Results 1 
8 of
8
Gibbs distribution analysis of temporal correlation structure on multicell spike trains from retina ganglion cells
, 2012
"... We present a method to estimate Gibbs distributions with spatiotemporal constraints on spike trains statistics. We apply this method to spike trains recorded from ganglion cells of the salamander retina, in response to natural movies. Our analysis, restricted to a few neurons, performs more accura ..."
Abstract

Cited by 11 (5 self)
 Add to MetaCart
We present a method to estimate Gibbs distributions with spatiotemporal constraints on spike trains statistics. We apply this method to spike trains recorded from ganglion cells of the salamander retina, in response to natural movies. Our analysis, restricted to a few neurons, performs more accurately than pairwise synchronization models (Ising) or the 1time step Markov models (Marre et al. (2009)) to describe the statistics of spatiotemporal spike patterns and emphasizes the role of higher order spatiotemporal interactions.
SEE PROFILE
, 2012
"... Spatiotemporal spike trains analysis for large scale networks using maximum entropy principle and MonteCarlo method ..."
Abstract
 Add to MetaCart
(Show Context)
Spatiotemporal spike trains analysis for large scale networks using maximum entropy principle and MonteCarlo method
ROLE OF ELECTRIC SYNAPSES IN SPIKE TRAINS STATISTICS OF LINEAR INTEGRATE AND FIRE NEURAL NETWORKS
, 2013
"... Communication between neurons involves chemical synapses as well as electric synapses. On theoretical grounds, the role of gap junctions in encoding and shaping collective dynamics as well as spike train statistics is quite less understood than the role of chemical synapses. In previous work [1] the ..."
Abstract
 Add to MetaCart
(Show Context)
Communication between neurons involves chemical synapses as well as electric synapses. On theoretical grounds, the role of gap junctions in encoding and shaping collective dynamics as well as spike train statistics is quite less understood than the role of chemical synapses. In previous work [1] the collective spike train statistics in conductancebased Integrate and Fire neural networks was studied rigorously. It was especially shown that this statistics is characterized by a Gibbs distribution whose potential can be explicitly computed. This provides moreover a firm theoretical ground for recent studies attempting to describe experimental rasters in the retina [4] as well as in the parietal cat cortex [2] by Gibbs distributions and maximal entropy principle. The work presented at AREADNE will extend the mathematical analysis of previous work [1] to conductancebased Integrate and Fire neural networks with chemical synapses as well as electric synapses, in the presence of noise. In opposition to previous paper dealing with this subject [3] we do not consider mean field approximations and the analysis is not limited to pulse type chemical synapses. The core of the analysis is to show how multiple single neurons interact in the presence of gap junctions. In conductance based models coupled with gap junctions, the subthreshold
Author manuscript, published in "Journal of PhysiologyParis (2013)" Spike train statistics and Gibbs
, 2013
"... This paper is based on a lecture given in the LACONEU summer school, Valparaiso, January 2012. We introduce Gibbs distribution in a general setting, including non stationary dynamics, and present then three examples of such Gibbs distributions, in the context of neural networks spike train statistic ..."
Abstract
 Add to MetaCart
(Show Context)
This paper is based on a lecture given in the LACONEU summer school, Valparaiso, January 2012. We introduce Gibbs distribution in a general setting, including non stationary dynamics, and present then three examples of such Gibbs distributions, in the context of neural networks spike train statistics: (i) Maximum entropy model with spatiotemporal constraints; (ii) Generalized Linear Models; (iii) Conductance based Integrate and Fire model with chemical synapses and gap junctions.
3. Scientific Foundations.....................................................................3
"... 3.1. Neural networks dynamics 3 3.2. Meanfield approaches 3 3.3. Neural fields 4 3.4. Spike train statistics 4 ..."
Abstract
 Add to MetaCart
(Show Context)
3.1. Neural networks dynamics 3 3.2. Meanfield approaches 3 3.3. Neural fields 4 3.4. Spike train statistics 4