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

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).
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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


https://hal.inria.fr/hal-00756467
Contributor : Thierry Viéville <>
Submitted on : Friday, November 23, 2012 - 9:58:57 AM
Last modification on : Tuesday, August 6, 2013 - 11:45:22 AM

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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. <hal-00756467>

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