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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: Gleeson, Padraig, Crook, Sharon, Cannon, Robert C., Hines, Michael L., Billings, Guy O., Farinella, Matteo, Morse, Thomas M., Davison, Andrew P., Ray, Subhasis, Bhalla, Upinder S., Barnes, Simon R., Dimitrova, Yoana D., Silver, R. Angus
Formato: Texto
Lenguaje:English
Publicado: Public Library of Science 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2887454/
https://www.ncbi.nlm.nih.gov/pubmed/20585541
http://dx.doi.org/10.1371/journal.pcbi.1000815
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author Gleeson, Padraig
Crook, Sharon
Cannon, Robert C.
Hines, Michael L.
Billings, Guy O.
Farinella, Matteo
Morse, Thomas M.
Davison, Andrew P.
Ray, Subhasis
Bhalla, Upinder S.
Barnes, Simon R.
Dimitrova, Yoana D.
Silver, R. Angus
author_facet Gleeson, Padraig
Crook, Sharon
Cannon, Robert C.
Hines, Michael L.
Billings, Guy O.
Farinella, Matteo
Morse, Thomas M.
Davison, Andrew P.
Ray, Subhasis
Bhalla, Upinder S.
Barnes, Simon R.
Dimitrova, Yoana D.
Silver, R. Angus
author_sort Gleeson, Padraig
collection PubMed
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.
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spelling pubmed-28874542010-06-22 NeuroML: A Language for Describing Data Driven Models of Neurons and Networks with a High Degree of Biological Detail Gleeson, Padraig Crook, Sharon Cannon, Robert C. Hines, Michael L. Billings, Guy O. Farinella, Matteo Morse, Thomas M. Davison, Andrew P. Ray, Subhasis Bhalla, Upinder S. Barnes, Simon R. Dimitrova, Yoana D. Silver, R. Angus PLoS Comput Biol Research Article 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. Public Library of Science 2010-06-17 /pmc/articles/PMC2887454/ /pubmed/20585541 http://dx.doi.org/10.1371/journal.pcbi.1000815 Text en Gleeson et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Gleeson, Padraig
Crook, Sharon
Cannon, Robert C.
Hines, Michael L.
Billings, Guy O.
Farinella, Matteo
Morse, Thomas M.
Davison, Andrew P.
Ray, Subhasis
Bhalla, Upinder S.
Barnes, Simon R.
Dimitrova, Yoana D.
Silver, R. Angus
NeuroML: A Language for Describing Data Driven Models of Neurons and Networks with a High Degree of Biological Detail
title 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_short 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
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2887454/
https://www.ncbi.nlm.nih.gov/pubmed/20585541
http://dx.doi.org/10.1371/journal.pcbi.1000815
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