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Channel based generating function approach to the stochastic Hodgkin-Huxley neuronal system

Internal and external fluctuations, such as channel noise and synaptic noise, contribute to the generation of spontaneous action potentials in neurons. Many different Langevin approaches have been proposed to speed up the computation but with waning accuracy especially at small channel numbers. We a...

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Detalles Bibliográficos
Autores principales: Ling, Anqi, Huang, Yandong, Shuai, Jianwei, Lan, Yueheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4778126/
https://www.ncbi.nlm.nih.gov/pubmed/26940002
http://dx.doi.org/10.1038/srep22662
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author Ling, Anqi
Huang, Yandong
Shuai, Jianwei
Lan, Yueheng
author_facet Ling, Anqi
Huang, Yandong
Shuai, Jianwei
Lan, Yueheng
author_sort Ling, Anqi
collection PubMed
description Internal and external fluctuations, such as channel noise and synaptic noise, contribute to the generation of spontaneous action potentials in neurons. Many different Langevin approaches have been proposed to speed up the computation but with waning accuracy especially at small channel numbers. We apply a generating function approach to the master equation for the ion channel dynamics and further propose two accelerating algorithms, with an accuracy close to the Gillespie algorithm but with much higher efficiency, opening the door for expedited simulation of noisy action potential propagating along axons or other types of noisy signal transduction.
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spelling pubmed-47781262016-03-09 Channel based generating function approach to the stochastic Hodgkin-Huxley neuronal system Ling, Anqi Huang, Yandong Shuai, Jianwei Lan, Yueheng Sci Rep Article Internal and external fluctuations, such as channel noise and synaptic noise, contribute to the generation of spontaneous action potentials in neurons. Many different Langevin approaches have been proposed to speed up the computation but with waning accuracy especially at small channel numbers. We apply a generating function approach to the master equation for the ion channel dynamics and further propose two accelerating algorithms, with an accuracy close to the Gillespie algorithm but with much higher efficiency, opening the door for expedited simulation of noisy action potential propagating along axons or other types of noisy signal transduction. Nature Publishing Group 2016-03-04 /pmc/articles/PMC4778126/ /pubmed/26940002 http://dx.doi.org/10.1038/srep22662 Text en Copyright © 2016, Macmillan Publishers Limited http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
spellingShingle Article
Ling, Anqi
Huang, Yandong
Shuai, Jianwei
Lan, Yueheng
Channel based generating function approach to the stochastic Hodgkin-Huxley neuronal system
title Channel based generating function approach to the stochastic Hodgkin-Huxley neuronal system
title_full Channel based generating function approach to the stochastic Hodgkin-Huxley neuronal system
title_fullStr Channel based generating function approach to the stochastic Hodgkin-Huxley neuronal system
title_full_unstemmed Channel based generating function approach to the stochastic Hodgkin-Huxley neuronal system
title_short Channel based generating function approach to the stochastic Hodgkin-Huxley neuronal system
title_sort channel based generating function approach to the stochastic hodgkin-huxley neuronal system
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4778126/
https://www.ncbi.nlm.nih.gov/pubmed/26940002
http://dx.doi.org/10.1038/srep22662
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