A convolutional neural network for estimating synaptic connectivity from spike trains

Abstract 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 connectivi...

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Autores principales:	Daisuke Endo, Ryota Kobayashi, Ramon Bartolo, Bruno B. Averbeck, Yasuko Sugase-Miyamoto, Kazuko Hayashi, Kenji Kawano, Barry J. Richmond, Shigeru Shinomoto
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ff1bac4996b740f18282822bf171d347
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spelling	oai:doaj.org-article:ff1bac4996b740f18282822bf171d3472021-12-02T15:03:07ZA convolutional neural network for estimating synaptic connectivity from spike trains10.1038/s41598-021-91244-w2045-2322https://doaj.org/article/ff1bac4996b740f18282822bf171d3472021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-91244-whttps://doaj.org/toc/2045-2322Abstract 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.Daisuke EndoRyota KobayashiRamon BartoloBruno B. AverbeckYasuko Sugase-MiyamotoKazuko HayashiKenji KawanoBarry J. RichmondShigeru ShinomotoNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-18 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Daisuke Endo Ryota Kobayashi Ramon Bartolo Bruno B. Averbeck Yasuko Sugase-Miyamoto Kazuko Hayashi Kenji Kawano Barry J. Richmond Shigeru Shinomoto A convolutional neural network for estimating synaptic connectivity from spike trains
description	Abstract 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.
format	article
author	Daisuke Endo Ryota Kobayashi Ramon Bartolo Bruno B. Averbeck Yasuko Sugase-Miyamoto Kazuko Hayashi Kenji Kawano Barry J. Richmond Shigeru Shinomoto
author_facet	Daisuke Endo Ryota Kobayashi Ramon Bartolo Bruno B. Averbeck Yasuko Sugase-Miyamoto Kazuko Hayashi Kenji Kawano Barry J. Richmond Shigeru Shinomoto
author_sort	Daisuke Endo
title	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_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_sort	convolutional neural network for estimating synaptic connectivity from spike trains
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/ff1bac4996b740f18282822bf171d347
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A convolutional neural network for estimating synaptic connectivity from spike trains

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