Analyzing large-scale spike trains data with spatio-temporal constraints

Hassan Nasser 1 Olivier Marre 2, 3 Selim Kraria 1 Thierry Viéville 4 Bruno Cessac 1
1 NEUROMATHCOMP - Mathematical and Computational Neuroscience
CRISAM - Inria Sophia Antipolis - Méditerranée , JAD - Laboratoire Jean Alexandre Dieudonné : UMR6621
4 Mnemosyne - Mnemonic Synergy
LaBRI - Laboratoire Bordelais de Recherche en Informatique, INRIA Bordeaux - Sud-Ouest, IMN - Institut des Maladies Neurodégénératives
Abstract : Recent experimental advances have made it possible to record several hundred neurons simultaneously in the retina as well as in the cortex. Analyzing such a huge amount of data requires to elaborate statistical, mathematical and numer- ical methods, to describe both the spatio-temporal structure of the population activity and its relevance to sensory coding. Among these methods, the maxi- mum entropy principle has been used to describe the statistics of spike trains. Recall that the maximum entropy principle consists of xing a set of constraints, determined with the empirical average of quantities ("observables") measured on the raster: for example average ring rate of neurons, or pairwise corre- lations. Maximising the statistical entropy given those constraints provides a probability distribution, called a Gibbs distribution, that provides a statistical model to t the data and extrapolate phenomenological laws. Most approaches were restricted to instantaneous observables i.e. quantities corresponding to spikes occurring at the same time (singlets, pairs, triplets and so on).
Document type :
Conference papers
NeuroComp/KEOpS'12 workshop beyond the retina: from computational models to outcomes in bioengineering. Focus on architecture and dynamics sustaining information flows in the visuomotor system., Oct 2012, Bordeaux, France. 2012


https://hal.inria.fr/hal-00756467
Contributor : Thierry Viéville <>
Submitted on : Friday, November 23, 2012 - 9:58:57 AM
Last modification on : Wednesday, April 8, 2015 - 4:35:14 PM

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Hassan Nasser, Olivier Marre, Selim Kraria, Thierry Viéville, Bruno Cessac. Analyzing large-scale spike trains data with spatio-temporal constraints. NeuroComp/KEOpS'12 workshop beyond the retina: from computational models to outcomes in bioengineering. Focus on architecture and dynamics sustaining information flows in the visuomotor system., Oct 2012, Bordeaux, France. 2012. <hal-00756467>

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