Parameters estimation for spatio-temporal maximum entropy distributions: application to neural spike trains

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] who proposed the estimation of parameters where only spatial constraints were taken into account. The extension we propose allows to properly handle memory effects in spike statistics, for large sized neural networks.

34 pages, 33 figures

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Cited by in corpus (6)

• Inverse statistical problems: from the inverse Ising problem to data science
• Large Deviations Properties of Maximum Entropy Markov Chains from Spike Trains
• Inference of stochastic time series with missing data
• Maximum Entropy Principle Analysis in Network Systems with Short-time Recordings
• Thermodynamic Formalism in Neuronal Dynamics and Spike Train Statistics
• Information Entropy Production of Spatio-Temporal Maximum Entropy Distributions