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Title: Effective reduced diffusion-models: a data driven approach to the analysis of neuronal dynamics
Author: Deco, Gustavo
Martí, Daniel
Ledberg, Anders
Reig, Ramon
Sánchez-Vives, María Victoria
Keywords: Dinàmica d'una partícula
Xarxes neuronals (Neurobiologia)
Dynamics of a particle
Neural networks (Neurobiology)
Issue Date: 4-Dec-2009
Publisher: Public Library of Science (PLoS)
Abstract: We introduce in this paper a new method for reducing neurodynamical data to an effective diffusion equation, either experimentally or using simulations of biophysically detailed models. The dimensionality of the data is first reduced to the first principal component, and then fitted by the stationary solution of a mean-field-like one-dimensional Langevin equation, which describes the motion of a Brownian particle in a potential. The advantage of such description is that the stationary probability density of the dynamical variable can be easily derived. We applied this method to the analysis of cortical network dynamics during up and down states in an anesthetized animal. During deep anesthesia, intracellularly recorded up and down states transitions occurred with high regularity and could not be adequately described by a one-dimensional diffusion equation. Under lighter anesthesia, however, the distributions of the times spent in the up and down states were better fitted by such a model, suggesting a role for noise in determining the time spent in a particular state.
Note: Reproducció del document publicat a:
It is part of: PLoS Computational Biology, 2009, vol. 5, num. 12, p. e1000587
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ISSN: 1553-734X
Appears in Collections:Articles publicats en revistes (IDIBAPS: Institut d'investigacions Biomèdiques August Pi i Sunyer)
Articles publicats en revistes (Cognició, Desenvolupament i Psicologia de l'Educació)

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