Spatio-temporal spike train analysis for large scale networks using the maximum entropy principle and Monte Carlo method

Hassan Nasser 1 Olivier Marre 2 Bruno Cessac 1
1 NEUROMATHCOMP - Mathematical and Computational Neuroscience
CRISAM - Inria Sophia Antipolis - Méditerranée , JAD - Laboratoire Jean Alexandre Dieudonné : UMR6621
Abstract : Understanding the dynamics of neural networks is a major challenge in experimental neuroscience. For that purpose, a modelling of the recorded activity that reproduces the main statistics of the data is required. In a first part, we present a review on recent results dealing with spike train statistics analysis using maximum entropy models (MaxEnt). Most of these studies have been focusing on modelling synchronous spike patterns, leaving aside the temporal dynamics of the neural activity. However, the maximum entropy principle can be generalized to the temporal case, leading to Markovian models where memory effects and time correlations in the dynamics are properly taken into account. In a second part, we present a new method based on Monte-Carlo sampling which is suited for the fitting of large-scale spatio-temporal MaxEnt models. The formalism and the tools presented here will be essential to fit MaxEnt spatio-temporal models to large neural ensembles.
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Journal of Statistical Mechanics: Theory and Experiment, IOP Science, 2013, 2013 (03), pp.P03006. 〈10.1088/1742-5468/2013/03/P03006〉
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https://hal.inria.fr/hal-00846160
Contributeur : Pierre Kornprobst <>
Soumis le : jeudi 18 juillet 2013 - 16:09:47
Dernière modification le : jeudi 11 janvier 2018 - 16:51:42

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Hassan Nasser, Olivier Marre, Bruno Cessac. Spatio-temporal spike train analysis for large scale networks using the maximum entropy principle and Monte Carlo method. Journal of Statistical Mechanics: Theory and Experiment, IOP Science, 2013, 2013 (03), pp.P03006. 〈10.1088/1742-5468/2013/03/P03006〉. 〈hal-00846160〉

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