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

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

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 ext...

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Bibliographic Details
Main Authors: Hassan Nasser, Bruno Cessac
Format: Article
Language:English
Published: MDPI AG 2014-04-01
Series:Entropy
Subjects:
neural coding
Gibbs distribution
maximum entropy
convex duality
spatio-temporal constraints
large-scale analysis
spike train
MEA recordings
Online Access:http://www.mdpi.com/1099-4300/16/4/2244
Description
Summary: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.
ISSN:1099-4300

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