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Ultrafast simulation of large-scale neocortical microcircuitry with biophysically realistic neurons

Understanding the activity of the mammalian brain requires an integrative knowledge of circuits at distinct scales, ranging from ion channel gating to circuit connectomics. Computational models are regularly employed to understand how multiple parameters contribute synergistically to circuit behavio...

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Detalles Bibliográficos
Autores principales: Oláh, Viktor J, Pedersen, Nigel P, Rowan, Matthew JM
Formato: Online Artículo Texto
Lenguaje:English
Publicado: eLife Sciences Publications, Ltd 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9640191/
https://www.ncbi.nlm.nih.gov/pubmed/36341568
http://dx.doi.org/10.7554/eLife.79535
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author Oláh, Viktor J
Pedersen, Nigel P
Rowan, Matthew JM
author_facet Oláh, Viktor J
Pedersen, Nigel P
Rowan, Matthew JM
author_sort Oláh, Viktor J
collection PubMed
description Understanding the activity of the mammalian brain requires an integrative knowledge of circuits at distinct scales, ranging from ion channel gating to circuit connectomics. Computational models are regularly employed to understand how multiple parameters contribute synergistically to circuit behavior. However, traditional models of anatomically and biophysically realistic neurons are computationally demanding, especially when scaled to model local circuits. To overcome this limitation, we trained several artificial neural network (ANN) architectures to model the activity of realistic multicompartmental cortical neurons. We identified an ANN architecture that accurately predicted subthreshold activity and action potential firing. The ANN could correctly generalize to previously unobserved synaptic input, including in models containing nonlinear dendritic properties. When scaled, processing times were orders of magnitude faster compared with traditional approaches, allowing for rapid parameter-space mapping in a circuit model of Rett syndrome. Thus, we present a novel ANN approach allowing for rapid, detailed network experiments using inexpensive and commonly available computational resources.
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spelling pubmed-96401912022-11-08 Ultrafast simulation of large-scale neocortical microcircuitry with biophysically realistic neurons Oláh, Viktor J Pedersen, Nigel P Rowan, Matthew JM eLife Neuroscience Understanding the activity of the mammalian brain requires an integrative knowledge of circuits at distinct scales, ranging from ion channel gating to circuit connectomics. Computational models are regularly employed to understand how multiple parameters contribute synergistically to circuit behavior. However, traditional models of anatomically and biophysically realistic neurons are computationally demanding, especially when scaled to model local circuits. To overcome this limitation, we trained several artificial neural network (ANN) architectures to model the activity of realistic multicompartmental cortical neurons. We identified an ANN architecture that accurately predicted subthreshold activity and action potential firing. The ANN could correctly generalize to previously unobserved synaptic input, including in models containing nonlinear dendritic properties. When scaled, processing times were orders of magnitude faster compared with traditional approaches, allowing for rapid parameter-space mapping in a circuit model of Rett syndrome. Thus, we present a novel ANN approach allowing for rapid, detailed network experiments using inexpensive and commonly available computational resources. eLife Sciences Publications, Ltd 2022-11-07 /pmc/articles/PMC9640191/ /pubmed/36341568 http://dx.doi.org/10.7554/eLife.79535 Text en © 2022, Oláh et al https://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited.
spellingShingle Neuroscience
Oláh, Viktor J
Pedersen, Nigel P
Rowan, Matthew JM
Ultrafast simulation of large-scale neocortical microcircuitry with biophysically realistic neurons
title Ultrafast simulation of large-scale neocortical microcircuitry with biophysically realistic neurons
title_full Ultrafast simulation of large-scale neocortical microcircuitry with biophysically realistic neurons
title_fullStr Ultrafast simulation of large-scale neocortical microcircuitry with biophysically realistic neurons
title_full_unstemmed Ultrafast simulation of large-scale neocortical microcircuitry with biophysically realistic neurons
title_short Ultrafast simulation of large-scale neocortical microcircuitry with biophysically realistic neurons
title_sort ultrafast simulation of large-scale neocortical microcircuitry with biophysically realistic neurons
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9640191/
https://www.ncbi.nlm.nih.gov/pubmed/36341568
http://dx.doi.org/10.7554/eLife.79535
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