@article{Cofré:2018:10.3390/e20080573,
author = {Cofré, R and Maldonado, C and Rosas, F},
doi = {10.3390/e20080573},
journal = {Entropy},
pages = {573--573},
title = {Large Deviations Properties of Maximum Entropy Markov Chains from Spike Trains},
url = {http://dx.doi.org/10.3390/e20080573},
volume = {20},
year = {2018}
}

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TY  - JOUR
AB  - <jats:p>We consider the maximum entropy Markov chain inference approach to characterize the collective statistics of neuronal spike trains, focusing on the statistical properties of the inferred model. To find the maximum entropy Markov chain, we use the thermodynamic formalism, which provides insightful connections with statistical physics and thermodynamics from which large deviations properties arise naturally. We provide an accessible introduction to the maximum entropy Markov chain inference problem and large deviations theory to the community of computational neuroscience, avoiding some technicalities while preserving the core ideas and intuitions. We review large deviations techniques useful in spike train statistics to describe properties of accuracy and convergence in terms of sampling size. We use these results to study the statistical fluctuation of correlations, distinguishability, and irreversibility of maximum entropy Markov chains. We illustrate these applications using simple examples where the large deviation rate function is explicitly obtained for maximum entropy models of relevance in this field.</jats:p>
AU  - Cofré,R
AU  - Maldonado,C
AU  - Rosas,F
DO  - 10.3390/e20080573
EP  - 573
PY  - 2018///
SP  - 573
TI  - Large Deviations Properties of Maximum Entropy Markov Chains from Spike Trains
T2  - Entropy
UR  - http://dx.doi.org/10.3390/e20080573
VL  - 20
ER  - 

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