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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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Detalles Bibliográficos
Autores principales: Herzog, Rubén, Morales, Arturo, Mora, Soraya, Araya, Joaquín, Escobar, María-José, Palacios, Adrian G., Cofré, Rodrigo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8323916/
https://www.ncbi.nlm.nih.gov/pubmed/34329314
http://dx.doi.org/10.1371/journal.pone.0251647
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author Herzog, Rubén
Morales, Arturo
Mora, Soraya
Araya, Joaquín
Escobar, María-José
Palacios, Adrian G.
Cofré, Rodrigo
author_facet Herzog, Rubén
Morales, Arturo
Mora, Soraya
Araya, Joaquín
Escobar, María-José
Palacios, Adrian G.
Cofré, Rodrigo
author_sort Herzog, Rubén
collection PubMed
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.
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spelling pubmed-83239162021-07-31 Scalable and accurate method for neuronal ensemble detection in spiking neural networks Herzog, Rubén Morales, Arturo Mora, Soraya Araya, Joaquín Escobar, María-José Palacios, Adrian G. Cofré, Rodrigo PLoS One Research Article 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. Public Library of Science 2021-07-30 /pmc/articles/PMC8323916/ /pubmed/34329314 http://dx.doi.org/10.1371/journal.pone.0251647 Text en © 2021 Herzog et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Herzog, Rubén
Morales, Arturo
Mora, Soraya
Araya, Joaquín
Escobar, María-José
Palacios, Adrian G.
Cofré, Rodrigo
Scalable and accurate method for neuronal ensemble detection in spiking neural networks
title 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_short 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
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8323916/
https://www.ncbi.nlm.nih.gov/pubmed/34329314
http://dx.doi.org/10.1371/journal.pone.0251647
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