Scalable and accurate method for neuronal ensemble detection in spiking neural networks.

We propose a novel, scalable, and accurate method for detecting neuronal ensembles from a population of spiking neurons. Our approach offers a simple yet powerful tool to study ensemble activity. It relies on clustering synchronous population activity (population vectors), allows the participation o...

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Autores principales:	Rubén Herzog, Arturo Morales, Soraya Mora, Joaquín Araya, María-José Escobar, Adrian G Palacios, Rodrigo Cofré
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/86864e33fb6740108547dd89be706d48
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id	oai:doaj.org-article:86864e33fb6740108547dd89be706d48
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spelling	oai:doaj.org-article:86864e33fb6740108547dd89be706d482021-12-02T20:04:46ZScalable and accurate method for neuronal ensemble detection in spiking neural networks.1932-620310.1371/journal.pone.0251647https://doaj.org/article/86864e33fb6740108547dd89be706d482021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0251647https://doaj.org/toc/1932-6203We propose a novel, scalable, and accurate method for detecting neuronal ensembles from a population of spiking neurons. Our approach offers a simple yet powerful tool to study ensemble activity. It relies on clustering synchronous population activity (population vectors), allows the participation of neurons in different ensembles, has few parameters to tune and is computationally efficient. To validate the performance and generality of our method, we generated synthetic data, where we found that our method accurately detects neuronal ensembles for a wide range of simulation parameters. We found that our method outperforms current alternative methodologies. We used spike trains of retinal ganglion cells obtained from multi-electrode array recordings under a simple ON-OFF light stimulus to test our method. We found a consistent stimuli-evoked ensemble activity intermingled with spontaneously active ensembles and irregular activity. Our results suggest that the early visual system activity could be organized in distinguishable functional ensembles. We provide a Graphic User Interface, which facilitates the use of our method by the scientific community.Rubén HerzogArturo MoralesSoraya MoraJoaquín ArayaMaría-José EscobarAdrian G PalaciosRodrigo CofréPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 7, p e0251647 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Rubén Herzog Arturo Morales Soraya Mora Joaquín Araya María-José Escobar Adrian G Palacios Rodrigo Cofré Scalable and accurate method for neuronal ensemble detection in spiking neural networks.
description	We propose a novel, scalable, and accurate method for detecting neuronal ensembles from a population of spiking neurons. Our approach offers a simple yet powerful tool to study ensemble activity. It relies on clustering synchronous population activity (population vectors), allows the participation of neurons in different ensembles, has few parameters to tune and is computationally efficient. To validate the performance and generality of our method, we generated synthetic data, where we found that our method accurately detects neuronal ensembles for a wide range of simulation parameters. We found that our method outperforms current alternative methodologies. We used spike trains of retinal ganglion cells obtained from multi-electrode array recordings under a simple ON-OFF light stimulus to test our method. We found a consistent stimuli-evoked ensemble activity intermingled with spontaneously active ensembles and irregular activity. Our results suggest that the early visual system activity could be organized in distinguishable functional ensembles. We provide a Graphic User Interface, which facilitates the use of our method by the scientific community.
format	article
author	Rubén Herzog Arturo Morales Soraya Mora Joaquín Araya María-José Escobar Adrian G Palacios Rodrigo Cofré
author_facet	Rubén Herzog Arturo Morales Soraya Mora Joaquín Araya María-José Escobar Adrian G Palacios Rodrigo Cofré
author_sort	Rubén Herzog
title	Scalable and accurate method for neuronal ensemble detection in spiking neural networks.
title_short	Scalable and accurate method for neuronal ensemble detection in spiking neural networks.
title_full	Scalable and accurate method for neuronal ensemble detection in spiking neural networks.
title_fullStr	Scalable and accurate method for neuronal ensemble detection in spiking neural networks.
title_full_unstemmed	Scalable and accurate method for neuronal ensemble detection in spiking neural networks.
title_sort	scalable and accurate method for neuronal ensemble detection in spiking neural networks.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/86864e33fb6740108547dd89be706d48
work_keys_str_mv	AT rubenherzog scalableandaccuratemethodforneuronalensembledetectioninspikingneuralnetworks AT arturomorales scalableandaccuratemethodforneuronalensembledetectioninspikingneuralnetworks AT sorayamora scalableandaccuratemethodforneuronalensembledetectioninspikingneuralnetworks AT joaquinaraya scalableandaccuratemethodforneuronalensembledetectioninspikingneuralnetworks AT mariajoseescobar scalableandaccuratemethodforneuronalensembledetectioninspikingneuralnetworks AT adriangpalacios scalableandaccuratemethodforneuronalensembledetectioninspikingneuralnetworks AT rodrigocofre scalableandaccuratemethodforneuronalensembledetectioninspikingneuralnetworks
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Scalable and accurate method for neuronal ensemble detection in spiking neural networks.

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