| 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, 1, 2009 | 185 | 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 | 122 | 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 | 109 | 1993 |
| A discrete time neural network model with spiking neurons B Cessac Journal of Mathematical Biology 56 (3), 311-345, 2008 | 107 | 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 | 85 | 2010 |
| On dynamics of integrate-and-fire neural networks with conductance based synapses B Cessac, T Viéville Frontiers in computational neuroscience, 2, 2008 | 85 | 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 | 71 | 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 | 70 | 1998 |
| Increase in complexity in random neural networks B Cessac Journal de Physique I 5 (3), 409-432, 1995 | 70 | 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 | 65 | 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 | 63 | 2011 |
| From neuron to neural networks dynamics B Cessac, M Samuelides The European Physical Journal Special Topics 142 (1), 7-88, 2007 | 56 | 2007 |
| 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 | 53 | 2000 |
| A view of neural networks as dynamical systems B Cessac International Journal of Bifurcation and Chaos 20 (06), 1585-1629, 2010 | 50 | 2010 |
| 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 | 41 | 2013 |
| Linear response, susceptibility and resonances in chaotic toy models B Cessac, JA Sepulchre Physica D: Nonlinear Phenomena 225 (1), 13-28, 2007 | 41 | 2007 |
| Pan-retinal characterisation of light responses from ganglion cells in the developing mouse retina G Hilgen, S Pirmoradian, D Pamplona, P Kornprobst, B Cessac, ... Scientific reports 7 (1), 1-14, 2017 | 37 | 2017 |
| Exact computation of the maximum-entropy potential of spiking neural-network models R Cofre, B Cessac Physical Review E 89 (5), 052117, 2014 | 36 | 2014 |
| 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 | 36 | 2008 |
| Random recurrent neural networks dynamics M Samuelides, B Cessac The European Physical Journal Special Topics 142 (1), 89-122, 2007 | 35 | 2007 |
Olivier MarreInstitut de la Vision, INSERM, ParisVerified email at inserm.fr
