@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		      = {}
}