An efficient analytical reduction of detailed nonlinear neuron models

Realistic simulations of neurons and neural networks are key for understanding neural computations. Here the authors describe Neuron_Reduce, an analytic approach to simplify neurons receiving thousands of synapses and accelerate their simulations by 40–250 folds, while preserving voltage dynamics an...

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Autores principales:	Oren Amsalem, Guy Eyal, Noa Rogozinski, Michael Gevaert, Pramod Kumbhar, Felix Schürmann, Idan Segev
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/942319641ed7409a8a1dadc85babcf06
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spelling	oai:doaj.org-article:942319641ed7409a8a1dadc85babcf062021-12-02T17:31:10ZAn efficient analytical reduction of detailed nonlinear neuron models10.1038/s41467-019-13932-62041-1723https://doaj.org/article/942319641ed7409a8a1dadc85babcf062020-01-01T00:00:00Zhttps://doi.org/10.1038/s41467-019-13932-6https://doaj.org/toc/2041-1723Realistic simulations of neurons and neural networks are key for understanding neural computations. Here the authors describe Neuron_Reduce, an analytic approach to simplify neurons receiving thousands of synapses and accelerate their simulations by 40–250 folds, while preserving voltage dynamics and dendritic computations.Oren AmsalemGuy EyalNoa RogozinskiMichael GevaertPramod KumbharFelix SchürmannIdan SegevNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-13 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Oren Amsalem Guy Eyal Noa Rogozinski Michael Gevaert Pramod Kumbhar Felix Schürmann Idan Segev An efficient analytical reduction of detailed nonlinear neuron models
description	Realistic simulations of neurons and neural networks are key for understanding neural computations. Here the authors describe Neuron_Reduce, an analytic approach to simplify neurons receiving thousands of synapses and accelerate their simulations by 40–250 folds, while preserving voltage dynamics and dendritic computations.
format	article
author	Oren Amsalem Guy Eyal Noa Rogozinski Michael Gevaert Pramod Kumbhar Felix Schürmann Idan Segev
author_facet	Oren Amsalem Guy Eyal Noa Rogozinski Michael Gevaert Pramod Kumbhar Felix Schürmann Idan Segev
author_sort	Oren Amsalem
title	An efficient analytical reduction of detailed nonlinear neuron models
title_short	An efficient analytical reduction of detailed nonlinear neuron models
title_full	An efficient analytical reduction of detailed nonlinear neuron models
title_fullStr	An efficient analytical reduction of detailed nonlinear neuron models
title_full_unstemmed	An efficient analytical reduction of detailed nonlinear neuron models
title_sort	efficient analytical reduction of detailed nonlinear neuron models
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/942319641ed7409a8a1dadc85babcf06
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An efficient analytical reduction of detailed nonlinear neuron models

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