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Items: 24

1.

A biophysical model explains the spontaneous bursting behavior in the developing retina.

Matzakos-Karvouniari D, Gil L, Orendorff E, Marre O, Picaud S, Cessac B.

Sci Rep. 2019 Feb 12;9(1):1859. doi: 10.1038/s41598-018-38299-4.

2.

PRANAS: A New Platform for Retinal Analysis and Simulation.

Cessac B, Kornprobst P, Kraria S, Nasser H, Pamplona D, Portelli G, Viéville T.

Front Neuroinform. 2017 Sep 1;11:49. doi: 10.3389/fninf.2017.00049. eCollection 2017.

3.

Pan-retinal characterisation of Light Responses from Ganglion Cells in the Developing Mouse Retina.

Hilgen G, Pirmoradian S, Pamplona D, Kornprobst P, Cessac B, Hennig MH, Sernagor E.

Sci Rep. 2017 Feb 10;7:42330. doi: 10.1038/srep42330.

4.

On the Mathematical Consequences of Binning Spike Trains.

Cessac B, Le Ny A, Löcherbach E.

Neural Comput. 2017 Jan;29(1):146-170. doi: 10.1162/NECO_a_00898. Epub 2016 Oct 20.

PMID:
27764593
5.

Exact computation of the maximum-entropy potential of spiking neural-network models.

Cofré R, Cessac B.

Phys Rev E Stat Nonlin Soft Matter Phys. 2014 May;89(5):052117. Epub 2014 May 12.

PMID:
25353749
6.

Effects of cellular homeostatic intrinsic plasticity on dynamical and computational properties of biological recurrent neural networks.

Naudé J, Cessac B, Berry H, Delord B.

J Neurosci. 2013 Sep 18;33(38):15032-43. doi: 10.1523/JNEUROSCI.0870-13.2013.

7.

Spike train statistics and Gibbs distributions.

Cessac B, Cofré R.

J Physiol Paris. 2013 Nov;107(5):360-8. doi: 10.1016/j.jphysparis.2013.03.001. Epub 2013 Mar 15. Review.

PMID:
23501168
8.

Parameter estimation in spiking neural networks: a reverse-engineering approach.

Rostro-Gonzalez H, Cessac B, Vieville T.

J Neural Eng. 2012 Apr;9(2):026024. doi: 10.1088/1741-2560/9/2/026024. Epub 2012 Mar 15.

PMID:
22419215
9.

Gibbs distribution analysis of temporal correlations structure in retina ganglion cells.

Vasquez JC, Marre O, Palacios AG, Berry MJ 2nd, Cessac B.

J Physiol Paris. 2012 May-Aug;106(3-4):120-7. doi: 10.1016/j.jphysparis.2011.11.001. Epub 2011 Nov 17.

10.

The role of the asymptotic dynamics in the design of FPGA-based hardware implementations of gIF-type neural networks.

Rostro-Gonzalez H, Cessac B, Girau B, Torres-Huitzil C.

J Physiol Paris. 2011 Jan-Jun;105(1-3):91-7. doi: 10.1016/j.jphysparis.2011.09.004. Epub 2011 Sep 21.

PMID:
21964248
11.

Statistics of spike trains in conductance-based neural networks: Rigorous results.

Cessac B.

J Math Neurosci. 2011 Aug 25;1(1):8. doi: 10.1186/2190-8567-1-8.

12.

A discrete time neural network model with spiking neurons: II: dynamics with noise.

Cessac B.

J Math Biol. 2011 Jun;62(6):863-900. doi: 10.1007/s00285-010-0358-4. Epub 2010 Jul 24.

PMID:
20658138
13.

Overview of facts and issues about neural coding by spikes.

Cessac B, Paugam-Moisy H, Viéville T.

J Physiol Paris. 2010 Jan-Mar;104(1-2):5-18. doi: 10.1016/j.jphysparis.2009.11.002. Epub 2009 Nov 29. Review.

PMID:
19925865
14.

A constructive mean-field analysis of multi-population neural networks with random synaptic weights and stochastic inputs.

Faugeras O, Touboul J, Cessac B.

Front Comput Neurosci. 2009 Feb 18;3:1. doi: 10.3389/neuro.10.001.2009. eCollection 2009.

15.

On dynamics of integrate-and-fire neural networks with conductance based synapses.

Cessac B, Viéville T.

Front Comput Neurosci. 2008 Jul 4;2:2. doi: 10.3389/neuro.10.002.2008. eCollection 2008.

16.

A mathematical analysis of the effects of Hebbian learning rules on the dynamics and structure of discrete-time random recurrent neural networks.

Siri B, Berry H, Cessac B, Delord B, Quoy M.

Neural Comput. 2008 Dec;20(12):2937-66. doi: 10.1162/neco.2008.05-07-530.

PMID:
18624656
17.

Effects of Hebbian learning on the dynamics and structure of random networks with inhibitory and excitatory neurons.

Siri B, Quoy M, Delord B, Cessac B, Berry H.

J Physiol Paris. 2007 Jan-May;101(1-3):136-48. Epub 2007 Oct 16. Review.

PMID:
18042357
18.
19.

Transmitting a signal by amplitude modulation in a chaotic network.

Cessac B, Sepulchre JA.

Chaos. 2006 Mar;16(1):013104.

PMID:
16599735
20.

Stable resonances and signal propagation in a chaotic network of coupled units.

Cessac B, Sepulchre JA.

Phys Rev E Stat Nonlin Soft Matter Phys. 2004 Nov;70(5 Pt 2):056111. Epub 2004 Nov 17.

PMID:
15600696
21.

Self-organization and dynamics reduction in recurrent networks: stimulus presentation and learning.

Samuelides M, Doyon B, Cessac B, Quoy M, Dauce E.

Neural Netw. 1998 Apr;11(3):521-533.

PMID:
12662827
22.

Anomalous scaling and Lee-Yang zeros in self-organized criticality.

Cessac B, Meunier JL.

Phys Rev E Stat Nonlin Soft Matter Phys. 2002 Mar;65(3 Pt 2A):036131. Epub 2002 Feb 28.

PMID:
11909189
23.

Lyapunov exponents and transport in the Zhang model of self-organized criticality.

Cessac B, Blanchard P, Krüger T.

Phys Rev E Stat Nonlin Soft Matter Phys. 2001 Jul;64(1 Pt 2):016133. Epub 2001 Jun 28.

PMID:
11461357
24.

Mean-field equations, bifurcation map and chaos in discrete time, continuous state, random neural networks.

Doyon B, Cessac B, Quoy M, Samuelides M.

Acta Biotheor. 1995 Jun;43(1-2):169-75.

PMID:
7709685

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