Accurate and fast simulation of channel noise in conductance-based model neurons by diffusion approximation.

Stochastic channel gating is the major source of intrinsic neuronal noise whose functional consequences at the microcircuit- and network-levels have been only partly explored. A systematic study of this channel noise in large ensembles of biophysically detailed model neurons calls for the availabili...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Daniele Linaro, Marco Storace, Michele Giugliano
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2011
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/93dad9e2592c4bfca417f1a06e0e3e8b
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:93dad9e2592c4bfca417f1a06e0e3e8b
record_format	dspace
spelling	oai:doaj.org-article:93dad9e2592c4bfca417f1a06e0e3e8b2021-11-18T05:50:40ZAccurate and fast simulation of channel noise in conductance-based model neurons by diffusion approximation.1553-734X1553-735810.1371/journal.pcbi.1001102https://doaj.org/article/93dad9e2592c4bfca417f1a06e0e3e8b2011-03-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/21423712/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Stochastic channel gating is the major source of intrinsic neuronal noise whose functional consequences at the microcircuit- and network-levels have been only partly explored. A systematic study of this channel noise in large ensembles of biophysically detailed model neurons calls for the availability of fast numerical methods. In fact, exact techniques employ the microscopic simulation of the random opening and closing of individual ion channels, usually based on Markov models, whose computational loads are prohibitive for next generation massive computer models of the brain. In this work, we operatively define a procedure for translating any Markov model describing voltage- or ligand-gated membrane ion-conductances into an effective stochastic version, whose computer simulation is efficient, without compromising accuracy. Our approximation is based on an improved Langevin-like approach, which employs stochastic differential equations and no Montecarlo methods. As opposed to an earlier proposal recently debated in the literature, our approximation reproduces accurately the statistical properties of the exact microscopic simulations, under a variety of conditions, from spontaneous to evoked response features. In addition, our method is not restricted to the Hodgkin-Huxley sodium and potassium currents and is general for a variety of voltage- and ligand-gated ion currents. As a by-product, the analysis of the properties emerging in exact Markov schemes by standard probability calculus enables us for the first time to analytically identify the sources of inaccuracy of the previous proposal, while providing solid ground for its modification and improvement we present here.Daniele LinaroMarco StoraceMichele GiuglianoPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 7, Iss 3, p e1001102 (2011)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Daniele Linaro Marco Storace Michele Giugliano Accurate and fast simulation of channel noise in conductance-based model neurons by diffusion approximation.
description	Stochastic channel gating is the major source of intrinsic neuronal noise whose functional consequences at the microcircuit- and network-levels have been only partly explored. A systematic study of this channel noise in large ensembles of biophysically detailed model neurons calls for the availability of fast numerical methods. In fact, exact techniques employ the microscopic simulation of the random opening and closing of individual ion channels, usually based on Markov models, whose computational loads are prohibitive for next generation massive computer models of the brain. In this work, we operatively define a procedure for translating any Markov model describing voltage- or ligand-gated membrane ion-conductances into an effective stochastic version, whose computer simulation is efficient, without compromising accuracy. Our approximation is based on an improved Langevin-like approach, which employs stochastic differential equations and no Montecarlo methods. As opposed to an earlier proposal recently debated in the literature, our approximation reproduces accurately the statistical properties of the exact microscopic simulations, under a variety of conditions, from spontaneous to evoked response features. In addition, our method is not restricted to the Hodgkin-Huxley sodium and potassium currents and is general for a variety of voltage- and ligand-gated ion currents. As a by-product, the analysis of the properties emerging in exact Markov schemes by standard probability calculus enables us for the first time to analytically identify the sources of inaccuracy of the previous proposal, while providing solid ground for its modification and improvement we present here.
format	article
author	Daniele Linaro Marco Storace Michele Giugliano
author_facet	Daniele Linaro Marco Storace Michele Giugliano
author_sort	Daniele Linaro
title	Accurate and fast simulation of channel noise in conductance-based model neurons by diffusion approximation.
title_short	Accurate and fast simulation of channel noise in conductance-based model neurons by diffusion approximation.
title_full	Accurate and fast simulation of channel noise in conductance-based model neurons by diffusion approximation.
title_fullStr	Accurate and fast simulation of channel noise in conductance-based model neurons by diffusion approximation.
title_full_unstemmed	Accurate and fast simulation of channel noise in conductance-based model neurons by diffusion approximation.
title_sort	accurate and fast simulation of channel noise in conductance-based model neurons by diffusion approximation.
publisher	Public Library of Science (PLoS)
publishDate	2011
url	https://doaj.org/article/93dad9e2592c4bfca417f1a06e0e3e8b
work_keys_str_mv	AT danielelinaro accurateandfastsimulationofchannelnoiseinconductancebasedmodelneuronsbydiffusionapproximation AT marcostorace accurateandfastsimulationofchannelnoiseinconductancebasedmodelneuronsbydiffusionapproximation AT michelegiugliano accurateandfastsimulationofchannelnoiseinconductancebasedmodelneuronsbydiffusionapproximation
_version_	1718424800543637504

Accurate and fast simulation of channel noise in conductance-based model neurons by diffusion approximation.

Ejemplares similares