{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T04:22:56Z","timestamp":1772252576381,"version":"3.50.1"},"reference-count":49,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2018,1,9]],"date-time":"2018-01-09T00:00:00Z","timestamp":1515456000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Proyectos REDES ETAPA INICIAL, Convocatoria 2017","award":["REDI170457"],"award-info":[{"award-number":["REDI170457"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/e20010034","type":"journal-article","created":{"date-parts":[[2018,1,9]],"date-time":"2018-01-09T14:00:08Z","timestamp":1515506408000},"page":"34","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Information Entropy Production of Maximum Entropy Markov Chains from Spike Trains"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7498-7122","authenticated-orcid":false,"given":"Rodrigo","family":"Cofr\u00e9","sequence":"first","affiliation":[{"name":"Centro de Investigaci\u00f3n y Modelamiento de Fen\u00f3menos Aleatorios, Facultad de Ingenier\u00eda, Universidad de Valpara\u00edso, Valpara\u00edso 2340000, Chile"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cesar","family":"Maldonado","sequence":"additional","affiliation":[{"name":"IPICYT\/Divisi\u00f3n de Matem\u00e1ticas Aplicadas, Instituto Potosino de Investigaci\u00f3n Cient\u00edfica y Tecnol\u00f3gica, San Luis Potos\u00ed 78216, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,1,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1016\/j.jphysparis.2017.02.005","article-title":"Recent progress in multi-electrode spike sorting methods","volume":"4","author":"Lefebvre","year":"2016","journal-title":"J. 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