Título
Large deviations properties of maximum entropy markov chains from spike trains
Autor
RODRIGO COFRE
CESAR OCTAVIO MALDONADO AHUMADA
Fernando Rosas
Nivel de Acceso
Acceso Abierto
Identificador alterno
doi: https://doi.org/10.3390/e20080573
Materias
Computational neuroscience - (AUTOR) Spike train statistics - (AUTOR) Maximum entropy principle - (AUTOR) Large deviation theory - (AUTOR) Out-of-equilibrium statistical mechanics - (AUTOR) Thermodynamic formalism - (AUTOR) Entropy production - (AUTOR) CIENCIAS FÍSICO MATEMÁTICAS Y CIENCIAS DE LA TIERRA - (CTI) MATEMÁTICAS - (CTI) MATEMÁTICAS - (CTI)
Resumen o descripción
"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."
Editor
MDPI AG
Fecha de publicación
2018
Tipo de publicación
Artículo
Versión de la publicación
Versión publicada
Recurso de información
Formato
application/pdf
Sugerencia de citación
Cofré, R.; Maldonado, C.; Rosas, F. Large Deviations Properties of Maximum Entropy Markov Chains from Spike Trains. Entropy 2018, 20, 573.
Repositorio Orígen
Repositorio IPICYT
Descargas
365