMC Neuroscience 2016, 17(Suppl 1):P104

There is exceptionally strong circumstantial evidence that organisms with larger nervous systems are capable of performing more complex computational tasks. Yet relatively few studies have investigated this effect directly, instead typically treating network size as a fixed property of a simulation while exploring the effects of other parameters. Recently, Diehl and Cook [1] found that network performance did increase modestly with network size; however, larger networks also required longer training times to achieve a given performance. In this work, we directly addresses the relationship between network size and computational capacity by using a biomimetic spiking network model of motor cortex to direct a virtual arm towards a target via reinforcement learning [2]. The reaching task was performed by a two-joint virtual arm controlled by four muscles (flexor and extensor muscles for shoulder and elbow joints). These muscles were controlled by a neural model that consisted of excitatory and inhibitory Izhikevich neurons in three cortical populations: a proprioceptive population, which received input from the current arm position; a motor population, which was used to drive the arm muscles; and a sensory population, which served as the link between the proprioceptive and motor populations. The model was trained to reach the target using exploratory movements coupled with reinforcement learning and spike-timing dependent plasticity (STDP). The model was implemented using NEURON.

A major challenge in scaling network size is that not all properties of the network can be held constant. As shown by van Albada et al. [3], while first-order properties (such as average firing rate) can be maintained, there are limitations in preserving second- and higher-order statistical properties (such as noise correlations). Thus, we explored multiple different ways of scaling the connectivity of the network, including (a) preserving connection probability, scaling connection weight to be inversely proportional to model size, and increasing the variance of the external drive; and (b) reducing connection probability to preserve average node degree and leaving other parameters unchanged. In addition, we explored scaling each of the neuronal population groups versus only scaling the sensory (processing) population group. Large differences were observed in network dynamics and statistics based on different scaling choices. However, the relationship between network size and task performance was significant only for certain specific choices of model parameters. Overall, task performance is highly sensitive to the network's metaparameters, such as STDP learning rates. We found that these must be optimized specifically for different network sizes; otherwise, differences in suitability of these parameters overwhelm the intrinsic advantages of larger networks. In conclusion, while network size does affect computational capacity, the relationship is strongly dependent on the manner in which the scaling is implemented.

References

1. Diehl PU, Cook M. Unsupervised learning of digit recognition using spike-timing-dependent plasticity. Front Comp Neurosci. 2015;9:99.

2. Dura-Bernal S, Li K, Neymotin SA, Francis JT, Principe JC, Lytton WW. Restoring behavior via inverse neurocontroller in a lesioned cortical spiking model driving a virtual arm. Front Neurosci. 2016;10:28.

3. 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:e1004490.

P105

NetPyNE: a Python package for NEURON to facilitate development and parallel simulation of biological neuronal networks

Salvador Dura-Bernal¹, Benjamin A. Suter², Samuel A. Neymotin¹, Cliff C. Kerr³, Adrian Quintana⁴, Padraig Gleeson⁴, Gordon M. G. Shepherd², William W. Lytton¹

¹Department Physiology & Pharmacology, SUNY Downstate, Brooklyn, NY 11203, USA; ²Department Physiology, Northwestern University, Chicago, IL 60611, USA; ³Complex Systems Group, School of Physics, University of Sydney, Sydney, NSW 2006, Australia; ⁴Department of Neuroscience, Physiology & Pharmacology, University College London, London WC1E6BT, UK

**Correspondence:** Salvador Dura-Bernal - salvadordura@gmail.com

BMC Neuroscience 2016, 17(Suppl 1):P105

NEURON is a widely used neuronal simulator, with over 1600 published models. It enables multiscale simulation ranging from the molecular to the network level. However, learning to use NEURON, especially running parallel simulations, requires much technical training. NetPyNE (Network development Python package for NEURON) greatly facilitates the development and parallel simulation of biological neuronal networks in NEURON, potentially bringing its benefits to a wider audience, including experimentalists. It is also intended for experienced modelers, providing powerful features to incorporate complex anatomical and physiological data into models.

NetPyNE seamlessly converts a set of high-level specifications into a NEURON model. Specifications are provided in a simple, standardized, declarative format, based solely on Python's lists and dictionaries. The user can define network populations and their properties, including cell type, number or density. For each cell type, the user can define morphology, biophysics and implementation, or choose to import these from existing files (HOC templates or Python classes). Cell models for each population can be easily changed, and several models can be combined to generate efficient hybrid networks, e.g. composed of Hodgkin-Huxley multicompartment cells and Izhikevich point neurons. NetPyNE provides an extremely flexible format to specify connectivity, with rules based on pre- and post-synaptic cell properties, such as cell type or location. Multiple connectivity functions are available, including all-to-all, probabilistic, convergent or divergent. Additionally, connectivity parameters (e.g. weight, probability or delay) can be specified as a function of pre/post-synaptic spatial properties. This enables implementation of complex biological patterns, such as delays or connection probabilities that depend on distance between cells, or weights that depend on the post-synaptic neuron's cortical depth. The subcellular distribution of synapses along the dendrites can be specified, and is automatically adapted to the morphology of each model neuron. Learning mechanisms, including spike-timing dependent plasticity and reinforcement learning, can be readily incorporated.

Using the high-level network specifications, NetPyNE instantiates the full model (all cells and connections) as a hierarchical Python structure including the NEURON objects necessary for simulation. Based on a set of simulation options (e.g. duration, integration step), NetPyNE runs the model in parallel using MPI, eliminating the burdensome task of manually distributing the workload and gathering data across computing nodes. Optionally NetPyNE plots output data, such as spike raster plots, LFP power spectra, connectivity matrix, or intrinsic time-varying variables (e.g. voltage) of any subset of cells. To facilitate data sharing, the package saves and loads the high-level specifications, instantiated network, and simulation results using common file formats (Pickle, Matlab, JSON or HDF5). NetPyNE can convert instantiated networks to and from NeuroML, a standard data format for exchanging models in computational neuroscience.

NetPyNE has been used to develop a variety of multiscale models: primary motor cortex with cortical depth-dependent connectivity; the claustrum; and sensorimotor cortex that learns to control a virtual arm. The package is open source, easily installed, and includes comprehensive online documentation, a step-by-step tutorial and example
networks (www.neurosimlab.org/netpyne). We believe this tool will
strengthen the neuroscience community and encourage collabora-
tions between experimentalists and modelers.

**Acknowledgements:** Research supported by NIH grant U01 EB017695 and DARPA grant N66001-10-C-2008.

P107

Inter-areal and inter-regional inhomogeneity in co-axial
anisotropy of Cortical Point Spread in human visual areas

Juhyoung Ryu¹, Sang-Hun Lee¹

¹Brain and Cognitive Science, Seoul National University, Seoul 151-742,
Republic of Korea

**Correspondence:** Juhyoung Ryu - jh67753737@snu.ac.kr, visionsl@snu.ac.kr

BMC Neuroscience 2016, 17(Suppl 1):P107

A focal visual stimulus can evoke widespread neural activation far
beyond the directly stimulated site, a phenomenon referred to as the
"cortical point spread" (CPS). The lateral connections among neurons
in the early visual cortex have been proposed as a likely anatomic-
cal conduit for the CPS, and recent functional studies on humans [1]
and non-human primates [2] demonstrated that the CPS is spatially
anisotropic, spread preferentially along with the axis of stimulus ori-
entation, dubbed as 'coaxial anisotropy.' Although these two seminal
studies documented the coaxial anisotropy robustly in two different
species, there are several remaining questions to be further explored.
First, previous human psychophysical studies reported substantial
degrees of inhomogeneity in association field characteristics over the
visual space (e.g., crowding effects), which implies the presence of cor-
responding inhomogeneity of coaxial anisotropy. Second, the animal
study [2] examined the coaxial anisotropy only from V1 of two mon-
keys, and the human study [1] reported a substantial degree of indi-
vidual differences and inter-areal differences. The current study is set
out to address these two aspects of coaxially anisotropic CPS.

We acquired time series of functional magnetic resonance imaging (fMRI) measurements in V1 while human individuals viewed a ring or wedge of Gabor patches that slowly drifted along the radial or tangential axis over a spatially extended (up to 8° in radius) region of retinotopic space (Fig. 60A). The orthogonal combination of two different drifting direction and stimulus orientation generated two interesting viewing conditions: coaxial and orthoaxial conditions (boxed and unboxed panels, respectively, in Fig. 60A). For individual gray matter units (2 mm iso volume voxels) in the early visual cortex (V1, V2, V3), we quantified the degree and sign of coaxial anisotropy by comparing the width of fMRI response profiles between the coaxial and orthoaxial conditions. In specific, we first estimated the width of CPS at the half of its maximum response respectively for two viewing conditions — coaxial condition (Wc) and orthoaxial condition (Wo),

then computed coaxial anisotropy index by taking the singed contrast
between these two width estimates: $CAI = (Wc - Wo)/(Wc + Wo)$.

**Results** The results replicated those in the previous study [1]: in all of the subjects inspected, the width of CPS was significantly greater along the coaxial axis than along the orthoaxial axis (Student's t test, p < 0.001), and the CAIs ranged from +0.05 to +0.15 (Fig. 60B). In addition, we found two interesting new findings: first, coaxial anisotropy tended to decrease along the processing hierarchy (V1 > V2 > V3; Fig. 60C); second, coaxial anisotropy tended to be more pronounced along the cardinal axes (horizontal meridians in particular) in retinotopic space.

**References**

1. Park SH, Cha K, Lee SH. Coaxial anisotropy of cortical point spread in human visual areas. J Neurosci. 2013; 33(3):1143–56a.

2. Michel MM, Chen Y, Geisler WS, Seidemann E. An illusion predicted by V1 population activity implicates cortical topography in shape perception. Nat Neurosci. 2013; 16(10):1477–83.

P108

Two bayesian quanta of uncertainty explain the temporal
dynamics of cortical activity in the non-sensory areas
during bistable perception

Joonwon Lee¹, Sang-Hun Lee¹

¹Department of Brain and Cognitive Sciences, Seoul National University, Seoul 151-742, Korea

**Correspondence:** Joonwon Lee - jwl89@snu.ac.kr; visionsl@snu.ac.kr

BMC Neuroscience 2016, 17(Suppl 1):P108

Bistable perception—involuntary fluctuation over time in perceptual appearance despite unchanging physical stimulation on sensory organs—has been a popular tool for exploring neural loci substantiating transitions in perceptual awareness. To track observers’ ongoing percepts, experimenters make the observers actively report binary states of their perception. Unfortunately, observers’ engagement in perceptual tracking is likely to invite the brain activities that are not associated directly with perceptual transition per se, but rather reflect the cognitive processes ensuing from temporal sequences of perceptual transitions. We reasoned that, in a computational perspective, the latter, post-transition component of bistable perception is majorly driven by two separate quanta of uncertainty: unexpected (UU) and expected uncertainty (EU), which have been proposed to relate to neuro-modulatory systems of norepinephrine and acetylcholine, respectively [1]. We found the Dynamic Belief Model (DBM) [2] particularly relevant to the concurrent measurement of those two quanta of uncertainty (i) because it is capable of updating the moment-to-moment, expected probability of binary events based on a recent history of those events (Fig. 61A) and (ii) because this probability directly estimates EU whereas the disparity between the expected probability and perceived outcome quantize UU.

<figure><img src="image_65.png" /><figcaption>**Fig. 60** Stimuli and fMRI results. **A** The snapshot of traveling Gabors are shown for the four different conditions. The black arrows represent a moving direction of wedge or ring. **B** Significant (yellow, t test p < 0.001) coaxial anisotropy in all subjects. **C** Coaxial anisotropy across visual areas (V1, V2, V3)</figcaption></figure>

<figure><img src="image_66.png" /><figcaption>**Fig. 61** Bayesian estimation to predict BOLD dynamics around switch. **A** Bayesian inference model of iteratively updated prior, input likelihood, and combined posterior. **B** (Upper) Uncertainty-driven BOLD estimated from Bayesian model locked to transition under different duration conditions. Time-series is built purely from real behavior history. (Lower) Average % BOLD signal of ACC region in 8 subjects (14 sessions)</figcaption></figure>
With the DBM in hand, we predicted the time courses of UU and EU (red curves in Fig. 61B), and explored the cortical loci substantiating those two kinds of uncertainty by acquiring fMRI measurements while human observers viewed a 'structure-from-motion (SfM)' display, in which ambiguous 2D motion of coherently moving dots gives perceptual alternations in 3D motion perception between bistable states, clockwise vs counterclockwise rotational motion. To compensate for the temporal resolution of fMRI activity, we slowed down the dynamics of bistable perception using the intermittent stimulation technique, which allowed us to identify gray-matter units (voxels) whose variability in fMRI time course can be explained by UU or EU.

As expected from previous studies, cortical activity increased substantially during the transition periods in many distributed brain regions. More importantly, the fMRI time series in these transition-locked regions were explained by the weighted linear sum of the time series of UU and EU quantity, some exhibiting greater weights for UU and others greater weights for EU. In additions, the time series of pupil size of the observers resembled the predicted time courses of UU, consistent with the previously reported tight linkage between UU and the LC-NE system. We conclude that the cortical activities previously claimed as being responsible for triggering perceptual transition are likely to reflect two post-transition cognitive quanta of uncertainty.

### References

1. Yu AJ, Dayan P. Uncertainty, neuromodulation, and attention. Neuron. 2005;46:681-92.

2. Yu AJ, Cohen JD. Sequential effects: superstition or rational behavior? NIPS. 2009;21:1873-80.

## P109

### Optimal and suboptimal integration of sensory and value information in perceptual decision making

Hyang Jung Lee¹, Sang-Hun Lee¹

¹Department of Brain and Cognitive Neuroscience, Seoul National University, Gwanak-gu, South Korea

**Correspondence:** Hyang Jung Lee - hyangjung.lee@snu.ac.kr, visionsl@snu.ac.kr

BMC Neuroscience 2016, 17(Suppl 1):P109

Optimization of decision-making often requires the effective integration of sensory and value information, particularly when sensory inputs are ambiguous and the criterion for the successful decision changes stochastically over time (e.g., a hitter making 'strike' or 'ball' decisions by integrating visual information and an umpire's calls). We adapted this 'sensory-value integration' situation to a laboratory, where 30 human subjects classified ring stimuli into 'small' or 'large' based on the perceived ring size and the trial-by-trial feedback ('correct' or 'incorrect') for judgment. The key manipulation was, unbeknownst to subjects, to induce a slight amount of bias, favoring either 'small' or 'large' choices, or staying 'unbiased'. Inspired by previous animal studies (Corrado et al. 2005; Lau and Glimcher 2005; Busse et al. 2011), we developed a Linear-Nonlinear-Poisson model to describe the dynamics of how humans adapt their moment-to-moment perceptual decision to subtle, yet volatile environmental feedbacks. The integration process takes place at the Linear stage where a decision variable is formed by combining the sensory information in the current trial and the reward/choice information histories. This is followed by the nonlinear stage where softmax rule is applied to translate the decision variable into probability for the ensuing Poisson stage. Fitting the model to the data set of each individual allowed us to explore the individual differences in optimal sensory-value integration in our task. Our L-N-P model effectively depicted, and generated as well, the temporal dynamics of human subjects' perceptual choices made in an environment with volatile and stochastic feedbacks. The correlation analysis identified a set of latent model parameters (e.g., reward kernel weight) that are tightly linked to the individual differences in ability to adapt their decision to abrupt changes in feedback. The ideal decision-maker analysis indicated that human subjects are generically suboptimal in forming an effective reward kernel for translating feedbacks in the past trials into action values in the upcoming trials.

**Acknowledgements:** Supported by National Research Foundation of Korea, NRF-2013R1A2A2A03017022.

### References

1. Busse L, Ayaz A, Dhruv, NT, Katzner S, Saleem, AB, Schölvinck ML, Carandini M. The detection of visual contrast in the behaving mouse. J Neurosci. 2011;31(31):11351-61.

2. Lau B, Glimcher PW. Dynamic response-by-response models of matching behavior in rhesus monkeys. J Exp Anal Behav. 2005;84(3):555-79.

3. Corrado GS, Sugrue LP, Seung, HS, Newsome, WT. Linear-nonlinear-Poisson models of primate choice dynamics. J Exp Anal Behav. 2005;84(3):581.

## P110

### A Bayesian algorithm for phoneme Perception and its neural implementation

Daeseob Lim¹, Sang-Hun Lee¹

¹Department of Brain and Cognitive Sciences, Seoul National University, Seoul, 08826, South Korea

**Correspondence:** Daeseob Lim - daeseob@snu.ac.kr, visionsl@snu.ac.kr

BMC Neuroscience 2016, 17(Suppl 1):P110

Human listeners seem effortless in recognizing a rapid stream of speech sounds uttered by their fellow speakers, thus being capable of readily participating in conversation. However, it remains poorly understood how the brain represents the basic processing unit of such fluent speech perception, phoneme. In a computational perspective, phoneme perception is a reverse engineering of speech production, where the goal is to infer from noisy acoustic signal which phonetic gesture is the one that was most probably intended by a speaker. This 'probabilistic inferential' nature of computations makes the Bayesian framework attractive. Here we developed a Bayesian algorithm that captures the two characteristic phenomena of phoneme perception, (1) sharp transitions in perception (categorization) and (2) enhanced discriminability (differentiation) at around phoneme boundaries, and then explored how this algorithm can be implemented in plastic human brains.

Our model posits (i) that the brain has the probabilistic knowledge of frequencies of phonetic stimuli prior to forming the likelihood of phoneme stimuli based on noisy sensory signals (prior and likelihood beliefs), (ii) that the brain combines these two knowledges to form a posterior distribution of probability (posterior belief), and (iii) that the brain 'optimally' utilizes this single posterior belief to concurrently perform the categorization and discrimination tasks. The major latent variables of our interest were the shape of the prior probability distribution function (prior PDF) and the width of the likelihood PDF. The parameters for these two PDFs were estimated by fitting the model to the behavioral data in a pair of psychophysical experiments, where human subjects both categorized and discriminated the acoustic stimuli comprising the cyclic transition among three voiced stop consonant-vowel syllables, /ba/-/da/-/ga/, varying in the place of articulation (labial-alveolar-velar). The behaviorally constrained models revealed the prior PDF with the three modes whose peaks correspond to the three prototypical phoneme syllables and the posterior PDF with large variance.

Having identified the prior and likelihood PDFs used by the optimal Bayesian listener, we explored plausible neural mechanisms for implementing those PDFs. Going beyond previous attempts, our proposal of Bayesian implementation offers a formal account for how the unequal frequency of acoustic stimuli, i.e., stimulus prior, is developmentally translated into an unequal distribution of sensory neurons via well-known canonical principles of neural plasticity ('neural remapping'). Specifically we propose that sensory neurons, stimulus tuning preferences of which were equally distributed initially, iteratively shift their tuning curves toward an experienced stimulus ('attractive shift') as a function of their current responitivity to that stimulus (Fig. 62). The population distribution of sensory tuning curves that were shaped by this remapping scenario, when plugged into typical probabilistic population coding schemes, reproduced qualitatively the human listeners' performances in the both phoneme tasks. Our model exercise on tuning width also shed new light on how the optimal tuning width
<figure><img src="image_67.png" /></figure>

**Fig. 62** Interaction between tuning curve and prior in population tuning. A Bias map for combinations of tuning curve width and prior sharpness. Negative bias means that perception of a near-/ba/stimulus was biased toward/ba/, namely categorical perception. Three inset plots on ordinate and abscissa show the cases of the lowest/ median/highest concentration parameters of tuning curve and prior peak, respectively. B Discrimination difference map. Negative number indicates that between-phoneme condition outperformed near-/ba/ condition. C Tuning curves of population neurons that was marked as green squares in A, B. Location of tuning centers were marked as dots for 60 neurons, and tuning curves of 30 out of those 60 neurons were drawn below

of sensory neurons (broad tuning in our case) can be constrained by the task requirements (categorical perception) and the stimulus environments (biased prior) imposed on a given sensory system (speech perception).

P111

Complexity of EEG signals is reduced during unconsciousness induced by ketamine and propofol

Jisung Wang¹, Heonsoo Lee¹

¹Physics department, Pohang University of Science and Technology, Pohang, South Korea

**Correspondence:** Heonsoo Lee - beafool@postech.ac.kr

BMC Neuroscience 2016, 17(Suppl 1):P111

Identifying a universal feature of brain dynamics during anesthetic-induced unconsciousness has been an important work for both practical use of monitoring depth of general anesthesia and scientific knowledge about the nature of consciousness. However, it is difficult because anesthetics with different mechanism of action (MOA) induce distinct brain dynamics. From the perspective of complex system science, we claim that the dynamics generated by conscious brain is more complex compared to one from anesthetic-induced unconscious brain regardless of anesthetic types. To test the hypothesis, we used ketamine and propofol which fall into two distinct anesthetic groups [1]. Disorder and complexity of electroencephalogram (EEG) signals were analyzed before and after bolus injection of drugs. For the analysis, we employed Shannon entropy (SE) and fluctuation complexity (FC), which are information theory-based measures quantifying disorder and complexity, respectively [2]. The study shows that ketamine and propofol both reduced the complexity (p < 0.00001 for both) of EEG signals from the whole brain area (Fp1, F3, T3, P3) while each respectively increased (p = 0.000112) and decreased (p < 0.00001) disorder of the signal (Fig. 63). The finding supports our claim and suggests considering the EEG complexity as a common measure of consciousness.

<figure><img src="image_68.png" /></figure>

**Fig. 63** A SE and FC values of Fp1 channel for three different states, which are wakeful, ketamine-induced, and propofol-induced states, are averaged over subjects (n = 29 for ketamine-induced, n = 20 for propofol-induced and n = 49 for wakeful states). Error bars represent standard errors. Wakeful state has the intermediate SE value between ones of two other states. For FC value, however, wakeful state has the highest one and both anesthetized states have smaller ones, forming a concave relationship between three states. B Each dot manifests averaged SE and FC values of one subject over 23 10 s-long epochs overlapping 5 s each other. Error bars here also indicate standard errors. Most wakeful states have higher FC values compared to ones of unconscious states and have intermediate SE values. Ketamine-induced states are mainly located in the lower right part when propofol-induced states are clustered at the lower left part of the area in SE-FC plot. C FC values of EEG signals from the whole brain area, covering pre-frontal, frontal, temporal, and parietal regions, significantly decreased during ketamine-induced loss of consciousness (p < 0.0001). D All FC values of the signals from different regions were also significantly reduced during propofol-induced unconscious state (p < 0.0001)

References

1. Lee U, Ku S, Noh G, Baek S, Choi B, Mashour GA. Disruption of frontal-parietal communication by ketamine, propofol, and sevoflurane. J Am Soc Anesthesiol. 2013;118:1245-6.

2. Bates JE, Shepard HK. Measuring complexity using information fluctuation. Phys Lett A. 1993;172:416-25.

P112

Self-organized criticality of neural avalanche in a neural model on complex networks

Nam Jung¹, Le Anh Quang¹, Seung Eun Maeng¹, Tae Ho Lee¹, Jae Woo Lee¹

¹Department of Physics, Inha University, Namgu, Incheon 22212, Korea

**Correspondence:** Jae Woo Lee - jaewlee@inha.ac.kr

BMC Neuroscience 2016, 17(Suppl 1):P112

The concept of the self-organized criticality is applied to many natural and economical systems [1-4]. The distribution of neural avalanche in neural models obeys a power law with exponents of the mean-field theory. Neural avalanches in cultured neocortical network show self-organized criticality over long stable period with exponent -1.5 of the power law for the distribution of the neural avalanche [1]. We consider a modified integrate-and-fire model introduced by Levina, Hermann and Geisel (LHG model) [2]. We extend the LHG model on the complex networks such as fully-connected network, random network, small-world network, and scale-free networks. In the LHG model the
<figure><img src="image_69.png" /><figcaption>**Fig. 64** Distribution of avalanche size for LHG model on the fully-connected network. The distribution function of avalanche size shows the power law with exponent -1.57</figcaption></figure>

membrane potential of a neuron is accumulated from input potential and random external input. In a fully connected network we observed the power law with exponent -1.57 as shown in Fig. 64. The exponent of the power law depends on the network structure of the neural systems.

**Acknowledgements:** This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (NRF-2014R1A2A1A11051982).

## References

1. Beggs J, Plenz D. Neural avalanche in neocortical circuits. J Neurosci. 2003;23:11167-77.

2. Levina A, Herrmann JM, Geisel T. Dynamical synapses causing self-organized criticality in neural networks. Nat Phys. 2007;3:857-60.

3. Li X, Small M. Neuronal avalanches of a self-organized neural network with active-neuron-dominant structure. Chaos. 2012;22:023104.

4. Liu H, Song Y, Xue F, Li X. Effects of bursting dynamic features on the generation of multi-clustered structure of neural network with symmetric spike-timing-dependent plasticity learning rule. Chaos. 2015;25:113108.

# P113
## Dynamic alterations in connection topology of the hippocampal network during ictal-like epileptiform activity in an in vitro rat model

Chang-hyun Park<sup>1,2</sup>, Sora Ahn<sup>3</sup>, Jangsup Moon<sup>1,2</sup>, Yun Seo Choi<sup>2</sup>, Juhee Kim<sup>2</sup>, Sang Beom Jun<sup>3,4</sup>, Seungjun Lee<sup>3</sup>, Hyang Woon Lee<sup>1,2</sup>

<sup>1</sup>Departments of Neurology, Ewha Womans University School of Medicine, Seoul, Korea; <sup>2</sup>Department of Medical Science, Ewha Womans University School of Medicine, Seoul, Korea; <sup>3</sup>Department of Electronics Engineering, Ewha Womans University College of Engineering, Seoul, Korea; <sup>4</sup>Brain & Cognitive Sciences, Ewha Womans University College of Scranton, Seoul, Korea

**Correspondence:** Chang-hyun Park - park.changhyun@gmail.com
BMC Neuroscience 2016, 17(Suppl 1):P113

The experimental approach using in vitro slices of the rat limbic system has been applied to identify mechanisms underlying epileptiform activity in ictal-like events [1]. We prepared combined slices of the rat hippocampus-entorhinal cortex and placed them in artificial corticospinal fluid that contained 4-aminopyridine (4AP). Field potential recordings were made with a microelectrode array composed of 6 × 10 microelectrodes with inter-electrode spacing of 500 µm (see Fig. 65A) when synchronous activity was induced by 4AP. Channels with high artifact rates were rejected, and the signal for each remaining channel was divided into 10 s windows without overlap. For each window, adjacency matrices, or binary networks, were estimated via

<figure><img src="image_70.png" /></figure>

<figure><img src="image_71.png" /><figcaption>**Fig. 65** A Microelectrodes placed to cover both entorhinal cortex and hippocampus. B Temporal changes in global and local efficiency around the time of ictal-like epileptiform activity. The red vertical line indicates the initiation of ictal-like events and the time series in blue displays recorded field potentials</figcaption></figure>

inter-channel connections based on spectral coherence in different frequency bands, including delta, theta, alpha, beta, gamma, ripple, and fast ripple. Topological properties of the inter-channel networks were assessed by calculating global and local efficiency [2]. As ictal-like events were initiated, global efficiency started to decrease and local efficiency started to increase, and the changes were maintained during ictal-like epileptiform activity (see Fig. 65B). Such changes related to a shift in connection topology to a regularized pattern are in line with the findings for the whole brain in a rat model [3].

**Conclusions** Although the initiation and propagation of epileptiform activity may not be fully appreciated due to the spatially isolated structure in the in vitro slice preparation, the pattern of ictal-like synchronous activity in the limbic system was related to changes in connection topology that may reflect a shift in brain states.

**Acknowledgements:** This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (2015R1C1A1A01052438 to C. Park and 2014R1A2A1A11052103 to H. W. Lee), and by the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI) funded by the Ministry of Health & Welfare (HI14C1989 to H. W. Lee).

## References

1. Avoli M, Barbarosle M, Lücke A, Nagao T, Lopantsev V, Köhling R: Synchro-nous GABA-mediated potentials and epileptiform discharges in the rat limbic system in vitro. J Neurosci. 1996;16(12):3912-24.

2. Latora V, Marchiori M. Efficient behavior of small-world networks. Phys Rev Lett. 2001;87(19):198701.

3. Otte WM, Dijkhuizen RM, van Meer MPA, van der Hel WS, Verlinde SAMW, van Nieuwenhuizen O, Viergever MA, Stam CJ, Braun KPJ. Characterization of functional and structural integrity in experimental focal epilepsy: reduced network efficiency coincides with white matter changes. PLoS One. 2012;7(7):e39078.

# P114
## Computational model to replicate seizure suppression effect by electrical stimulation

Sora Ahn<sup>1</sup>, Sumin Jo<sup>1</sup>, Eunji Jun<sup>1</sup>, Suin Yu<sup>1</sup>, Hyang Woon Lee<sup>2</sup>, Sang Beom Jun<sup>1</sup>, Seungjun Lee<sup>1</sup>

<sup>1</sup>Department of Electronics Engineering, Ewha Womans University, Seoul, 120-750, Korea; <sup>2</sup>Department of Neurology, Ewha Womans University, Seoul, 120-750, Korea

**Correspondence:** Seungjun Lee - slee@ewha.ac.kr
BMC Neuroscience 2016, 17(Suppl 1):P114

Deep brain stimulation (DBS) method for suppression of epileptic seizure is being developed mostly based on clinical experiences because the suppression mechanism by electrical stimulation is still unclear. As such, it is difficult to improve efficacy of the DBS method. The study of computational models allows to predict and analyze the effect of electrical stimulation by computer simulation such that it can help to determine optimum stimulation parameters to suppress seizure activity in various conditions.
<figure><img src="image_72.png" /><figcaption>**Fig. 66** Recording data (A) and simulation results (B) of SLE suppression effect by electrical stimulation</figcaption></figure>

In this paper, we propose a hippocampal network model which portrays propagation characteristics of seizure-like events (SLEs) and suppression phenomena by electrical stimulation. The model is composed of four sub-networks representing EC, DG, CA3 and CA1 and well-known synaptic pathways between sub-networks. Each sub-network consists of excitatory and inhibitory neurons which are described by Izhikevich's model [1]. Besides synaptic transmission [2], electrical field transmission [3] between neurons is also considered. Input gains of neurons are controlled by interaction strengths between sub-networks which are calculated by Granger causality analysis. We adopt the "potassium accumulation hypothesis" in order to replicate suppression effect by electrical stimulation [4, 5]. The effectiveness of the model is confirmed by comparing the simulation results with experimental data which were measured in rat hippocampal slice (horizontal, 400um) in bicuculline bath application. Local field potentials are recorded using micro-electrode array (MEA) and electrical stimulation (130 Hz, 500 µA, biphasic, 3–5 s) is applied manually in EC by an additional depth electrode when SLE is initiated.

Following Fig. 66 shows time domain signals recorded in in vitro measurement and generated from the computer model, respectively. After stimulation, the SLE in EC is suppressed immediately, while SLEs in other areas still remained. The simulation results show similar waveforms with experimental data.

**Acknowledgements:** This work was supported by the National Research Foundation of Korea (No. 2014R1A2A1A11052763).

## References

1. Izhikevich EM. Simple model of spiking neurons. IEEE Trans Neural Netw. 2003;14(6): 1569-72.

2. Izhikevich EM, Gally JA, Edelman GM. Spike-timing dynamics of neuronal groups. Cereb Cortex. 2004;14(8):933-44.

3. Fröhlich F, McCormick DA. Endogenous electric fields may guide neocortical network activity. Neuron. 2010;67(1):129-43.

4. Fertziger AP, Ranck JB. Potassium accumulation in interstitial space during epileptiform seizures. Exp Neurol. 1970;26(3):571-85.

5. Beurrier C, Bioulac B, Audin J, Hammond C. High-frequency stimulation produces a transient blockade of voltage-gated currents in subthalamic neurons. J Neurophysiol. 2001;85(4):1351-56.

## P115

### Identifying excitatory and inhibitory synapses in neuronal networks from spike trains using sorted local transfer entropy

Felix Goetze¹,², Pik-Yin Lai¹

¹Department of Physics, National Central University, Chung-Li, Taiwan, ROC; ²Taiwan International Graduate Program for Molecular Science and Technology, Institute for Atomic and Molecular Sciences, Academia Sinica, Taipei, Taiwan, ROC

**Correspondence:** Felix Goetze - afgoetze@gmail.com

BMC Neuroscience 2016, 17(Suppl 1):P115

Transfer entropy [1] is the established method for quantifying the effective connectivity among neurons.

It has been shown in simulations [2] that measuring it from simultaneously recorded spike trains of neurons can detect the underlying connections and therefore reconstruct a neuronal network from its observed dynamics. Being interpreted as the predicted information

transfer, it quantifies the directed non-linear interactions between time series as a model-free method regardless of the underlying interaction type, which could be either inhibitory or excitatory.

Making the distinction between excitatory and inhibitory synapses, however is important in order to understand the underlying principles of spatiotemporal patterns in functional networks.

In our study we describe a method for the measuring of interaction types, based on the concept of local transfer entropies [3]. In contrast to the averaging across all configurations of variables of the source process and the target process as in the Transfer Entropy estimation, the local transfer entropy quantifies the effect of a specific configuration of variables on how they either inform or misinform on the future of the target process. For example observing the presynaptic neuron fire and then observing the postsynaptic neuron fire is informative for an excitatory connection, but misinformative for an inhibitory connection. On average, knowing the past of the source process of an inhibitory or excitatory connection are both predictive of the future of the target process, but local transfer entropies of specific variable configurations have opposite signed values for each interaction type respectively. Sorting the local entropies according to interaction type yields the quantity we call sorted local transfer entropy that aims to identify inhibitory and excitatory synapses from recorded spike trains.

We validate this method with simulated spike trains from the Izhikevich model [4] of cortical neuronal networks, by following a previous paper [2]. The random network is noise-driven and consists of 800 excitatory and 200 inhibitory neurons. Synapses have random delays and the synaptic strengths evolved according to a spike-timing plasticity rule, before the recordings for the analysis are collected. Using Transfer Entropy and the new quantity Sorted Local Transfer Entropy, we reconstruct the networks and distinguish inhibitory from excitatory synapses. The use of two decision boundaries for classifying inhibitory and excitatory synapses separately improves the overall network reconstruction.

## References

1. Schreiber T. Measuring information transfer. Phys Rev Lett. 2000;85:461.

2. Ito S, Hansen ME, Heiland R, Lumsdaine A, Litke AM, Beggs JM, Zochowski M. Extending transfer entropy improves identification of effective connectivity in a spiking cortical network model. PLoS One. 2011;6:e27431

3. Lizier JT. Measuring the dynamics of information processing on a local scale in time and space. In: Directed information measures in neuroscience. Berlin: Springer; 2014. p. 161-93.

4. Izhikevich EM. Polychronization: computation with spikes. Neural Comput. 2006;18(2):245-82.

## P116

### Neural network model for obstacle avoidance based on neuromorphic computational model of boundary vector cell and head direction cell

Seonghyun Kim¹, Jeehyun Kwag¹

Department of Brain and Cognitive Engineering, Korea University, Seoul, Korea

**Correspondence:** Jeehyun Kwag - jkwag@korea.ac.kr

BMC Neuroscience 2016, 17(Suppl 1):P116

Developing robots that can perform autonomous exploration in unfamiliar environment is one of the challenges in robotics. Autonomously navigating robots are generally equipped with an obstacle avoidance (OA) system based on sensors such as Light Detection and Ranging (LIDAR) and camera to detect obstacles as well as complex algorithms to correct the noise of sensors [1]. Interestingly, rodent brain shows remarkable reliability to noise in OA [2] through using neurons specialized in processing spatial orientation and spatial boundary called head direction cell (HDC) [3] and boundary vector cell (BVC) [4], respectively. Therefore, building a bioinspired OA system with neural network that consists of neuromorphic HDC and BVC may help increase the efficiency of autonomous navigation. Hence, we built a neural network for OA consisting of all-to-all synaptic connections between six HDCs, four BVCs, and two motor neurons where HDC and BVC were constructed as multi-compartment Hodgkin-Huxley models using the NEURON based on full morphology and electrophysiological properties in vitro
[5, 6]. Each HDC was modeled to spike at specific preferred directions separated by 60°, BVCs were modeled to spike at boundaries of cardinal directions and motor neurons were modeled to spike with Gaussian white noise as a background noise. We also built a virtual rat that navigated within a 1 m × 1 m environment whose trajectory was controlled by spikes of motor neurons receiving synaptic inputs from: (1) HDCs, (2) BVCs and (3) both HDCs and BVCs. Number of obstacle detection (detection number: DN) and the time spent during obstacle collision (collision time: CT) were analyzed to compare the efficiency of neural network for OA.

We first verified that our neuromorphic HDC and BVC models could mimic the experimentally recorded electrophysiological properties in vitro and in vivo: HDCs reached maximum firing rate at each preferred direction, and BVCs increased their firing rate as the virtual rat approached boundaries. Using such neuromorphic HDC and BVC models, we investigated the roles of HDC and BVC in neural network for OA. Firstly, we performed the control simulation where virtual rat was controlled with neural network without neither HDC nor BVC and observed that DN was 40 and CT was 137 s. When HDC was added to the neural network, the result was similar to control simulation, (DN = 38 and CT = 139 s), indicating HDC alone cannot perform OA efficiently. When BVC alone was included in the neural network, DN substantially increased and CT decreased compared to control model (DN = 110 and CT = 73 s), indicating that OA efficiency increased. Finally, when both HDC and BVC were included in the neural network, the OA performance was most efficient (DN = 139 and CT = 39 s). These results suggest that our neural network model composed of neuromorphic HDC and BVC neurons can successfully perform OA even with background noise. Therefore, here we suggest the bioinspired neural network that consists of neuromorphic computational model of HDC and BVC could serve as a new approach to build an efficient OA system.

**Acknowledgements:** This study was supported by the Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Science, ICT and Future Planning (NRF-2013R1A1A2053280).

## References

1. Zohaib M, Pasha M, Riaz R, Javaid N, Ilahi M, Khan R. Control strategies for mobile robot with obstacle avoidance. J Basic Appl Sci Res. 2013;3(4):1027-36.

2. Vorhees CV, Williams MT. Assessing spatial learning and memory in rodents. ILAR J. 2014;55(2):310-32.

3. Taube JS, Muller RU, Ranck JB Jr. Head-direction cells recorded from the postsubiculum in freely moving rats. I. Description and quantitative analysis. J Neurosci. 1990;10(2):420-35.

4. Lever C, Burton S, Jeewajee A, O'Keefe J, Burgess N: Boundary vector cells in the subiculum of the hippocampal formation. J Neurosci. 2009;29(31):9771-77.

5. Yoder RM, Taube JS. Projections to the anterodorsal thalamus and lateral mammillary nuclei arise from different cell populations within the postsubiculum: implications for the control of head direction cells. Hippocampus. 2011;21(10):1062-73.

6. Menendez de la Prida L, Suarez F, Pozo MA. Electrophysiological and morphological diversity of neurons from the rat subicular complex in vitro. Hippocampus. 2003;13(6):728-44.

## P117

### Dynamic gating of spike pattern propagation by Hebbian and anti-Hebbian spike timing-dependent plasticity in excitatory feedforward network model

Hyun Jae Jang¹, Jeehyun Kwag¹

Department of Brain and Cognitive Engineering, Korea University, Seoul, Korea

**Correspondence:** Jeehyun Kwag - jkwag@korea.ac.kr

BMC Neuroscience 2016, 17(Suppl 1):P117

Precise timings of spikes within in vivo spike train are believed to carry information critical for neural computation [1]. For such neural

information to be effective, temporal patterns of spike train should be able to propagate across multiple neuronal layers in the feedforward network (FFN) of the brain without dissipation [2]. To support such reliable propagation of spike patterns, preferential and selective strengthening of synaptic pathways through which the spike patterns are routed may be necessary. Asymmetric Hebbian and anti-Hebbian spike timing-dependent plasticity (STDP), where synaptic strengths are strengthened or weakened depending on the precise relative timing and the order between pre- and postsynaptic spikes [3, 4], may serve as a good candidate for dynamically routing the propagation of spike patterns.

Hence, we investigated the role of Hebbian and anti-Hebbian STDP in spike pattern propagation using a six-layered FFN model composed of 200 Hodgkin-Huxley excitatory neurons in each layer. Asymmetric and symmetric/Hebbian and anti-Hebbian STDP were modeled at excitatory synapses using exponential functions [5]. In vivo spike train obtained from public database (crcns.org) was used as an input spike pattern ($T_{IN}$) in layer 1, which was simulated in a small subset of excitatory neurons in layer 1 of FFN model, while the rest were made to spontaneously spike with spike frequencies showing log-normal distribution to mimic in vivo background noise. The propagation of temporal spike pattern was quantified by analyzing the similarity ratio (SR) between $T_{IN}$ and output spike pattern in layer 6 ($T_{OUT}$), which calculates how instantaneous inter-spike intervals of $T_{IN}$ and $T_{OUT}$ are similar. In FFN model without STDP, the spike pattern of $T_{IN}$ in layer 1 became dissipated in noise as it propagated across layers, and consequently failed to preserve its spike pattern to layer 6 with low SR (0.49). When asymmetric anti-Hebbian STDP was included in FFN model, $T_{IN}$ also failed to propagate to layer 6 with low SR (0.17). However, in the presence of asymmetric Hebbian STDP, $T_{IN}$ successfully propagated to the final layer with high SR (0.87), indicating that asymmetric Hebbian STDP preferentially enhanced $T_{IN}$ propagation in FFN model. Further analysis revealed that asymmetric Hebbian STDP selectively strengthened the synaptic weights of the synaptic pathways routing $T_{IN}$ while it weakened the synaptic weights of that routing noise, effectively serving as an open-gate for propagating $T_{IN}$. In contrast, asymmetric anti-Hebbian STDP curve selectively weakened the synaptic weights of the synaptic pathways routing $T_{IN}$ serving as a close-gate for propagating $T_{IN}$. We also tested the effect of symmetric Hebbian STDP which induces only LTP or symmetric anti-Hebbian STDP which induces only LTD, and found that both types of symmetric STDP failed to propagate $T_{IN}$ with low SR (symmetric Hebbian = 0.23, symmetric anti-Hebbian = 0.14). Our results demonstrate that only asymmetric Hebbian STDP facilitates the reliable propagation of in vivo temporal pattern while asymmetric and symmetric anti-Hebbian STDP blocks temporal pattern propagation, suggesting that different types of STDP may dynamically gate the propagation of neural information.

**Acknowledgements:** This study was supported by Human Frontier Science Program (RGY0073/2015) and the Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Science, ICT and Future Planning (NRF-2013R1A1A2053280).

## References

1. Mainen ZF, Sejnowski TJ. Reliability of spike timing in neocortical neurons. Science. 1995;268(5216):1503-6.

2. Kumar A, Rotter S, Aertsen A. Spiking activity propagation in neuronal networks: reconciling different perspectives on neural coding. Nat Rev Neurosci. 2010;11(9):615-27.

3. Bi GQ, Poo MM. Synaptic modifications in cultured hippocampal neurons: dependence on spike timing, synaptic strength, and postsynaptic cell type. J Neurosci. 1998;18(24):10464-72.

4. Feldman DE. The spike-timing dependence of plasticity. Neuron. 2012;75(4):556-71.

5. Song S, Miller KD, Abbott LF. Competitive Hebbian learning through spike-timing-dependent synaptic plasticity. Nat Neurosci. 2000;3(9):919-26.
P118

**Inferring characteristics of input correlations of cells exhibiting
up-down state transitions in the rat striatum**

Marko Filipović¹,², Ramon Reig³, Ad Aertsen¹,², Gilad Silberberg⁴, Arvind Kumar¹,⁵

¹Bernstein Center Freiburg, Freiburg, Germany; ²Faculty of Biology, University of Freiburg, Freiburg, 79104, Germany; ³Instituto de Neurociencias de Alicante, University of Alicante, Alicante, Spain; ⁴Department of Neuroscience, Karolinska Institute, Stockholm, 17177, Sweden; ⁵Department of Computational Science and Technology, School of Computer Science and Communication, KTH Royal Institute of Technology, Stockholm, 10040, Sweden

**Correspondence:** Marko Filipović - marko.filipovic@bcf.uni-freiburg

BMC Neuroscience 2016, 17(Suppl 1):P118

Rat striatal projection neurons (SPNs) recorded under ketamine anes-
thesia exhibit slow oscillations, with transitions between depolar-
ized and hyperpolarized membrane potential also referred to as up
and down states, respectively. It is presumed that the activity during
hyperpolarized down-states is determined by intracellular processes,
whereas the large membrane voltage fluctuations during up-states
are a product of increased synaptic input. Because local striatal activity
during an up-state is weak, the statistics of the up-state fluctuations
mainly reflect cortical feedforward input to the SPNs.

To infer the statistics of the cortical input to SPNs we measured the statistics and spectrum of the membrane potential of SPNs in up and down states. The spectrum of the membrane potential reflects the filtering properties of the membrane and can be used to estimate the effective time constant (τ<sub>eff</sub>) of the neuron. Our analysis showed that SPNs have significantly smaller τ<sub>eff</sub> in the up-state than in the down-state, consistent with the assumption that the barrage of synaptic input causes an increase in membrane conductance during the up state. However, this observation is inconsistent with the idea that depolarization of SPNs should increase the membrane time constant because of the closing of some of the voltage dependent ion channels (e.g. the Kir) channels [1].

The mean (μ<sub>up</sub>) and variance (σ<sub>up</sub>) of the membrane potential during up states varied in a correlated manner. At the same time, for a given SPN, μ<sub>up</sub> and σ<sub>up</sub> of individual up-state membrane potentials were highly variable across different up states, indicating a corresponding variability in the cortical inputs. Using a point neuron model of an SPN, we show that the correlation and variability of the up-state mean and variance could be explained if we assume that SPNs receive correlated inputs.

Across different SPNs, each recorded in a different animal, we observed a high variability in the correlation (ρ) between μ<sub>up</sub> and σ<sub>up</sub>. This variability could arise from the heterogeneity in the neuron morphology, intracellular properties, conductance state of the neurons, synaptic weights and the input rate and correlations. Using a point neuron model we tested the dependence of ρ on each of these properties. Our analysis showed that the variability of the correlation between μ<sub>up</sub> and σ<sub>up</sub> arises because of the diversity of synaptic weights and input correlations, and not because of intrinsic properties of SPNs. This suggests that neuronal heterogeneity could be obscured by the statistics of the synaptic inputs and synaptic weights.

In summary, our analysis of up-down states allows us to make general inferences about characteristics of correlated synaptic input, such as strength of correlations and input firing rate, solely based on mem-
brane potential recordings of SPNs exhibiting up and down states.

**Acknowledgements:** This work was supported in parts by the Erasmus Mundus Joint Doctorate Programme EUROSPIN (MF), an ERC starting grant (GS), the Knut and Alice Wallenberg Academy Fellowship, the Karolinska Institutet Strategic Research program in Neuroscience (StratNeuro; GS, AK), and the Swedish Medical Research Council (GS).

**Reference**

1. Nisenbaum ES, Wilson CJ. Potassium currents responsible for inward and outward rectification in rat neostriatal spiny projection neurons. J Neurosci. 1995, 15: 4449–63.

P119

**Graph properties of the functional connected brain under the**
**influence of Alzheimer's disease**

Claudia Bachmann¹, Simone Buttler¹, Heidi Jacobs²,³,⁴, Kim Dillen⁵, Gereon R Fink⁵,⁶, Juraj Kukolja⁵,⁶, Abigail Morrison¹,⁷,⁸

¹Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA BRAIN Institute I, Jülich Research Centre, Jülich, Germany; ²Faculty of Health, Medicine and Life Science, School for Mental Health and Neuroscience (MHENS), Alzheimer Centre Limburg, Maastricht University Medical Centre, PO Box 616, 6200 MD Maastricht, The Netherlands; ³Department of Radiology & Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA; ⁴Faculty of Psychology and Neuroscience, Department of Cognitive Neuroscience, Maastricht University, PO BOX 616, 6200 MD Maastricht, The Netherlands; ⁵Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Jülich Research Centre, Jülich, Germany; ⁶Department of Neurology, University Hospital of Cologne, Cologne, Germany; ⁷Computational Neuroscience, Bernstein Center Freiburg, Freiburg, 79104, Germany; ⁸Institute of Cognitive Neuroscience, Faculty of Psychology, Ruhr-University Bochum, 44801 Bochum, Germany

Correspondence: Claudia Bachmann - c.bachmann@fz-juelich.de

BMC Neuroscience 2016, 17(Suppl 1):P119

Diagnosing Alzheimer's disease (AD), especially in the early stage, is costly and burdensome for the patients, since it comprises a battery of psychological tests and an extraction of disease specific biomarkers from the cerebrospinal fluid. A cheaper and more convenient procedure would be a diagnosis based on images obtained through fMRI. Based on previous polymodal studies demonstrating disrupted inter- and intra-cortical connectivity in AD [1], we argue that the functional connectivity of the whole cortex might be a good predictor for the cause of the disease. In resting state fMRI, previous attempts to analyze graph properties of whole brain networks contradict each other [2]. In our opinion there are two general critical points in the methodology of these studies that are likely to contribute to the variability of the results. First, we criticize that the activities of the brain areas (graph nodes) that are used to calculate the functional connectivities (weights of the graph edges) are composed of functionally inhomogeneous signals, as individual brains are often mapped onto a standard atlas brain of known functional coherent areas [2, 3]. The second problem consists in converting the resulting weighted graphs into simple graphs, by setting weights above an arbitrary threshold w<sub>min</sub> to 1, and those below it to 0 [2]. The drawback here is that there is no validation for an optimal threshold, and information that might be relevant in AD may be lost. In this work we address the first problem by applying an activity-driven, region-growing clustering algorithm derived from image processing [4]. In order to guarantee functionally homogeneous clusters, the threshold for inclusion of a voxel in a region is regulated by a heterogeneity criterion [3]. Applying this algorithm, we end up with undirected weighted graphs with varying numbers of nodes for three sets of data: healthy elderly controls, mild cognitive impairment and Alzheimer's disease. Targeting the second problem, we analyze the dependence of graph theoretic measures (shortest path length, in- and out-degree distribution, clustering coefficient, modularity and minimal spanning tree [5]) on w<sub>min</sub>. Finally, we investigate the distribution of these measures for each data set to determine candidates for a predictive measure.

**Acknowledgements:** We acknowledge partial support by the Helm-
holtz Alliance through the Initiative and Networking Fund of the
Helmholtz Association and the Helmholtz Portfolio theme "Supercom-
puting and Modeling for the Human Brain".

**References**

1. Bokde AL, Ewers M, Hampel H: Assessing neuronal networks: understanding Alzheimer's disease. Prog Neurobiol. 2009;89:125-33.

2. Tijms BM, Wink AM, de Haan W, van der Flier WM, Stam CJ, Scheltens P, Barkhof F. Neurobiol: Alzheimer's disease: connecting findings from graph theoretical studies of brain networks. Aging. 2013;34: 2023-36.
3. Marrelec G, Fransson P. Assessing the influence of different ROI selection strategies on functional connectivity analyses of fMRI data acquired during steady-state conditions. PLoS One. 2011;6(4):e14788.

4. Lu Y, Jiang T, Zang Y. Region growing method for the analysis of functional MRI data. *Neuroimage*. 2003;20(1):455–65.

5. Wang J, Zuo X, Dai Z, Xia M, Zhao Z, Zhao X, Jia J, Han Y, He Y. Disrupted functional brain connectome in individuals at risk for Alzheimer's disease. *Biol Psychiatry*. 2013;73(5):472–81.

P120

Learning sparse representations in the olfactory bulb

Daniel Kepple<fnref n="1" />, Hamza Giaffar<fnref n="1" />, Dima Rinberg<fnref n="2" />, Steven Shea<fnref n="1" />, Alex Koulakov<fnref n="1" />

In mouse olfaction, olfactory receptor responses are aggregated in spherical structures of the main olfactory bulb (MOB) called 'glomeruli'. These signals are then received by mitral cells (MC) and communicated to the cortex. MC responses are modified by local inhibitory interneurons called granule cells GC, which vastly outnumbered MCs (50fold-100fold). In our previous work [1], we proposed a model in which GCs inhibit MC to form a sparse, incomplete representation (SIR) of odors (Fig. 67). Here, we reason that since sparse representations are efficient, sparseness may increase with learning. We extend the SIR model to allow network synaptic weights to be adjusted, increasing representational sparseness and increasing stimulus discriminability. We derive learning rules for dendrodendritic connectivity between GCs and MCs and also for centrifugal cortico-granule synapses. We computationally test these learning rules and make several predictions of GC and MC plasticity. Specifically, we predict that a minority of GCs outcompete the rest of the population to generate a negative image of a learned odor. Additionally, we predict that participation of the GC network will confer combination selectivity and the ability to discriminate overlapping input patterns. Finally, we experimentally validate these predictions for the dynamics of GCs during locus coeruleus-induced MOB plasticity.

Reference

1. Koulakov AA, Rinberg D. Sparse incomplete representations: a potential role of olfactory granule cells. Neuron. 2011;72(1):124–36

<figure><img src="image_73.png" /><figcaption>**Fig. 67** Sparse incomplete representations (SIR). In our previously formulated model of the main olfactory bulb network [1], MCs receive inputs from receptor neurons in the glomeruli (black circles) and interact with GCs through dendrodendritic synapses. GCs build representations of MC glomerular inputs (red arrows). The representations are contained in the inhibitory inputs returned by the GCs to the MCs (blue arrows). Because GCs inhibit each other through second-order inhibitory interactions, only a few GCs respond to an odorant (full blue circles with a dendrite shown). The vast majority of GCs do not change their firing rate in response to an odorant (empty circles). Thus, the responses of GCs are sparse. Because some MCs manage to retain the responses to odorants, the representation by GCs is called *incomplete*. According to this model, MCs transmit to higher areas the errors in the GC representation</figcaption></figure>

P121

Functional classification of homologous basal-ganglia networks

Jyotika Bahuguna<fnref n="1" />,<fnref n="2" />,⁵, Tom Tetzlaff<fnref n="1" />, Abigail Morrison<fnref n="1" />,<fnref n="2" />, Arvind Kumar<fnref n="2" />,³, Jeanette Hellgren Kotaleski³

¹Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6) and JARA BRAIN Institute, Jülich Research Centre, Jülich, Germany; ²Computational Neuroscience, Bernstein Center Freiburg, Freiburg, 79104, Germany; ³Computational Brain Science, Department of Computational Science and Technology, School of Computer Science and Communication, KTH, Royal Institute of Technology, Stockholm, Sweden

**Correspondence:** Jyotika Bahuguna - j.bahuguna@fz-juelich.de

BMC Neuroscience 2016, 17(Suppl 1):P121

The basal ganglia (BG) are a set of nuclei that play an important role in motor and cognitive functions. Indeed many brain diseases such as Parkinson's disease (PD) can be attributed to dysfunction of one or more BG nuclei. The classical model of basal ganglia has been regularly updated with discoveries of new sub-populations within a nucleus or new projections from existing nuclei in recent years. It is unclear how these new insights on the structure of the BG network foster our understanding of its function. The effective connectivities among these recently identified BG sub-populations are only partially known. In the framework of a simple firing-rate model subjected to a genetic algorithm, we identified effective BG connectivities which are consistent with experimentally established firing-rate and phase relationships in Subthalamic Nucleus (STN) and two GPe subpopulations (arkypallidal [GPe-TA] and prototypical [GPe-TI]) in both healthy and PD states [1]. This is in extension to an earlier model that identified effective connectivities for the STN-TA-TI-sub circuit [2].

As expected, we found that multiple parameter combinations can fit the data [1]. We re-classified these homologous networks that reproduced the healthy and PD state, on the basis of two dynamical features: suppression of Gpi activity and susceptibility of the BG network to oscillate in the presence of cortical input. These features were chosen because task execution requires Gpi suppression while oscillations in the STN-GPe subnetwork are characteristic of PD. We found that most putative pathological networks showed insufficient suppression of Gpi activity and high susceptibility to oscillations whereas most putative healthy networks showed sufficient suppression of Gpi activity and low susceptibility to oscillations. This is consistent with experimental data that shows that lack of Gpi suppression [3] or oscillations [4, 5] is correlated with Parkinsonian symptoms such as stymied movement and tremor. A small fraction of networks, however, in both cases show deficiency in only one of the features. This could indicate the configurations of healthy networks that might be more pathology prone and in contrast configurations of pathological networks that might be easier to push into a healthy state. Further analysis of estimated BG connectivity revealed that transitions between the putative PD and healthy networks were possible by modifying the strength of the relevant projections. Most of the transitions involved changes in corticostriatal, striatopallidal and pallidopallidal projections. Finally, the variance observed in the functional classification of putative pathological and healthy networks might hint at the variance observed in manifestation of Parkinson's disease (PD).

**Acknowledgements:** Klinische Forschergruppe (KFO219, TP12) of the Deutsche Forschungsgemeinschaft; Helmholtz Association, EuroSPIN and Erasmus Mundus Joint Doctorate Programme.

References

1. Mallet A, Pogosyan A, Márton LF, Bolam JP, Brown P, Magill PJ. Parkinsonian beta oscillations in the external globus pallidus and their relationship with subthalamic nucleus activity. J Neurosci. 2008;28(52):14245–58.

2. Nevado-Holgado AJ, Mallet N, Magill PJ, Bogacz R. Effective connectivity of the subthalamic nucleus-globus pallidus network during Parkinsonian oscillations. J. Physiol. 2014;592(7):1429–55.

3. Boraud T, Bezard E, Bioulac B, Gross CE. Ratio of inhibited-to-activated pallid neurons decreases dramatically during passive limb movement in the MPTP-treated monkey. J Physiol. 2000;83(3):1760–63.

<footnote n="1">¹Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA;</footnote>

<footnote n="2">²NYU Neuroscience Institute, New York, NY 10016, USA</footnote>

<footnote>**Correspondence:** Daniel Kepple - akula@cshl.edu</footnote>

<footnote>BMC Neuroscience 2016, 17(Suppl 1):P120</footnote>
4. Chen CC, Litvak V, Gilbertson T, Kühn A, Lu CS, Lee ST, Tsai CH, Tisch S, Limousin P, Hariz M, Brown P. Excessive synchronization of basal ganglia neurons at 20 Hz slows movement in Parkinson's disease. Exp Neurol. 2007;205(1):214-21.

5. Moran A, Bergman H, Israel Z, Bar-Gad I. Subthalamic nucleus functional organization revealed by parkinsonian neuronal oscillations and synchrony. Brain. 2008;131(Pt-12):3395-409.

P122

**Short term memory based on multistability**

Tim Kunze<sup>1,2</sup>, Andre Peterson<sup>3</sup>, Thomas Knösche<sup>1</sup>

<sup>1</sup>Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; <sup>2</sup>Institute of Biomedical Engineering and Informatics, Ilmenau University of Technology, Ilmenau, Germany; <sup>3</sup>Department of Medicine, University of Melbourne, Melbourne, Australia

**Correspondence:** Tim Kunze - tkunze@cbs.mpg.de

BMC Neuroscience 2016, **17**(Suppl 1):P122

Neural circuits can be formally described by modeling the collective behavior of relatively homogeneous neural populations, so-called neural masses [1]. Some neural mass models consider the minimum set of one excitatory and one inhibitory subpopulation each. In contrast, three-population models make a distinction between excitatory pyramidal cells (PC), projecting to distant areas, and excitatory interneurons (EIN), providing local feedback. We investigate a three-population neural mass model, driven by input to the EIN, with respect to its input/output behavior. We find that such a circuit exhibits, for sufficiently salient inputs, a memory effect based on multi-stability. Furthermore, we test the hypothesis that this mechanism essentially depends on the separation between input and output neurons and is thus not captured in the simpler two-population model.

We use a neural mass model [2], where a pyramidal cell subpopulation receives negative feedback from an inhibitory interneuron subpopulation and positive feedback either directly through self-connections or indirectly via a secondary excitatory subpopulation of interneurons. The respective feedback topology of interest (including which subpopulation is targeted by external input) is controlled by a single parameter. We systematically applied transient sensory inputs, modeled by pulses of various magnitude and duration, as external inputs to the EIN and monitored the behavior of the PC.

Depending on the duration and intensity of the applied stimuli (see Fig. 68A), the output either transiently follows the input (i) or it jumps to a more depolarized state, where it remains oscillating with a higher mean membrane potential even after the stimulus has ceased (ii) and where further input does not effect the output any more (iii). This state can be terminated by an impulse to the inhibitory interneurons (iv). The accessibility of this memory effect depends on the saliency of the stimulus in terms of duration and intensity (see Fig. 68B) and disappears in case of direct feedback in a structurally similar two population model.

The identified short-term memory mechanism would be important for temporal integration in cortical processing, potentially applicable in predictive coding schemes. The distinction between the input receiving excitatory subpopulation and the output sending excitatory subpopulation appears to be crucial for the described mechanism, which is further modulated by inhibitory feedback. The further examination of the ratio between excitation and inhibition, governing this mechanism, thus represents an important step to elucidate how the topology between excitatory and inhibitory neural populations affects emerging dynamics on a mesoscopic scale with potential effects on brain states and higher-order brain functionality.

**References**

1. Freeman WJ. Mass action in the nervous system. New York: Academic Press; 1975.

2. Spiegler A, Kiebel SJ, Atay FM, Knösche TR. Bifurcation analysis of neural mass models: Impact of extrinsic inputs and dendritic time constants. Neurolimage. 2010;52:1041-58.

<figure><img src="image_74.png" /></figure>

<figure><img src="image_75.png" /><figcaption>**Fig. 68** A Response of pyramidal cells to transient input to excitatory interneurons shows different modes. B Depending on intensity and duration of the stimulus</figcaption></figure>

P123

**A physiologically plausible, computationally efficient model and simulation software for mammalian motor units**

Minjung Kim<sup>1</sup>, Hojeong Kim<sup>1</sup>

<sup>1</sup>Division of IoT and Robotics Convergence Research, DGIST, Daegu, 42988, Korea

**Correspondence:** Hojeong Kim - hojeong.kim03@gmail.com

BMC Neuroscience 2016, **17**(Suppl 1):P123

**Background** A spinal motoneuron contacts a bunch of muscle fibers forming a motor unit that underlies all mammalian movements. The essential role of the motor unit is the transduction of synaptic inputs from descending and reflex pathways into muscle force. Since the input-output properties of both motoneurons and muscle fibers are non-linear, it has been difficult to make predictions on how changes in synaptic inputs to motoneuron, cellular properties of the motoneuron and muscle fibers and muscle length may affect motor output [1].

**Methods** To tackle this fundamental issue in the field of motor neuroscience, we developed a physiologically plausible but computationally efficient model of the motor unit and a software package that allows for virtual experiments on the input-output properties of the motor unit over a full range of physiological inputs and biophysical parameters.

**Results** The computational model of motor unit was first built in this study coupling the motoneuron model and the muscle unit model with a simplified axon model. The motoneuron model was developed using the recently reported two-compartment modeling approach [2]. The key feature of the new reduced motoneuron model is that all cable parameters of the reduced model are analytically determined based on the system properties such as input resistance, membrane time constant and electrical coupling properties between the soma and the dendrites, which are all empirically measurable from real motoneurons.

For the muscle unit, the recently developed muscle modeling approach was employed that consists of three sub-modules representing [3]: (1) the transformation of the spike signals from motoneurons
into the dynamics of calcium concentration in the sarcoplasm, (2) the conversion of the calcium concentration to the muscle activation level, and (3) the transformation of the muscle activation level into the muscle force using Hill-type muscle mechanics. The new muscle model was constructed in this study to reflect all experimentally identified dependencies of muscle activation dynamics on muscle length and movement over a full range of stimulation frequencies in cat soleus muscles.

Then, to enhance the usability and extendibility the software package for simulating and analyzing the developed motor unit model was designed and implemented based on the object-oriented programming paradigm and open source Python language along with graphic user interfaces (GUI). The software package developed in this study provides a GUI-based simulation environment in which a single motor neuron, muscle unit, and motor unit can be individually simulated and analyzed in a wide range of experimental conditions reflecting biological realisms.

**Conclusions** Our model of the motor unit and user-friendly simulation software may provide not only a computational framework to gain systemic insights into motor control by the central nervous system in a cellular perspective but also a basis on which to build biologically realistic large-scale neuro-musculo-skeletal models.

**Acknowledgements:** This work was supported by the DGIST R&D Program of the Ministry of Science, ICT and Future Planning of Korea (15-RS-02 and 16-RS-02).

## References

1. Heckman CJ, Enoka RM: Motor unit. Compr Physiol. 2012;2(4):2629-82.

2. Kim H, Jones KE, Heckman CJ. Asymmetry in signal propagation between the soma and dendrites plays a key role in determining dendritic excitability in motoneurons. PLoS One. 2014;9(8):e95454.

3. Kim H, Sandercock TG, Heckman CJ. An action potential-driven model of soleus muscle activation dynamics for locomotor-like movements. J Neural Eng. 2015;12(4).

## P125

### Decoding laser-induced somatosensory information from EEG

Ji Sung Park¹, Ji Won Yeon, Sung-Phil Kim¹

¹Department of Human Factors Engineering, Ulsan National Institute of Science and Technology, Ulsan 689-798, South Korea

**Correspondence:** Sung-Phil Kim - spkim@unist.ac.kr

BMC Neuroscience 2016, 17(Suppl 1):P125

Recently, our research group has proposed a new way of providing a non-nociceptive tactile sensation with laser [1]. In this study, we aimed to investigate laser-induced somatosensory information represented in cortical activity using the human EEG. The EEG data were acquired using the V-Amp amplifier (Brain Products GmbH, Gilching, Germany) with 16 wet electrodes that were placed on the scalp following the international 10-20 system. Twenty one subjects participated in the study (7 female and mean age of 22.4 years). During the experiment, a mechanical stimulus, a laser stimulus and a heat stimulus were given in a random order to subjects sixty times per stimulus. Subjects described the feeling of laser stimulation as non-painful sensation, painful sensation and no sensation. As described in the previous study, 56.3, 12.3 and 31.4 % of the subjects reported laser stimulation as non-painful, painful and no sensation, respectively [1]. To examine similarity of cortical activity in response to different stimuli, we employed a decoding analysis of the EEG data. In the decoding analysis, we used the linear discriminant analysis (LDA) method to classify the beta (21–28 Hz) event-related desynchronization/synchronization (ERD/S) patterns of EEG into one of the two classes representing every pair of stimuli (a total of six pairs from four stimuli) [2]. Classification error indicated how similar beta ERD/S patterns were between two stimuli: a larger error reflected more difficulty in discriminating patterns and consequently a greater similarity between patterns. The beta ERD/S patterns were estimated using the short time Fourier transform. Baseline correction was implemented using the 0.5 s period before stimulus onset. For each pair of stimuli, one-way ANOVA was used to

select four channels that exhibited the most differences in beta ERD/S patterns between classes and classification accuracy was assessed by the leave-four-out cross validation [3] (see Fig. 69 for the classification error between every stimulus pair). The classification results showed that to the beta ERD/S pattern induced by mechanical stimulation, the pattern by non-painful laser stimulation was most similar. Also, the results indicated closeness of cortical activities between non-painful and painful laser stimulations as well as painful laser and thermal stimulations (see Fig. 69). These results suggest that laser might induce similar beta responses whether it evoked painful or non-painful feelings but non-painful laser might share presumably non-nociceptive somatosensory information with mechanical stimulation whereas painful laser shared presumably nociceptive somatosensory information with thermal stimulation. We expect that further information theoretical analyses may reveal more details about somatosensory information encoded in cortical rhythms induced by laser.

## References

1. Jun J-H, Park J-R, Kim S-P, Bae YM, Park J-Y, Kim H-S, Choi S, Jung SJ, Park SH, Yeom D-I. Laser-induced thermoelastic effects can evoke tactile sensations. Sci Rep. 2015;5.

2. Pfurtscheller G, Lopes Da Silva FH. Event-related EEG/MEG synchronization and desynchronization: basic principles. Clin Neurophysiol. 1999;110(11):1842-57.

3. Celisse A, Robin S. Nonparametric density estimation by exact leave-p-out cross-validation. Comput Stat Data Anal. 2008;52(5):2350-68.

## P126

### Phase synchronization of alpha activity for EEG-based personal authentication

Jae-Hwan Kang¹, Chungho Lee¹, Sung-Phil Kim¹

¹Department of Human and Systems Engineering, Ulsan National Institute of Science and Technology, Ulsan

**Correspondence:** Sung-Phil Kim - spkim@unist.ac.kr

BMC Neuroscience 2016, 17(Suppl 1):P126

There has been a growing interest in the EEG-based biometric system as an alternative approach to personal authentication (PA). In this study, we focused on the potentialities of functional connectivity, especially phase synchronization in the alpha rhythm represented by the phase locking value (PLV) as a novel EEG signature for PA. We analyzed an EEG dataset of 39 trials from 7 subjects who participated in the 5-7 sessions repeatedly on different days. In the sessions, a total of 16 EEG signals were acquired by a portable EEG device, when the subjects were in a resting state with their eyes closed for 2 min. The

<figure><img src="image_76.png" /><figcaption>**Fig. 69** Each node represents stimulation type and each edge means classification error rate. Length of edge shows similarity between a pair of stimulation</figcaption></figure>
<figure><img src="image_77.png" /><figcaption>**Fig. 70** Overall characteristics of alpha phase synchronization for PA. **A** The upper triangle of association matrix indicates the PLV calculated by grand mean phase coherence. The lower triangle of association matrix indicates its CI values in pairs. **B** Topographical connections with the 12 top-ranked CI from the lower triangle of a</figcaption></figure>

<figure><img src="image_78.png" /><figcaption>**Fig. 71** A Model with local (left) and long-range connections (right). **B, C** Averaged tract lengths and weights from 4 connectomes **B** Joint distribution, and **C** histogram of weighted lengths for intra- and inter-hemispheric links. **D** Sketch of the spatial delay structure. **E** Phase-lag distributions (top) and phase-lags between areas (bottom rows)</figcaption></figure>

characteristics of alpha phase synchronization were estimated by the following procedures. (1) The alpha rhythm was extracted from the EEG signals using a band-pass filter of 8–13 Hz. (2) We randomly selected 20 2-s time segments of the alpha rhythm and calculated the mean phase coherence [1] between channels within each time segment. From all possible pairs of 16 EEG channels, a total of 120 mean alpha phase coherence values were extracted. (3) From these mean alpha phase coherence values, we calculated a criteria index (CI) of each of them where the CI calculated the ratio of an inter-subject variability to an intra-subject variability, which was developed to discriminate critical EEG features for PA in our previous study [2]. (4) Using the mean alpha phase coherence and its CI values, we constructed the association matrix of phase coherence and extracted the 12 top-ranked CI connections (Fig. 70). The topographical result showed that there were apparently two functional connectivity networks of the alpha rhythm in the brain for PA. The first network was distributed over the anterior regions including the pre-frontal, frontal and left central regions. The second one was located in the posterior region covering the occipital region. It should be noted that two regions have been well known as main sources of the alpha rhythm from many studies on EEG alpha rhythms. Our results suggest an important role of the alpha rhythm in the EEG-based biometrics system.

**Acknowledgements:** This work was supported by Institute for Information and communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (R0190-15-2054, Development of Personal Identification Technology based on Biomedical Signals to Avoid Identity Theft).

## References

1. Mormann F, Lehnertz K, David P, Elger CE. Mean phase coherence as a measure for phase synchronization and its application to the EEG of epilepsy patients. Phys D. 2000;144:358–69.

2. Kang J-H, Lee C, Kim S-P. EEG Feature Selection and the use of Lyapunov exponents for EEG-based biometrics. In: IEEE international conference on biomedical and health informatics, Las Vegas, NV, USA; 2016. p. 1–4.

## P129

### Investigating phase-lags in sEEG data using spatially distributed time delays in a large-scale brain network model

Andreas Spiegler¹, Spase Petkoski¹,², Matias J. Palva³, Viktor K. Jirsa¹

¹INSERM UMR 1106 Institut de Neurosciences des Systèmes - Aix-Marseille Université, Marseille, France; ²Aix-Marseille Université, CNRS, ISM UMR 7287, 13288, Marseille, France; ³Neuroscience Center, University of Helsinki, Helsinki 00014, Finland

**Correspondence:** Andreas Spiegler - Andreas.Spiegler@univ-amu.fr

BMC Neuroscience 2016, 17(Suppl 1):P129

On a large scale, the brain appears as a network composed of white matter tracts connecting brain areas within and between cerebral hemispheres. The finite transmission speed delays the interaction of

areas via these pathways. The delays are on the same scale as the brain oscillates, that is, 10–250 ms [1], and have been suggested to play a role in the functional organization. One potential key mechanism is synchronization [2], which could explain the phase-lags of brain signals. In humans, stereotactical EEG (sEEG) revealed frequency-specific inter-areal synchronization often associated with nearly zero phase-lag, or with variable phase-lags between ±π.

With the advance of non-invasive imaging techniques, large-scale modeling of the entire brain has become feasible using realistic connectivity and time delays [3], Fig. 71A. From structural and diffusion MRI, we obtained human connectomes composed of the strength and length of connections among 68 cortical areas. We approximated the bimodal tract length distribution (Fig. 71B) by Dirac deltas (Fig. 71C). Intra-hemispheric connections are in the 1st, and inter-hemispheric ones are in the 2nd mode. The delay of a connection was determined from its length divided by the speed of 5 m/s. The Kuramoto phase oscillator described the activity in each area. The phase difference of areas was analyzed and compared with the map of inter-areal phase lags obtained from resting state sEEG of epileptic patients.

The model of fixed oscillators (e.g., $f = 20$ Hz) switched from global incoherence to alternating in- and anti-phase coherence with increasing coupling strength. Increasing the natural frequencies for constant coupling resulted in alternating switching from in- to anti-phase coherence, but also to incoherence. Intra-hemispheric links were in-phase (phase-lags ~0), and inter-hemispheric links were either in- or anti-phase (±π), see clusters in Fig. 71E. Links among areas of low in-strength (sum of all the weights for that node) showed flatly distributed phase-lags. For $f = 20$ Hz, we found the phase-lags in the sEEG in the regime of in- and anti-phase coherence in the model, Fig. 71E.

We demonstrated that it is not simply the connectivity strength that matters in oscillatory large-scale brain networks, but time delays are of equal importance. The spatial structure in the time delays is reflected in the clustering of phase-lags. The model captured the statistics of the phase-lags as observed in the experimental data. The phase-lag structure of links at $f = 20$ Hz is explained in the model by a spatial organization of in- and anti-phase coherence.

## References

1. Buzsáki G, Draguhn A. Neuronal oscillations in cortical networks. Science. 2004;304(5679):1926–29.

2. Varela F, Lachaux J, Rodriguez E, Martinerie J. The brainweb: phase synchronization and large-scale integration. Nat Rev Neurosci. 2001;2(4): 229–39.

3. Deco G, Jirsa V, McIntosh AR, Sporns O, Kötter R. Key role of coupling, delay, and noise in resting brain fluctuations. Proc Natl Acad Sci USA. 2009;106(25):10302–07.

## P130

### Epileptic seizures in the unfolding of a codimension-3 singularity

Maria L. Saggio¹, Silvan F. Siep¹, Andreas Spiegler¹, William C. Stacey², Christophe Bernard¹, Viktor K. Jirsa¹

¹INSERM UMR 1106 Institut de Neurosciences des Systèmes - Aix-Marseille Université, Marseille, France; ²Department of Neurology,
Seizures can arise under a variety of conditions. Despite this fact, there are invariant features resulting in a characteristic electrophysiological signature. Investigations of these universal properties lead to a classification of a planar description of point-cycle fast-slow bursters [1] and a taxonomy of seizures [2]. The phenomenological model, the epilep- tor [2], is able to reproduce the main features of the predominant class of human seizure (~80 % of all cases), according to data from epileptic patients. We aim at generalizing this model to include other bursting classes of the taxonomy.

We extended the work by [3] on bursters, living in the unfolding of high codimension singularities, and systematically investigated the unfolding of the codimension-3 degenerate Takens-Bogdanov bifur- cation (focus, elliptic, saddle and cusp cases) [4]. The biological rele- vance of this codimension-3 bifurcation has been highlighted by other authors, and several classes appearing in the context of neuronal spiking have been identified in its unfolding (e.g., [5]). However, a sys- tematic search in this unfolding for all the planar point-cycle bursting classes predicted by [1] was still missing. The existence of 16 bursters of the type slow-wave (self-oscillating slow subsystem), and 16 of type hysteresis-loop (slow-subsystem oscillating thanks to feedback from the fast one) was predicted.

We could find all slow-wave bursters in the unfolding together with seven of the hysteresis-loop ones. With regard to these hysteresis-loop bursters, we propose a model able to reproduce each of them depend- ing only on the initial and final points of the path in the unfolding's parameter space. This model is based on the known normal form of the codimension-three bifurcation [4], therefore we can readily describe the role of all its variables and how the tuning of its parameters affects the models activity. We found that the codimension-three model incor- porates not only the repertoire (80 % of seizure) of the model proposed by [2] but also the classes that account for the remaining 20 % of sei- zures. Moreover, based on an ultra-slow modulation of the bursting path (see also [6]) in the model, possible transitions between bursting classes and, more importantly, transitions to regimes (in the parameter space) where bursting behavior is not possible at all could be predicted. These predictions could be tested using data from epileptic patients for whom different types of seizures coexist.

Overall, the main points of the present work are threefold: (i) a model description comprising the complete set of slow-wave bursters and seven (out of 16) hysteresis-loop bursters predicted by Izhikevich [1], (ii) a generalization of the model proposed by [2] to include the missing seizure types found in human data and to make prediction about their robustness, (iii) a framework to investigate the coexist- ence of different seizure types in the same patient and the transi- tions between them. The possibility of describing different seizure types with a unique model, thus with a unique set of variables and parameters, will facilitate the search for physiological correlates and treatments.

References

1. Izhikevich EM. Neural excitability, spiking and bursting. JBC. 2000;10(6):1171–266.

2. Jirsa VK, Stacey WC, Quilichini PP, Ivanov AI, Bernard C. On the nature of seizure dynamics. Brain. 2014;137(8):2210–30.

3. Golubitsky M, Josic K, Kaper TJ. An unfolding theory approach to bursting in fast-slow systems. In: Krauskopf B, Broer HW, Vegt G, editors. Global analysis of dynamical systems; 2001. p. 227–308.

4. Dumortier F, Roussarie R, Sotomayor J, Žaladek H. Bifurcations of planar vector fields—nilpotent singularities and abelian integrals. Berlin: Springer; 1991.

5. Osinga HM, Sherman A, Tsaneva-Atanasova K. Cross-currents between biology and mathematics: the codi-mension of pseudo-plateau bursting. DCDS-A. 2012;32(8):2853–77.

6. Franci A, Drion G, Sepulchre R. Modeling the modulation of neu- ronal bursting: a singularity theory approach. SIAM J Appl Dyn Syst. 2014;13(2):798–829.

In our daily life, we encounter thousands of complex problems, which are not 'one dimensional'. However, multi-dimensional problems were known to suffer from "curse of dimensionality" [1]. Therefore, the researches of reward learning and goal-directed behavior were mostly focused on single dimensional environment for a decade [2]. Even a few researches on multi-dimensional tasks was emphasizing that human representation learning is done by reducing the dimensional- ity, but not focusing on multiple compositional reasoning under multi- dimensional environment [3].

Here, the multi-dimensional decision task was conducted (Fig. 72A, B) and the framework of Reinforcement Learning (RL) was used for analysis. We investigated that the reasoning under multi-dimensional environment is processed in incremental order, rather than one-shot learning. Also, the exploration of the best strategy occurs depends more on internal value, that is exploring under low value and exploit- ing under high value (softmax decision rule) rather than random exploration (randomized e-greedy algorithm). Functional MRI were taken on each subject, while conducting the behavioral task. Brain regions of the incremental learning and the value sensitive explorative behavior will be verified.

We demonstrated that incremental learning rule can explain the multidimensional reasoning process better than other models (Fig. 72C-E). This result indicate that people deal with the complicated multi-dimensional problem, we solve them by adding dimensional information one by one.

References

1. Sutton RS, Barto AG. Reinforcement learning: An introduction. MIT Press; 1998.

2. Niv Y, Daniel R, Geana A, Gershman SJ, Leong YC, Radulescu A, Wilson RC. Reinforcement learning in multidimensional environments relies on attention mechanisms. J Neurosci. 2015;8145–57.

3. Lee SW, Shimojo S, O'Doherty JP. Neural computations underlying arbitration between model-based and model-free learning. Neuron. 2014;687–99.

<figure><img src="image_79.png" /><figcaption>**Fig. 72** Multidimensional decision making task design and model comparison. **A** Multidimensional decision making task schematics. **B** Systemic structure of the task. **C** The result of model comparison, proposed model has significantly high accuracy on prediction. **D, E** The models' prediction accuracy of proposed model (**D**) and naive model (**E**)</figcaption></figure>

<figure><img src="image_80.png" /></figure>

<footnote n="2">²Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA</footnote>

<footnote>**Correspondence:** Maria L. Saggio - marisa.saggio@gmail.com</footnote>

<footnote>BMC Neuroscience 2016, 17(Suppl 1):P130</footnote>
P132

A low-cost model of eye movements and memory in personal visual cognition

Yong-il Lee¹,², Jaeseung Jeong¹,²

¹Department of Bio and Brain Engineering, College of Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, South Korea; ²Program of Brain and Cognitive Engineering, College of Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, South Korea

**Correspondence:** Jaeseung Jeong - jsjeong@kaist.ac.kr

BMC Neuroscience 2016, 17(Suppl 1):P132

Eye movements are the most useful and clearest signal of our body to understand cognitive process and memory mechanism. Because it is convenient to measure the stream signal and quantify. Other sensory inputs, like auditory, gustatory, olfactory, and kinesthetic stimuli, are hard to estimate, but visual stimuli are easy by the eye-tracker [1]. And people are primarily visually oriented. Every day, people get over 80 percent of information from their own eye.

Until now, eye movements data has been measured and analyzed by very expensive eye-trackers mostly. The major companies' devices price, including SMI, Tobii and EyeLink, are at least 10,000$ with their analysis tool SW. Of course, there have been many substitution trials in open SW and open HW area [2]. But their performance is certainly lower than major brands. Also, their data representation doesn't have standard. Therefore, open SWs are difficult to apply for other utility services and scientific researchers have hesitated to use them to analyze result data to understand complex cognitive process. A few research results, which is investigating the relation between eye movement and cognitive model based on open eye-tracker platform, have reported by this time.

However, eye-tracker is not only for science, and other areas need the usability of eye movement, for instance, UI/UX, healthcare, driving, game, learning consulting, TV viewer rating, market research and so on [3]. If there is a more general and low-cost eye-tracker which is confirmed cognitive model, above areas would be effective and we could do better decision making. This research implements a low-cost eye-tracker using a front camera (webcam) and a pin camera (Fig. 73. If the pc or laptop has laptop has a front camera, it doesn't need more pin camera). The implementation includes the auto detection and classifying of useful memory based on eye movement of visual information on the device's display. To do this function, the camera measures the saccade variation spectrum, as the X-Y axis acceleration, and categorize individual pattern while the user is taking train session. It is developed using OpenCV library and C#. XLabs Inc., already has made the gaze/head tracker using front camera without cognitive pattern analysis [4]. In the future, we will try this function on the mobile devices, which are cellular phone, tablet pc, and game interface. These devices have more sensors, like GPS, illumination, and activity accelerator. Combination of sensors input would make more precise prediction for memory cognition.

References

1. Wedel M, Pieters R: Eye tracking for visual marketing. Now Publishers Inc; 2008.

2. Dalmaijer ES, Mathôt S, Van der Stigchel S. PyGaze: an open-source, cross-platform toolbox for minimal-effort programming of eyetracking experiments. Behav Res Methods. 2014;46(4):913-21.

3. Pannasch S, Helmert JR, Velichkovsky BM. Eye tracking and usability research: an introduction to the special issue. J Eye Mov Res. 2008;2(4):1-4.

4. https://xlabsgaze.com/about/.

<figure><img src="image_81.png" /></figure>

P133

Complex network analysis of structural connectome of autism spectrum disorder patients

Su Hyun Kim¹,², Mir Jeong¹, Jaeseung Jeong¹,²

¹Department of Bio and Brain Engineering, College of Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Korea; ²Program of Brain and Cognitive Engineering, College of Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Korea

**Correspondence:** Jaeseung Jeong - jsjeong@kaist.ac.kr

BMC Neuroscience 2016, 17(Suppl 1):P133

**Background** Human connectome which is the map of full connection of neuronal network in the human brain exhibits the characteristics of complex network [1]. The human connectome is known to be wired in a way that neurons efficiently transmit and communicate information. Statistical measures of complex network describe the topological features of a certain network and this enables researchers to compare effectiveness of information processing within a network. Autism spectrum disorder (ASD) subjects exhibit repetitive behaviors, impaired social communication skills, and sensory problems. Those symptoms of neurodevelopmental disorder is doubted to be originated from genetic causes [2]. Also, recent investigations find that ASD is a 'connection problem'. But still exact cause of ASD is unknown. The aim of the research to be conducted is to reveal the genetic cause of (ASD) by the statistical analysis of network measures of ASD patients and normal groups' structural connectome data using diffusion tensor imaging (DTI).

**Methods** The method to be used in the research is to compare the network measure values of various subject groups' connectome and relate the difference to the genetic mutations in common. DTI data describes the neuronal connection of the brain regions in the mesoscale level. Structural connectome that is constructed from DTI information.

Expected result is that there are genotypic changes of genes which affect development of neuronal connection in ASD subjects. This finding will shade a new light on the investigation of ASD diagnosis and treatment.

References

1. Bullmore E, Sporns O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci. 2009;10(3):186-98.

2. Freitag CM, Staal W, Klauck SM, Duketis E, Waltes R. Genetics of autistic disorders: review and clinical implications. Eur Child Adolesc Psychiatry. 2010;19(3):169-78.

P134

Cognitive motives and the neural correlates underlying human social information transmission, gossip

Jeungmin Lee¹,², Jaehyung Kwon¹, Jerald D. Kralik¹,², Jaeseung Jeong¹,²

¹Department of Bio and Brain Engineering, College of Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea; ²Program of Brain Engineering, College of Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea

**Correspondence:** Jaeseung Jeong - jsjeong@kaist.ac.kr

BMC Neuroscience 2016, 17(Suppl 1):P134

Gossip is a specific example of human conversation containing social factors and has been considered as malicious, useless idle-talk by general population. Several researchers have suggested the role of gossip as social police that control the members of social groups to behave cooperative rather than selfish. However, there is not enough data to explain the actual cognitive motives that drive people to spread gossip. Throughout this study, human gossiping behavior is defined as transmission of social information about an absent third-party (i.e. the target of the gossip). In order to define the types of gossip, various scenarios containing social information are divided into 48 different categories by the third-party identity, valence and contents. Big five personality inventory, prosocial personality battery, cultural orientation scale and moral foundations questionnaire were used to
measure personal traits that may influence the gossiping behavior of individuals. We found out that people, regardless to the scores of their personal traits, tend to spread gossip about in-group and celebrities more than out-group members. We also found out that positive gossip about in-group members is spread with significantly higher rates than in-group negative gossip, whereas the spread pattern was the opposite when the gossip is about celebrities. With such findings, we conducted fMRI study using in-group and out-group gossip with positive and negative valence. Increased activity in various brain regions including medial frontal gyrus, dorsolateral prefrontal cortex and precuneus was found when participants made decisions whether or not to spread gossip. With the obtained data, we tried to construct a computational model that may be used for classification of spread gossip.

P135
EEG hyperscanning detects neural oscillation for the social interaction during the economic decision-making

Jaehwan Jahng¹,², Dong-Uk Hwang³, Jaeseung Jeong¹,²

¹Department of Bio and Brain Engineering, College of Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, South Korea; ²Program of Brain and Cognitive Engineering, College of Engineering, Korea Advanced Institute of Science and Technology

<figure><img src="image_82.png" /></figure>

**Fig. 74** A Brain synchrony analyses. Intra-brain and inter-brain phase synchronies in alpha band [0.5, 1] s. Links between electrodes means that the phase activities there are synchronized. All synchronies here were higher in FF groups than in FB groups (gray line). Blue line denotes the synchronies that were higher in CC epochs compared with DD epochs of FF groups (CC > DD) whereas red line denotes the synchronies that were higher in DD epochs compared with CC epochs of FF groups (CC < DD). Intra-brain synchronies are drawn in both brains and only one of each pair of inter-brain synchronies are drawn. Significant level was at *p* < 0.05, Bonferroni corrected. B Magnitudes of phase synchronies that showed significant strategical differences. These correspond to the links depicted as blue and red line in A. * *p* < 0.05; ** *p* < 0.01, Bonferroni corrected

(KAIST), Daejeon, 34141, South Korea; ³Division of Computational Mathematics, National Institute for Mathematical Sciences (NIMS), Daejeon, 34047, South Korea

**Correspondence:** Jaehwan Jahng - jahngjh.627@gmail.com

BMC Neuroscience 2016, 17(Suppl 1):P135

Social interaction is an important feature of the economic exchange. However, little is known about the varying neural mechanism during the economic decision-making depending on the different degrees of the social interaction. In this study, we used an iterated version of Prisoner's dilemma game (PDG) with an EEG hyperscanning to investigate how the presence of face-to-face interaction modulates the social interactions and in turn the aspects of an economic decision-making. Participants played the game either face-to-face (FF) or face-blocked (FB). On the behavioral level, face-to-face interaction led both participants to choose cooperative strategies more often. On the neural level, FF groups showed significantly different alpha power during the first 0.5 s after seeing each outcome compared with FB groups in right temporo-parietal region. By computing the phase locking value (PLV), we measured the brain synchrony and found that the inter-brain phase synchronies across right temporo-parietal area were significantly associated with both the group differences and strategical differences of both players (Fig. 74). These results suggest that inter-brain alpha synchronies across right temporo-parietal area might serve as an implicit neural marker for both the social interaction level and intention to either cooperate or defect. Moreover, our results warrant the future hyperscanning studies on the social interactions of autism spectrum disorder (ASD) patients as all neural substrates revealed are known to be deeply associated with their social traits.

**Acknowledgements:** This research was supported by the CHUNG Moon Soul Research Center for Bio Information and Bio Electronics (CMSC) in KAIST and a Korea Science and Engineering Foundation (KOSEF) grant funded by the Korean government (No. 2006-2005399). The funders had no role in study design, data collection and analysis, the decision to publish, or the preparation of the manuscript.

P136
Detecting purchase decision based on hyperfrontality of the EEG

Jae-Hyung Kwon¹,², Sang-Min Park¹,², Jaeseung Jeong¹,²

¹Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, South Korea;

²Program of Brain and Cognitive Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, South Korea

**Correspondence:** Jae-Hyung Kwon - jh2393@kaist.ac.kr

BMC Neuroscience 2016, 17(Suppl 1):P136

Understanding and predicting purchase decision process is one of the fundamental issues in economics, marketing, decision sciences, yet an easily accessible means to monitoring purchase decisions has not been developed yet [1–3]. Using event-related functional fMRI, such purchase behavior with a shopping task was investigated [4] but it has limit on potential practical uses due to the cost and portability of the MRI. The electroencephalogram (EEG) has been suggested to have many advantages for applications in marketing due to its relatively low cost, portability, and high temporal resolution. The aim of the current study was to determine the possibility of the EEG as a tool for detecting and predicting purchase decision in potential consumers. Twenty-three participants were recruited to record their EEGs as they saw the pictures of products followed by the products' prices and made the choice of whether to buy them or not. We estimated the power spectra and approximate entropy (ApEn), an information-theoretic measure to quantify the complexity [5], of their EEGs and compared them for purchase and non-purchase trials. The support vector machine (SVM) method was to predict their purchase decisions. We found that the relative spectral powers and ApEn values of the EEG significantly differed between purchase and non-purchase trials, in particular frontal regions. SVM could distinguish and predict purchase and non-purchase decisions based on the spectral powers and ApEn values of the EEGs in frontal regions prior to the decision moment with a high accuracy (>87 %). This finding suggests that relatively inexpensive,
portable EEG recording technique has great potential as a neural pre-
dictor of purchase behavior in neuromarketing and neuroeconomics.

References

1. Lee N, Broderick AJ, Chamberlain L. What is "neuromarketing"? A discussion and agenda for future research. Int J Psychophysiol. 2007;63:199-204.

2. Mirja Hubert PK. A current overview of consumer neuroscience. J Con-
sum Behav. 2008;7:272-92.

3. Ariely D, Berns GS. Neuromarketing: the hope and hype of neuroimaging in business. Nat Rev Neurosci. 2010;11:284-92.

4. Knutson B, Rick S, Wimmer GE, Prelec D, Loewenstein G. Neural predictors of purchases. Neuron. 2007;53:147-56.

5. Gu F, Meng XIN, Shen E, Cai Z. Can we measure consciousness with EEG complexities? Int J Bifurc Chaos. 2003;13:733-42.

P137

Vulnerability-based critical neurons, synapses, and pathways
in the *Caenorhabditis elegans* connectome

Seongkyun Kim<sup>1</sup>, Hyoungkyu Kim<sup>1</sup>, Jerald D. Kralik<sup>1</sup>, Jaeseung Jeong<sup>1</sup>
Department of Bio and Brain Engineering, Program of Brain and Cognitive
Engineering, College of Engineering, Korea Advanced Institute of Science
and Technology (KAIST), Daejeon, 34141, South Korea

**Correspondence:** Jerald D. Kralik - jerald.kralik@raphe.kaist.ac.kr, Jerald D. Kralik - jsjeong@kaist.ac.kr

BMC Neuroscience 2016, 17(Suppl 1):P137

Determining the fundamental architectural design of complex
nervous systems will lead to significant medical and technological
advances. Yet it remains unclear how nervous systems evolved highly
efficient networks with near optimal sharing of pathways that yet
produce multiple distinct behaviors to reach the organism's goals.
To determine this, we investigated the vulnerability of the nematode

roundworm *Caenorhabditis elegans* connectome [1] by attacking each
of 279 individual neurons and 6393 chemical synapses and 890 electri-
cal junctions in the connectome, and quantifying the lethality of the
network in terms of global information processing using graph-theo-
retic measures: i.e., examining vulnerability with respect to clustering
(C), efficiency (E), and betweenness (B).

The vulnerability analyses, V<sub>Ct</sub>, V<sub>Er</sub>, V<sub>Bt</sub>, identified 12 critical neurons and 29 critical synapses that are the most important components for establishing fundamental network properties. These critical elements were found to be control elements—i.e., those with the most influence over multiple underlying pathways. In addition, we found that the critical synapses formed into circuit-level control units, suggesting fractal-like control in the connectome. More specifically, three main critical pathways emerged from the results (Fig. 75A, B).

**Conclusions** The critical pathways that emerged from our computa-
tional analysis provide evidence for (a) the importance of backward
locomotor control, avoidance behavior, and social feeding to the organ-
ism; (b) the potential roles of specific neurons whose functions have
been unclear; and (c) both parallel and serial design elements in the
connectome—i.e., specific evidence for a mixed architectural design.
This design structure may be fundamental to nervous systems, provid-
ing necessary building blocks for the evolution of higher intelligence.

**Acknowledgements:** Supported by the National Research Founda-
tion of Korea (NRF-2013R1A1A2011570).

Reference

1. Altun Z, Hall D. Worm Atlas; 2002. http://www.wormatlas.org.

P138

Motif analysis reveals functionally asymmetrical neurons in *C. elegans*

Pyeong Soo Kim¹, Seongkyun Kim¹, Hyoungkyu Kim¹, Jaeseung Jeong¹
¹Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 305-701, South Korea

Correspondence: Jaeseung Jeong - jsjeong@kaist.ac.kr

BMC Neuroscience 2016, 17(Suppl 1):P138

Majority of animal species have bilaterally symmetrical nervous sys-
tem. Symmetric and asymmetric features among their morphological
symmetric nervous system have been interesting issue for long time.
The simplest bilaterally symmetrical organism is nematode called
*Caenorhabditis elegans*. Previously, symmetry for *C. elegans* has only
been thoroughly studied in morphological and functional manner [1].
According to previous observation, there are 92 bilaterally symmetri-
cal neuronal pairs and remaining 95 neurons are mostly located on
the axis of symmetry. Functionally there are only 2 neuronal pairs that
show asymmetrical gene expression among 92 pairs of symmetrical
neurons. We examined the symmetry of *C. elegans* nervous network
which has not been studied.

Total of 279 neurons and 2990 links in *C. elegans* were used. Neurons
were classified into bilaterally symmetrical neurons, unilateral neurons,
and unilateral neurons. According to the neuronal positions, we could
define the symmetry of each individual link and expand that defini-
tion to define the symmetry of motif [2]. After defining symmetry of
nervous network, we suggest a novel approach to classify asymmetric
neurons of *C. elegans* nervous system by examining asymmetric net-
work topology for every node. We defined 5 explicit locally topologi-
cal parameters for a neuron; (1) the degree is defined as the number
of asymmetric links attached to the neuron, (2) the motif is defined
as distribution of the numbers of asymmetric motifs for a neuron, (3)
the degree ratio is defined as ratio of asymmetric links over totally
attached links to the neuron including both of symmetric links and
asymmetric links, (4) the motif ratio is distribution of the rates for
asymmetric motifs over total motifs including both of symmetric and
asymmetric motifs, and (5) the relative distance is defined by the dif-
ference of asymmetric motif fingerprint of bilaterally symmetrical
neurons. Thresholds were defined using mean and standard deviation
(SD) values of asymmetries to find statistically asymmetric compo-
nents. Neurons with asymmetry value over the threshold were con-
sidered as asymmetric neurons (asymmetric neurons > SD from the

<figure><img src="image_83.png" /></figure>
mean values). We checked our asymmetric neurons with ASE and AWC neurons that are only known to show bilaterally asymmetrical gene expression. As a result, our study suggested that (4) ratio of asymmetric motif and (5) relative distance measures successfully classified ASE and AWC as asymmetric neurons. Except for ASE and AWC neurons, BDU, PLM, and PVW neurons are classified asymmetric in both measures. These results could be interpreted that BDU neurons, PLM neurons, and ALN neurons might possess asymmetric features that have not been discovered.

## References

1. Oliver H, Johnston RJ, Chang S. Left-right asymmetry in the nervous system: the *Caenorhabditis elegans* model. Nat Rev Neurosci. 2002;3(8):629-40.

2. Sporns O, Kötter R. Motifs in brain networks. PLoS Biol. 2004;2(11):e369.

## P139

### Computational approach to preference-based serial decision dynamics: do temporal discounting and working memory affect it?

Sangsup Yoon<sup>1,2</sup>, Jaehyung Kwon<sup>1,2</sup>, Sewoong Lim<sup>1,2</sup>, Jaeseung Jeong<sup>1,2</sup>

<sup>1</sup>Department of Bio and Brain Engineering, College of Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, South Korea; <sup>2</sup>Program of Brain and Cognitive Engineering, College of Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, South Korea

**Correspondence:** Jaeseung Jeong - jsjeong@kaist.ac.kr

BMC Neuroscience 2016, 17(Suppl 1):P139

When we face the multiple options (such as different taste of chocolates in a box) and choose one by one sequentially, there is no reason to prefer particular order of choice than any other possible choice strategies since all the items will eventually be consumed by ourselves. Recent studies have, however, revealed that there are distinct patterns of choice strategy in this preference-based serial decision tasks. Interestingly, there are two opposite choice patterns (favorite-first and favorite-last) in human subjects [1], while non-human animals (rhesus monkeys) only chose their favorite options at first [2]. Although several hypotheses for underlying neural mechanisms have been suggested to explain about how these distinct choice strategies appeared, they are not directly tested yet.

The goal of the current study was to examine whether temporal discounting and working memory affect choice strategy of serial decision-making and if so, to examine how they influence it. To measure the choice strategy, we used the modified version of 'the sushi problem' task [1], which use the pictures of opposite sex as a reward instead of the sushi [3]. We also measured the temporal discounting parameters and working memory performance by using the same set of opposite-sex pictures. The whole pictures were rated by each subject twice before the main experiment, the average rating score were subsequently used to divide the whole picture set into four groups based on the difference of subject specific preference. In 'sushi' task, subjects were asked to choose among four options (squares contain different number of stars; 1 ~ 4), each of which represents following short presentation (1.5 s) of pictures right after their choice. Each trial ends when subject choose all of four options, so they couldn't skip or miss any options. The temporal discounting parameter was measured by the subject choice between sooner-small reward and later-larger reward, in this case, the magnitude of reward was the number of stars which indicate the subject-specific attractiveness of each pictures, the delay of reward was relatively shorter (1-30 s) than typical temporal discounting task since our task offered actual outcome (see the picture) of each choices [4]. The picture version of n-back task was used to measure the working memory performance.

Consistent with previous studies, we observed distinct patterns of choice order in 'sushi' task, favorite-last strategy was most dominant (58 %) and favorite-first was second (31 %). We also found the relationship of both temporal discounting and working memory with choice strategies. The favorite-last group showed significantly lower rate of temporal discounting and higher performance of working memory

than favorite-first group. The effects of working memory and temporal discounting parameters on choice strategy were examined by logistic regression analysis, which revealed how the propensity to discount future events and the memory effect about recent events predicted the pattern of serial choice. We also constructed simple computational models using support vector machine and naïve Bayes classifier to predict their decision patterns based on working memory performance and temporal discounting parameters. We showed that these computational models successfully predicted preference-based decision patterns.

## References

1. Jaeseung J, Younmin O, Miriam C, Jerald DK. Preference-based serial decision dynamics: your first sushi reveals your eating order at the sushi table. Plos One. 2014;9(5):e96653

2. Kanghoon J, Jerald DK. Get it while it's hot: a peak-first bias in self-generated choice order in rhesus macaques. Plos One. 2013;8(12):e83814.

3. Itzhak A, Nancy E, Dan A, Christopher FC, Ethan O, Hans CB. Beautiful faces have variable reward value: fMRI and behavioral evidence. Neuron. 2001;32:537-51.

4. Benjamin YH, Purak CP, Robert OD, Michael LP. Economic principles motivating social attention in humans. Proc R Soc B. 2007;274:1751-56.

## P141

### Social stress induced neural network reconfiguration affects decision making and learning in zebrafish

Choongseok Park<sup>1</sup>, Thomas Miller<sup>2</sup>, Katie Clements<sup>2</sup>, Sungwoo Ahn<sup>3</sup>, Eon Hye Ji<sup>4</sup>, Fadi A. Issa<sup>2</sup>

<sup>1</sup>Department of Mathematics, North Carolina A&T State University, Greensboro, NC, 27411, USA; <sup>2</sup>Department of Biology, East Carolina University, Greenville, NC, 27858, USA; <sup>3</sup>Department of Mathematics, East Carolina University, Greenville, NC, 27858, USA; <sup>4</sup>David Geffen School of Medicine, UCLA, Los Angeles, CA, 90095, USA

**Correspondence:** Choongseok Park - issaf14@ecu.edu,

Fadi A. Issa - cpark@ncat.edu

BMC Neuroscience 2016, 17(Suppl 1):P141

In many social species, behavioral mechanisms of how social hierarchies formed and maintained have been studied extensively [1]. However, the neural bases underlying behavioral decisions and dynamics of neural circuits that permit animals to adapt to changes in social rank are poorly understood. In this study we focused on two social stress induced behaviors in zebrafish [the Mauthner cell (M-cell) mediated startle escape response and swimming behavior] to investigate how social regulation affects intrinsic cellular and network properties that result in the behavioral differences between dominant (DOMs) and subordinate (SUBs) animals. We utilized a non-invasive technique that allowed us to monitor the activation pattern of the two neural circuits in freely behaving animals.

High behavioral responsiveness and a low stimulus threshold for the initiation of escape in M-cell were observed in SUBs while DOMs showed the quicker habituation to repeated auditory stimulation compared to SUBs. We also observed that on average SUBs generated significantly less number of swim bursts compared to DOMs. These results suggest that social status induced stress can modify the startle plasticity as well as the local swimming circuit. The change in M-cell's excitability due to the change in the presynaptic inhibitory drive may be responsible for the lowered threshold. On the other hand, the local neural circuits and their intrinsic modulatory components (motor neurons and interneurons) may be configured differently according to social status to produce status-dependent swim patterns [2].

To test these ideas, we developed a biologically-based mathematical model whose network architecture is based on recent experimental data [3]. The model is able to reproduce several hallmarks of social status induced behavioral differences that were experimentally observed between DOMs and SUBs, as well as some inherent activity patterns. Changing some intrinsic synaptic and network parameters was sufficient to obtain the transition between DOMs and SUBs activity patterns while maintaining the network architecture.
Recent experiments show that the startle plasticity in M-cell can be modulated by endocannabinoids, 2-AG [3]. We chose the availability of 2-AG in M-cell as one of main parameters for the simulation, whose dynamics is governed by the intracellular calcium level in M-cell. Model simulation shows that high behavioral responsiveness in SUBs results from the increased excitability in M-cell, which can be interpreted as the reduced inhibitory input to M-cell. To reproduce less swimming activity in SUBs, the hallmark of social status induced behavioral difference observed in our experiments, we chose another intrinsic parameter, the availability of 2-AG in inhibitory interneurons to represent 2-AG modulated local network property. Model simulation shows that less swimming activity in SUBs is produced by the increased inhibitory input to the swimming neural circuit via the 2-AG driven elevated interneuron activity.

**Acknowledgements:** This work was partially supported by Simons Foundation Collaboration Grants for Mathematicians (#317566) to CP and East Carolina University, Department of Biology fund to FAI.

## References

1. Bergman TJ, Beehner JC, Cheney DL, Seyfarth RM. Hierarchical classification by rank and kinship in baboons. Science. 2003;302(5648):1234-36.

2. Issa FA, Drummond J, Cattaert D, Edwards DH. Neural circuit reconfiguration by social status. J Neurosci. 2012;32(16):5638-45.

3. Song J, Ampatzis K, Ausborn J, Manira AE. A hardwired circuit supplemented with endocannabinoids encodes behavioral choice in zebrafish. Curr Biol. 2015;25:2610-20.

## P142

### Descriptive, generative, and hybrid approaches for neural connectivity inference from neural activity data

JeongHun Baek¹, Shigeyuki Oba¹, Junichiro Yoshimoto²,³, Kenji Doya², Shin Ishii¹

¹Graduate School of Informatics, Kyoto University, Yoshidahonmachi

36-1, Sakyo, Kyoto, Japan; ²Neural Computation Unit, Okinawa

Institute of Science and Technology Graduate University, 1919-1

Tancha, Onna-son, Kunigami-gun, Okinawa, Japan; ³Graduate School of Information Science, Nara Institute of Science and Technology, 8916-5

Takayama, Ikoma, Nara, Japan

**Correspondence:** JeongHun Baek - ku21fang@gmail.com

BMC Neuroscience 2016, 17(Suppl 1):P142

Identification of the connectivity between neurons is important not only for elucidating neural bases for various functions but also for reconstructing the dynamics emerged in the connectivity. This thesis considers efficient methods for estimating synaptic connections from neural activity data. There are two kinds of approaches to neural connectivity inference: analytic one based on descriptive statistics and reproductive one based on statistical generative models. Analyses based on descriptive statistics, such as Pearson correlation, can identify neural connectivity based on activity data, with low computational cost. It, however, cannot reproduce the dynamic behaviors of the underlying connectivity, and hence, it is not suited for simulating the identified network.

Reproductive approach based on statistical generative models, such as generalized linear model, can naturally simulate the dynamic behaviors of the identified network, once we determine the network parameters from activity data. Contrary to this advantage, the computational cost of reproductive approach is often much heavier than analytic one. To utilize the preferable characters of the two approaches, in this study, we propose a hybrid approach of using a descriptive statistic for prescreening of existing connections and then performing generative model inference for dynamic model construction. We applied the hybrid approach to artificially generated spike data of various network sizes.

**Results and conclusions** Figure 76A shows the accuracy of functional connectivity analysis, in terms of ROC-AUC of binary classification, presence/absence of connectivity, where we see the hybrid approach performed slightly worse than the GFAM10. Note that the hybrid approach performed almost same with GFAM10 when the number

<figure><img src="image_84.png" /></figure>

**Fig. 76 A** Comparison of the prescreening accuracy in terms of ROC-AUC value (higher is better accuracy). **B** Comparison of the computation time. 'GFAM10 [1]', regarded as the original method, denotes generative functional additive model which is extended version of generalized linear model. 'Correlation-GFAM10' denotes a hybrid approach which performs the Pearson correlation for prescreening and then performs GFAM10

of neurons was 2000. Figure 76B shows the computation time of the two methods, where we see that GFAM10 took five times as much time as the hybrid approach. Our hybrid approach successfully reduced computational time, into about one-fifth of that of the sole reproductive approach based on the GFAM, while maintaining the estimation accuracy of the response functions within the identified functional connectivity.

## Reference

1. Song D, Wang H, Tu CY, Marmarelis VZ, Hampson RE, Deadwyler SA, Berger TW. Identification of sparse neural functional connectivity using penalized likelihood estimation and basis functions. J Comput Neurosci. 2013;35(3):335-57.

## P145

### Divergent-convergent synaptic connectivities accelerate coding in multilayered sensory systems

Thiago S. Mosqueiro¹, Martin F. Strube-Bloss², Brian Smith³, Ramon Huerta¹

¹University of California San Diego, La Jolla CA, USA; ²Biocenter University of Würzburg, Würzburg, Germany; ³School of Life Sciences, Arizona State University, Tempe, AZ, USA

**Correspondence:** Brian Smith - brian.h.smith@asu.edu

BMC Neuroscience 2016, 17(Suppl 1):P145

A central dogma in perception postulates that a minimal number of higher-order neurons provide the coding basis required for decision making and survival [1]. However, sensory information must travel through several neural layers before converging onto a smaller number of neurons in a premotor decision layer [2]. This multi-layered processing and convergence induces a time lag between peripheral input and adaptive behavior, which is inconsistent with the need for reaction speed. We propose that the divergent-convergent organization often occurring in multilayered neuropils enhances processing speed. Insect olfactory processing is a good model for investigating perceptual timing [3], where effective classification in the 4th layer 'anticipates' classification in input layers by 50 ms (Fig. 77A, B) [4]. Here we show that this anticipation emerges from divergent-convergent connectivity and the relative sizes of the layers, which rapidly amplifies subtle input signals and improves precision (Fig. 77C). We reproduced experimental results of peak classification in MBONs anticipating PNs by 50 ms on average (Fig. 77D). This becomes more pronounced as the KC layer grows, although increased noise is also observed. For an oversized KC layer, thus, this anticipation becomes lower and the signal is eventually destroyed by the emphasized noise. Interestingly, the key feature to this anticipation is indeed the ratio between KCs and PNs, showing that larger brains may balance these populations to achieve jointly higher pattern recognition capabilities and fasts discrimination times. We have analyzed fast coding