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

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Entropy 2014, 16(4), 2244-2277; https://doi.org/10.3390/e16042244
Received: 19 February 2014 / Revised: 28 March 2014 / Accepted: 8 April 2014 / Published: 22 April 2014
(This article belongs to the Special Issue Maximum Entropy and Its Application)
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. View Full-Text
Keywords: neural coding; Gibbs distribution; maximum entropy; convex duality; spatio-temporal constraints; large-scale analysis; spike train; MEA recordings neural coding; Gibbs distribution; maximum entropy; convex duality; spatio-temporal constraints; large-scale analysis; spike train; MEA recordings
MDPI and ACS Style

Nasser, H.; Cessac, B. Parameter Estimation for Spatio-Temporal Maximum Entropy Distributions: Application to Neural Spike Trains. Entropy 2014, 16, 2244-2277.

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