NeuroML: a language for describing data driven models of neurons and networks with a high degree of biological detail.

Biologically detailed single neuron and network models are important for understanding how ion channels, synapses and anatomical connectivity underlie the complex electrical behavior of the brain. While neuronal simulators such as NEURON, GENESIS, MOOSE, NEST, and PSICS facilitate the development of...

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Autores principales:	Padraig Gleeson, Sharon Crook, Robert C Cannon, Michael L Hines, Guy O Billings, Matteo Farinella, Thomas M Morse, Andrew P Davison, Subhasis Ray, Upinder S Bhalla, Simon R Barnes, Yoana D Dimitrova, R Angus Silver
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2010
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/d6213c4969c9433db88533de3998fec6
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spelling	oai:doaj.org-article:d6213c4969c9433db88533de3998fec62021-12-02T19:58:21ZNeuroML: a language for describing data driven models of neurons and networks with a high degree of biological detail.1553-734X1553-735810.1371/journal.pcbi.1000815https://doaj.org/article/d6213c4969c9433db88533de3998fec62010-06-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/20585541/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Biologically detailed single neuron and network models are important for understanding how ion channels, synapses and anatomical connectivity underlie the complex electrical behavior of the brain. While neuronal simulators such as NEURON, GENESIS, MOOSE, NEST, and PSICS facilitate the development of these data-driven neuronal models, the specialized languages they employ are generally not interoperable, limiting model accessibility and preventing reuse of model components and cross-simulator validation. To overcome these problems we have used an Open Source software approach to develop NeuroML, a neuronal model description language based on XML (Extensible Markup Language). This enables these detailed models and their components to be defined in a standalone form, allowing them to be used across multiple simulators and archived in a standardized format. Here we describe the structure of NeuroML and demonstrate its scope by converting into NeuroML models of a number of different voltage- and ligand-gated conductances, models of electrical coupling, synaptic transmission and short-term plasticity, together with morphologically detailed models of individual neurons. We have also used these NeuroML-based components to develop an highly detailed cortical network model. NeuroML-based model descriptions were validated by demonstrating similar model behavior across five independently developed simulators. Although our results confirm that simulations run on different simulators converge, they reveal limits to model interoperability, by showing that for some models convergence only occurs at high levels of spatial and temporal discretisation, when the computational overhead is high. Our development of NeuroML as a common description language for biophysically detailed neuronal and network models enables interoperability across multiple simulation environments, thereby improving model transparency, accessibility and reuse in computational neuroscience.Padraig GleesonSharon CrookRobert C CannonMichael L HinesGuy O BillingsMatteo FarinellaThomas M MorseAndrew P DavisonSubhasis RayUpinder S BhallaSimon R BarnesYoana D DimitrovaR Angus SilverPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 6, Iss 6, p e1000815 (2010)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Padraig Gleeson Sharon Crook Robert C Cannon Michael L Hines Guy O Billings Matteo Farinella Thomas M Morse Andrew P Davison Subhasis Ray Upinder S Bhalla Simon R Barnes Yoana D Dimitrova R Angus Silver NeuroML: a language for describing data driven models of neurons and networks with a high degree of biological detail.
description	Biologically detailed single neuron and network models are important for understanding how ion channels, synapses and anatomical connectivity underlie the complex electrical behavior of the brain. While neuronal simulators such as NEURON, GENESIS, MOOSE, NEST, and PSICS facilitate the development of these data-driven neuronal models, the specialized languages they employ are generally not interoperable, limiting model accessibility and preventing reuse of model components and cross-simulator validation. To overcome these problems we have used an Open Source software approach to develop NeuroML, a neuronal model description language based on XML (Extensible Markup Language). This enables these detailed models and their components to be defined in a standalone form, allowing them to be used across multiple simulators and archived in a standardized format. Here we describe the structure of NeuroML and demonstrate its scope by converting into NeuroML models of a number of different voltage- and ligand-gated conductances, models of electrical coupling, synaptic transmission and short-term plasticity, together with morphologically detailed models of individual neurons. We have also used these NeuroML-based components to develop an highly detailed cortical network model. NeuroML-based model descriptions were validated by demonstrating similar model behavior across five independently developed simulators. Although our results confirm that simulations run on different simulators converge, they reveal limits to model interoperability, by showing that for some models convergence only occurs at high levels of spatial and temporal discretisation, when the computational overhead is high. Our development of NeuroML as a common description language for biophysically detailed neuronal and network models enables interoperability across multiple simulation environments, thereby improving model transparency, accessibility and reuse in computational neuroscience.
format	article
author	Padraig Gleeson Sharon Crook Robert C Cannon Michael L Hines Guy O Billings Matteo Farinella Thomas M Morse Andrew P Davison Subhasis Ray Upinder S Bhalla Simon R Barnes Yoana D Dimitrova R Angus Silver
author_facet	Padraig Gleeson Sharon Crook Robert C Cannon Michael L Hines Guy O Billings Matteo Farinella Thomas M Morse Andrew P Davison Subhasis Ray Upinder S Bhalla Simon R Barnes Yoana D Dimitrova R Angus Silver
author_sort	Padraig Gleeson
title	NeuroML: a language for describing data driven models of neurons and networks with a high degree of biological detail.
title_short	NeuroML: a language for describing data driven models of neurons and networks with a high degree of biological detail.
title_full	NeuroML: a language for describing data driven models of neurons and networks with a high degree of biological detail.
title_fullStr	NeuroML: a language for describing data driven models of neurons and networks with a high degree of biological detail.
title_full_unstemmed	NeuroML: a language for describing data driven models of neurons and networks with a high degree of biological detail.
title_sort	neuroml: a language for describing data driven models of neurons and networks with a high degree of biological detail.
publisher	Public Library of Science (PLoS)
publishDate	2010
url	https://doaj.org/article/d6213c4969c9433db88533de3998fec6
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NeuroML: a language for describing data driven models of neurons and networks with a high degree of biological detail.

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