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A convolutional neural network for estimating synaptic connectivity from spike trains

The recent increase in reliable, simultaneous high channel count extracellular recordings is exciting for physiologists and theoreticians because it offers the possibility of reconstructing the underlying neuronal circuits. We recently presented a method of inferring this circuit connectivity from n...

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Autores principales: Endo, Daisuke, Kobayashi, Ryota, Bartolo, Ramon, Averbeck, Bruno B., Sugase-Miyamoto, Yasuko, Hayashi, Kazuko, Kawano, Kenji, Richmond, Barry J., Shinomoto, Shigeru
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8187444/
https://www.ncbi.nlm.nih.gov/pubmed/34103546
http://dx.doi.org/10.1038/s41598-021-91244-w
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author Endo, Daisuke
Kobayashi, Ryota
Bartolo, Ramon
Averbeck, Bruno B.
Sugase-Miyamoto, Yasuko
Hayashi, Kazuko
Kawano, Kenji
Richmond, Barry J.
Shinomoto, Shigeru
author_facet Endo, Daisuke
Kobayashi, Ryota
Bartolo, Ramon
Averbeck, Bruno B.
Sugase-Miyamoto, Yasuko
Hayashi, Kazuko
Kawano, Kenji
Richmond, Barry J.
Shinomoto, Shigeru
author_sort Endo, Daisuke
collection PubMed
description The recent increase in reliable, simultaneous high channel count extracellular recordings is exciting for physiologists and theoreticians because it offers the possibility of reconstructing the underlying neuronal circuits. We recently presented a method of inferring this circuit connectivity from neuronal spike trains by applying the generalized linear model to cross-correlograms. Although the algorithm can do a good job of circuit reconstruction, the parameters need to be carefully tuned for each individual dataset. Here we present another method using a Convolutional Neural Network for Estimating synaptic Connectivity from spike trains. After adaptation to huge amounts of simulated data, this method robustly captures the specific feature of monosynaptic impact in a noisy cross-correlogram. There are no user-adjustable parameters. With this new method, we have constructed diagrams of neuronal circuits recorded in several cortical areas of monkeys.
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spelling pubmed-81874442021-06-09 A convolutional neural network for estimating synaptic connectivity from spike trains Endo, Daisuke Kobayashi, Ryota Bartolo, Ramon Averbeck, Bruno B. Sugase-Miyamoto, Yasuko Hayashi, Kazuko Kawano, Kenji Richmond, Barry J. Shinomoto, Shigeru Sci Rep Article The recent increase in reliable, simultaneous high channel count extracellular recordings is exciting for physiologists and theoreticians because it offers the possibility of reconstructing the underlying neuronal circuits. We recently presented a method of inferring this circuit connectivity from neuronal spike trains by applying the generalized linear model to cross-correlograms. Although the algorithm can do a good job of circuit reconstruction, the parameters need to be carefully tuned for each individual dataset. Here we present another method using a Convolutional Neural Network for Estimating synaptic Connectivity from spike trains. After adaptation to huge amounts of simulated data, this method robustly captures the specific feature of monosynaptic impact in a noisy cross-correlogram. There are no user-adjustable parameters. With this new method, we have constructed diagrams of neuronal circuits recorded in several cortical areas of monkeys. Nature Publishing Group UK 2021-06-08 /pmc/articles/PMC8187444/ /pubmed/34103546 http://dx.doi.org/10.1038/s41598-021-91244-w Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Endo, Daisuke
Kobayashi, Ryota
Bartolo, Ramon
Averbeck, Bruno B.
Sugase-Miyamoto, Yasuko
Hayashi, Kazuko
Kawano, Kenji
Richmond, Barry J.
Shinomoto, Shigeru
A convolutional neural network for estimating synaptic connectivity from spike trains
title A convolutional neural network for estimating synaptic connectivity from spike trains
title_full A convolutional neural network for estimating synaptic connectivity from spike trains
title_fullStr A convolutional neural network for estimating synaptic connectivity from spike trains
title_full_unstemmed A convolutional neural network for estimating synaptic connectivity from spike trains
title_short A convolutional neural network for estimating synaptic connectivity from spike trains
title_sort convolutional neural network for estimating synaptic connectivity from spike trains
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8187444/
https://www.ncbi.nlm.nih.gov/pubmed/34103546
http://dx.doi.org/10.1038/s41598-021-91244-w
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