bruno cessac
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A constructive mean-field analysis of multi population neural networks with random synaptic weights and stochastic inputs
OD Faugeras, JD Touboul, B Cessac
Frontiers in computational neuroscience 3, 1, 2009
Mean-field equations, bifurcation map and route to chaos in discrete time neural networks
B Cessac, B Doyon, M Quoy, M Samuelides
Physica d: nonlinear phenomena 74 (1-2), 24-44, 1994
Control of the transition to chaos in neural networks with random connectivity
B Doyon, B Cessac, M Quoy, M Samuelides
International Journal of Bifurcation and Chaos 3 (02), 279-291, 1993
A discrete time neural network model with spiking neurons
B Cessac
Journal of Mathematical Biology 56 (3), 311-345, 2008
Overview of facts and issues about neural coding by spikes
B Cessac, H Paugam-Moisy, T Viéville
Journal of Physiology-Paris 104 (1-2), 5-18, 2010
On dynamics of integrate-and-fire neural networks with conductance based synapses
B Cessac, T Viéville
Frontiers in computational neuroscience 2, 2, 2008
Effects of Hebbian learning on the dynamics and structure of random networks with inhibitory and excitatory neurons
B Siri, M Quoy, B Delord, B Cessac, H Berry
Journal of Physiology-Paris 101 (1-3), 136-148, 2007
Self-organization and dynamics reduction in recurrent networks: stimulus presentation and learning
E Dauce, M Quoy, B Cessac, B Doyon, M Samuelides
Neural networks 11 (3), 521-533, 1998
Increase in complexity in random neural networks
B Cessac
Journal de Physique I 5 (3), 409-432, 1995
Gibbs distribution analysis of temporal correlations structure in retina ganglion cells
JC Vasquez, O Marre, AG Palacios, MJ Berry II, B Cessac
Journal of Physiology-Paris 106 (3-4), 120-127, 2012
A discrete time neural network model with spiking neurons: II: Dynamics with noise
B Cessac
Journal of mathematical biology 62 (6), 863-900, 2011
From neuron to neural networks dynamics
B Cessac, M Samuelides
The European Physical Journal Special Topics 142 (1), 7-88, 2007
A view of neural networks as dynamical systems
B Cessac
International Journal of Bifurcation and Chaos 20 (06), 1585-1629, 2010
What can one learn about Self-Organized Criticality from Dynamical Systems theory?
P Blanchard, B Cessac, T Krüger
Journal of Statistical Physics 98 (1), 375-404, 2000
Spatio-temporal spike train analysis for large scale networks using the maximum entropy principle and Monte Carlo method
H Nasser, O Marre, B Cessac
Journal of Statistical Mechanics: Theory and Experiment 2013 (03), P03006, 2013
Linear response, susceptibility and resonances in chaotic toy models
B Cessac, JA Sepulchre
Physica D: Nonlinear Phenomena 225 (1), 13-28, 2007
A mathematical analysis of the effects of Hebbian learning rules on the dynamics and structure of discrete-time random recurrent neural networks
B Siri, H Berry, B Cessac, B Delord, M Quoy
Neural computation 20 (12), 2937-2966, 2008
Random recurrent neural networks dynamics
M Samuelides, B Cessac
The European Physical Journal Special Topics 142 (1), 89-122, 2007
Exact computation of the maximum-entropy potential of spiking neural-network models
R Cofre, B Cessac
Physical Review E 89 (5), 052117, 2014
Dynamics and spike trains statistics in conductance-based integrate-and-fire neural networks with chemical and electric synapses
R Cofré, B Cessac
Chaos, Solitons & Fractals 50, 13-31, 2013
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