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Contents Overview 5 OCNS - The Orga
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Organization for Computational Neur
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CNS*2013 Sponsors Brain Corporation
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General Info At the Meeting Venue T
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Local Information Since a list of a
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• You have a nice 360°-view over
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Gala Diner The gala diner will take
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Tutorials T1 Neural-mass and neural
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Main Meeting 25
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Oral session II: Visual system 14:0
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Tuesday July 16 9:00 - 9:10 Announc
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Workshops Note: W21 will be held Th
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W10 New approaches to spike train a
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Abstracts
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makes generalised seizures more lik
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• a scheme for biophysically deta
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• Pecevski et al. (2011), Probabi
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[5] Goodman (2010), Code generation
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eview key theoretical results and p
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Simon Laughlin Department of Zoolog
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Contributed Talks F1 Consistency re
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activation phase locks in the senso
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tigated whether eCBs could also pro
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2. Kobayashi R, Shinomoto S, Lansky
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O5 We now know what fly photorecept
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(B.Stem). The retina covers 10 by 1
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internally generated propagating wa
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We examine spike-count correlations
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Our results predict that thermosens
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corresponding experimental observat
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Figure 1: Properties of the model (
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goal-directed movements. Here, seve
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escence. In the low connectivity re
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O21 Multiscale modeling of cortical
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evidence [4]. We show here that the
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• How can one distinguish and stu
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network model in realtime W4 Method
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Rita Almeida, Karolinska Institute;
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greater detail. For example, dendri
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effective processing of task-relate
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W14 Modeling general anesthesia: fr
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Arvind Kumar, Freiburg: Basal gangl
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Morten Kringelbach, Oxford Maxime G
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Posters
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Posters Sunday Posters Posters P1 -
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P11 The role of inhibition in the g
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P25 P26 P27 P28 Estimating the frac
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P37 Zero-Lag Synchronization in Cor
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P48 Requirements for the Robust Ope
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P59 A maximum likelihood estimator
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P72 P73 The behavior of the electri
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P84 Multistability in large scale m
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P96 A biophysical model of cerebell
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P105 Estimating the transfer functi
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P117 P118 Neuronal Coding in the Ro
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P128 P129 P130 P131 P132 P133 P134
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P141 Visually guided behavior in fr
- Page 127 and 128: P151 P152 P153 P154 Estimation of n
- Page 129 and 130: P163 From laptops to supercomputers
- Page 131 and 132: P173 Single cell neuro-sensory dyna
- Page 133 and 134: P186 Latency and rate coding in a s
- Page 135 and 136: P200 On the influence of inhibitory
- Page 137 and 138: P213 Estimating synaptic connection
- Page 139 and 140: P228 Controlling the Go / No-Go dec
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- Page 143 and 144: P254 Motion control of thumb and in
- Page 145 and 146: P267 Non-instantaneous synaptic tra
- Page 147 and 148: P280 P281 Trial by trial decoding o
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- Page 151 and 152: P297 A numerical renormalisation gr
- Page 153 and 154: P309 Detection of neuronal signatur
- Page 155 and 156: P321 Long-Term Potentiation Through
- Page 157 and 158: P334 Sparse coding model captures V
- Page 159 and 160: P347 Influence of biophysical prope
- Page 161 and 162: P360 Plasticity of Network Dynamics
- Page 163 and 164: P373 The implications of evolutiona
- Page 165 and 166: P386 Attractor dynamics in local ne
- Page 167 and 168: P398 Why are all phase resetting cu
- Page 169 and 170: P410 Prefrontal cortical modulation
- Page 171 and 172: P422 Olfactory bulb network dynamic
- Page 173 and 174: P435 Center-Surround Interactions i
- Page 175 and 176: Appendix
- Page 177: Notes 177
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- Page 185 and 186: Page Index A Aazhang, Behnaam . . .
- Page 187 and 188: Chavane, Frederic . . . . . . . . .
- Page 189 and 190: Gerhard, Felipe . . . . . . . . . .
- Page 191 and 192: Kömek, Kübra. . . . . . . . . . .
- Page 193 and 194: Muresan, Raul Cristian. . . . . . .
- Page 195 and 196: Rotstein, Horacio G. . . . . . . .
- Page 197 and 198: V Valderrama, Mario . . . . . . . .
- Page 199 and 200: Contributions Index A Aazhang, Behn
- Page 201 and 202: Chartier, Josh . . . . . . . . . .
- Page 203 and 204: Gekas, Nikos . . . . . . . . . . .
- Page 205 and 206: Knösche, Thomas . . . . . . . . .
- Page 207 and 208: Mosqueiro, Thiago . . . . . . . . .
- Page 209 and 210: Ronacher, Bernhard . . . . . . . .
- Page 211 and 212: Tsigankov, Dmitry. . . . . . . . .