- Page 3: Contents Overview 5 OCNS - The Orga
- Page 7 and 8: Organization for Computational Neur
- Page 9: CNS*2013 Sponsors Brain Corporation
- Page 13 and 14: General Info At the Meeting Venue T
- Page 15 and 16: Local Information Since a list of a
- Page 17 and 18: • You have a nice 360°-view over
- Page 19: Gala Diner The gala diner will take
- Page 23 and 24: Tutorials T1 Neural-mass and neural
- Page 25 and 26: Main Meeting 25
- Page 27 and 28: Oral session II: Visual system 14:0
- Page 29: Tuesday July 16 9:00 - 9:10 Announc
- Page 33 and 34: W10 New approaches to spike train a
- Page 35: Abstracts
- Page 38 and 39: makes generalised seizures more lik
- Page 40 and 41: • a scheme for biophysically deta
- Page 42 and 43: • Pecevski et al. (2011), Probabi
- Page 44 and 45: [5] Goodman (2010), Code generation
- Page 46 and 47: eview key theoretical results and p
- Page 48 and 49: Simon Laughlin Department of Zoolog
- Page 51 and 52: Contributed Talks F1 Consistency re
- Page 53 and 54: activation phase locks in the senso
- Page 55 and 56: tigated whether eCBs could also pro
- Page 57 and 58: 2. Kobayashi R, Shinomoto S, Lansky
- Page 59 and 60: O5 We now know what fly photorecept
- Page 61 and 62: (B.Stem). The retina covers 10 by 1
- Page 63 and 64: internally generated propagating wa
- Page 65 and 66: We examine spike-count correlations
- Page 67 and 68: Our results predict that thermosens
- Page 69 and 70: corresponding experimental observat
- Page 71 and 72: Figure 1: Properties of the model (
- Page 73 and 74: goal-directed movements. Here, seve
- Page 75 and 76: escence. In the low connectivity re
- Page 77 and 78: O21 Multiscale modeling of cortical
- Page 79: 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
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P151 P152 P153 P154 Estimation of n
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P163 From laptops to supercomputers
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P173 Single cell neuro-sensory dyna
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P186 Latency and rate coding in a s
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P200 On the influence of inhibitory
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P213 Estimating synaptic connection
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P228 Controlling the Go / No-Go dec
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P240 P241 P242 P243 P244 P245 P246
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P254 Motion control of thumb and in
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P267 Non-instantaneous synaptic tra
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P280 P281 Trial by trial decoding o
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149
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P297 A numerical renormalisation gr
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P309 Detection of neuronal signatur
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P321 Long-Term Potentiation Through
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P334 Sparse coding model captures V
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P347 Influence of biophysical prope
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P360 Plasticity of Network Dynamics
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P373 The implications of evolutiona
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P386 Attractor dynamics in local ne
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P398 Why are all phase resetting cu
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P410 Prefrontal cortical modulation
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P422 Olfactory bulb network dynamic
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P435 Center-Surround Interactions i
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Appendix
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Notes 177
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179
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181
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183
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Page Index A Aazhang, Behnaam . . .
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Chavane, Frederic . . . . . . . . .
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Gerhard, Felipe . . . . . . . . . .
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Kömek, Kübra. . . . . . . . . . .
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Muresan, Raul Cristian. . . . . . .
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Rotstein, Horacio G. . . . . . . .
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V Valderrama, Mario . . . . . . . .
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Contributions Index A Aazhang, Behn
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Chartier, Josh . . . . . . . . . .
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Gekas, Nikos . . . . . . . . . . .
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Knösche, Thomas . . . . . . . . .
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Mosqueiro, Thiago . . . . . . . . .
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Ronacher, Bernhard . . . . . . . .
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Tsigankov, Dmitry. . . . . . . . .