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Information entropy production of maximum entropy markov chains from spike trains

dc.contributor.authorCofré, Rodrigo
dc.contributor.authorMaldonado Ahumada, César Octavio
dc.identifier.citationCofré, R.; Maldonado, C. Information Entropy Production of Maximum Entropy Markov Chains from Spike Trains. Entropy 2018, 20, 34.
dc.description.abstract"The spiking activity of neuronal networks follows laws that are not time-reversal symmetric; the notion of pre-synaptic and post-synaptic neurons, stimulus correlations and noise correlations have a clear time order. Therefore, a biologically realistic statistical model for the spiking activity should be able to capture some degree of time irreversibility. We use the thermodynamic formalism to build a framework in the context maximum entropy models to quantify the degree of time irreversibility, providing an explicit formula for the information entropy production of the inferred maximum entropy Markov chain. We provide examples to illustrate our results and discuss the importance of time irreversibility for modeling the spike train statistics."
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional
dc.subjectInformation entropy production
dc.subjectDiscrete Markov chains
dc.subjectSpike train statistics
dc.subjectGibbs measures
dc.subjectMaximum entropy principle
dc.titleInformation entropy production of maximum entropy markov chains from spike trains
dc.rights.accessAcceso Abierto

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Attribution-NonCommercial-NoDerivatives 4.0 Internacional
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 Internacional