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Information Entropy Production of Maximum Entropy Markov Chains from Spike Trains
Version 1
: Received: 7 November 2017 / Approved: 8 November 2017 / Online: 8 November 2017 (04:25:12 CET)
A peer-reviewed article of this Preprint also exists.
Cofré, R.; Maldonado, C. Information Entropy Production of Maximum Entropy Markov Chains from Spike Trains. Entropy 2018, 20, 34. Cofré, R.; Maldonado, C. Information Entropy Production of Maximum Entropy Markov Chains from Spike Trains. Entropy 2018, 20, 34.
Abstract
Experimental recordings of the collective activity of interacting spiking neurons exhibit random behavior and memory effects, thus the stochastic process modeling the spiking activity is expected to show some degree of time irreversibility. We use the thermodynamic formalism to build a framework, in the context of spike train statistics, to quantify the degree of irreversibility of any parametric maximum entropy measure under arbitrary constraints, and provide an explicit formula for the information entropy production of the inferred Markov maximum entropy process. We provide examples to illustrate our results and discuss the importance of time irreversibility for modeling the spike train statistics.
Keywords
information entropy production; Discrete Markov Chains; spike train statistics; Gibbs measures; maximum entropy principle
Subject
Physical Sciences, Thermodynamics
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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