@Article{	  nasser-marre-etal:12,
author = {Hassan Nasser and Olivier Marre and Bruno Cessac},
title = {Spike trains analysis using Gibbs distributions and Monte-Carlo method},
journal = {Journal of Statistical Mechanics},
year = {2012},
note = {To appear in March 2013},
url = {http://lanl.arxiv.org/abs/1209.3886},
topic = {Modeling of spiking neurons},
owner = {bcessac},
group = {Neuromathcomp},
annote = {Understanding the dynamics of neural networks is a major challenge in experimental
neuroscience. For that purpose, a modelling of the recorded activity that reproduces the main
statistics of the data is required. In a first part, we present a review on recent results
dealing with spike train statistics analysis using maximum entropy models (MaxEnt). Most of
these studies have been focusing on modelling synchronous spike patterns, leaving aside the
temporal dynamics of the neural activity. However, the maximum entropy principle can be
generalized to the temporal case, leading to Markovian models where memory effects and time
correlations in the dynamics are properly taken into account. In a second part, we present a new
method based on Monte-Carlo sampling which is suited for the fitting of large-scale
spatio-temporal MaxEnt models. The formalism and the tools presented here will be essential to
fit MaxEnt spatio-temporal models to large neural ensembles.},
x-editorial-board = {yes},
x-international-audience = {yes},
x-pays = {}


26 August 2016