@InCollection{	  cessac-palacios:12,
  author		      = {Bruno Cessac and Adrian Palacios},
  title			      = {Spike train statistics from empirical facts to theory: the case of the retina},
  editor		      = {Frédéric Cazals and Pierre Kornprobst},
  booktitle		      = {Modeling in Computational Biology and Biomedicine: A Multidisciplinary Endeavor},
  publisher		      = {Springer-Verlag},
  year			      = {2012},
  series		      = {Lectures Notes in Mathematical and Computational Biology (LNMCB)},
  url			      = {ftp://ftp-sop.inria.fr/neuromathcomp/team/bruno.cessac/Papers/author.pdf},
  topic			      = {Visual perception modeling},
  owner			      = {bcessac},
  group			      = {Neuromathcomp},
  annote		      = {This chapter focuses on methods from statistical physics and probability theory allowing the
				analysis of spike trains in neural networks. Taking as an example the retina we present recent
				works attempting to understand how retina ganglion cells encode the information transmitted to
				the visual cortex via the optical nerve, by analyzing their spike train statistics. We compare
				the maximal entropy models used in the literature of retina spike train analysis to rigorous
				results establishing the exact form of spike train statistics in conductance-based
				Integrate-and-Fire neural networks.},
  x-international-audience    = {yes},
  x-pays		      = {CL}
}