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Spike train statistics: from mathematical models to software to experiments

Bruno Cessac 1 
1 NEUROMATHCOMP - Mathematical and Computational Neuroscience
CRISAM - Inria Sophia Antipolis - Méditerranée , JAD - Laboratoire Jean Alexandre Dieudonné : UMR6621
Abstract : Recent advances in multi-electrodes array acquisition has made it possible torecord the activity of up to several hundreds of neurons at the same time andto register their collective spiking activity. This opens up new perspectivesin understanding how a neuronal network encodes the response to a stimulus, andwhat a spike train tells up about the network structure and nonlinear dynamics.For this, one has to develop statistical models properly handling thespatio-temporal aspects of spike trains, including memory effects. In thistalk, I will review several such statistical models, including Maximum EntropyModels, Generalized Linear Model or neuromimetic models dealing with theiradvantages, limits, and relations.
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Submitted on : Tuesday, December 23, 2014 - 11:45:08 AM
Last modification on : Thursday, August 4, 2022 - 4:58:17 PM



  • HAL Id : hal-01095746, version 1


Bruno Cessac. Spike train statistics: from mathematical models to software to experiments. 6th Workshop in Computational Neuroscience in Marseille, Mar 2014, Marseille, France. ⟨hal-01095746⟩



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