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http://ipicyt.repositorioinstitucional.mx/jspui/handle/1010/1722| Information entropy production of maximum entropy markov chains from spike trains | |
| RODRIGO COFRE CESAR OCTAVIO MALDONADO AHUMADA | |
| Acceso Abierto | |
| Atribución-NoComercial-SinDerivadas | |
| https://doi.org/10.3390/e20010034 | |
| Information entropy production Discrete Markov chains Spike train statistics Gibbs measures Maximum entropy principle | |
| "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." | |
| MDPI AG | |
| 2018 | |
| Artículo | |
| Cofré, R.; Maldonado, C. Information Entropy Production of Maximum Entropy Markov Chains from Spike Trains. Entropy 2018, 20, 34. | |
| MATEMÁTICAS | |
| Versión publicada | |
| publishedVersion - Versión publicada | |
| Aparece en las colecciones: | Publicaciones Científicas Control y Sistemas Dinámicos |
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| Fichero | Tamaño | Formato | |
|---|---|---|---|
| Entropy20(2018)34.pdf | 1.32 MB | Adobe PDF | Visualizar/Abrir |