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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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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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. |
format | Online Article Text |
id | pubmed-8323916 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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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