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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
eLife Sciences Publications, Ltd
2022
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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. |
format | Online Article Text |
id | pubmed-9640191 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | eLife Sciences Publications, Ltd |
record_format | MEDLINE/PubMed |
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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