Parameter Estimation for Spatio-Temporal Maximum Entropy Distributions: Application to Neural Spike Trains

Hassan Nasser 1 Bruno Cessac 1
1 NEUROMATHCOMP - Mathematical and Computational Neuroscience
CRISAM - Inria Sophia Antipolis - Méditerranée , JAD - Laboratoire Jean Alexandre Dieudonné : UMR6621
Abstract : We propose a numerical method to learn maximum entropy (MaxEnt) distributions with spatio-temporal constraints from experimental spike trains. This is an extension of two papers, [10] and [4], which proposed the estimation of parameters where only spatial constraints were taken into account. The extension we propose allows one to properly handle memory effects in spike statistics, for large-sized neural networks.
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https://hal.inria.fr/hal-01096213
Contributor : Bruno Cessac <>
Submitted on : Wednesday, December 17, 2014 - 8:40:02 AM
Last modification on : Monday, December 10, 2018 - 4:14:08 PM

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Hassan Nasser, Bruno Cessac. Parameter Estimation for Spatio-Temporal Maximum Entropy Distributions: Application to Neural Spike Trains. Entropy, MDPI, 2014, 16 (4), pp.2244-2277. ⟨10.3390/e16042244⟩. ⟨hal-01096213⟩

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