B. Cessac, A discrete time neural network model with spiking neurons: II: Dynamics with noise, Journal of Mathematical Biology, vol.19, issue.1???3, pp.863-900, 2011.
DOI : 10.1007/s00285-010-0358-4
URL : https://hal.archives-ouvertes.fr/inria-00530115

B. Cessac, Statistics of spike trains in conductance-based neural networks: Rigorous results, The Journal of Mathematical Neuroscience, vol.1, issue.1, 2011.
DOI : 10.1038/nature05534
URL : https://hal.archives-ouvertes.fr/hal-00640501

R. Cofré and B. Cessac, Dynamics and spike trains statistics in conductance-based integrate-and-_re neural networks with chemical and electric synapses, Chaos, Solitons and Fractals, 2012.

E. Ganmor, R. Segev, and E. Schneidman, The Architecture of Functional Interaction Networks in the Retina, Journal of Neuroscience, vol.31, issue.8, pp.3044-3054, 2011.
DOI : 10.1523/JNEUROSCI.3682-10.2011

H. Nasser, O. Marre, and B. Cessac, Spatio-temporal spike train analysis for large scale networks using the maximum entropy principle and Monte Carlo method, Journal of Statistical Mechanics: Theory and Experiment, vol.2013, issue.03, 2012.
DOI : 10.1088/1742-5468/2013/03/P03006
URL : https://hal.archives-ouvertes.fr/hal-00846160

J. Pillow, Y. Ahmadian, and L. Paninski, Model-Based Decoding, Information Estimation, and Change-Point Detection Techniques for Multineuron Spike Trains, Neural Computation, vol.79, issue.1, pp.1-45, 2011.
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E. Schneidman, M. Berry, R. Segev, and W. Bialek, Weak pairwise correlations imply strongly correlated network states in a neural population, Nature, vol.37, issue.7087, pp.1007-1012, 2006.
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J. Vasquez, O. Marre, A. Palacios, M. Berry, and B. Cessac, Gibbs distribution analysis of temporal correlation structure on multicell spike trains from retina ganglion cells, J. Physiol. Paris, 2012.