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Predicting synchronous firing of large neural populations from sequential recordings
A major goal in neuroscience is to understand how populations of neurons code for stimuli or actions. While the number of neurons that can be recorded simultaneously is increasing at a fast pace, in most cases these recordings cannot access a complete population: some neurons that carry relevant inf...
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/PMC7891787/ https://www.ncbi.nlm.nih.gov/pubmed/33507938 http://dx.doi.org/10.1371/journal.pcbi.1008501 |
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author | Sorochynskyi, Oleksandr Deny, Stéphane Marre, Olivier Ferrari, Ulisse |
author_facet | Sorochynskyi, Oleksandr Deny, Stéphane Marre, Olivier Ferrari, Ulisse |
author_sort | Sorochynskyi, Oleksandr |
collection | PubMed |
description | A major goal in neuroscience is to understand how populations of neurons code for stimuli or actions. While the number of neurons that can be recorded simultaneously is increasing at a fast pace, in most cases these recordings cannot access a complete population: some neurons that carry relevant information remain unrecorded. In particular, it is hard to simultaneously record all the neurons of the same type in a given area. Recent progress have made possible to profile each recorded neuron in a given area thanks to genetic and physiological tools, and to pool together recordings from neurons of the same type across different experimental sessions. However, it is unclear how to infer the activity of a full population of neurons of the same type from these sequential recordings. Neural networks exhibit collective behaviour, e.g. noise correlations and synchronous activity, that are not directly captured by a conditionally-independent model that would just put together the spike trains from sequential recordings. Here we show that we can infer the activity of a full population of retina ganglion cells from sequential recordings, using a novel method based on copula distributions and maximum entropy modeling. From just the spiking response of each ganglion cell to a repeated stimulus, and a few pairwise recordings, we could predict the noise correlations using copulas, and then the full activity of a large population of ganglion cells of the same type using maximum entropy modeling. Remarkably, we could generalize to predict the population responses to different stimuli with similar light conditions and even to different experiments. We could therefore use our method to construct a very large population merging cells’ responses from different experiments. We predicted that synchronous activity in ganglion cell populations saturates only for patches larger than 1.5mm in radius, beyond what is today experimentally accessible. |
format | Online Article Text |
id | pubmed-7891787 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-78917872021-03-01 Predicting synchronous firing of large neural populations from sequential recordings Sorochynskyi, Oleksandr Deny, Stéphane Marre, Olivier Ferrari, Ulisse PLoS Comput Biol Research Article A major goal in neuroscience is to understand how populations of neurons code for stimuli or actions. While the number of neurons that can be recorded simultaneously is increasing at a fast pace, in most cases these recordings cannot access a complete population: some neurons that carry relevant information remain unrecorded. In particular, it is hard to simultaneously record all the neurons of the same type in a given area. Recent progress have made possible to profile each recorded neuron in a given area thanks to genetic and physiological tools, and to pool together recordings from neurons of the same type across different experimental sessions. However, it is unclear how to infer the activity of a full population of neurons of the same type from these sequential recordings. Neural networks exhibit collective behaviour, e.g. noise correlations and synchronous activity, that are not directly captured by a conditionally-independent model that would just put together the spike trains from sequential recordings. Here we show that we can infer the activity of a full population of retina ganglion cells from sequential recordings, using a novel method based on copula distributions and maximum entropy modeling. From just the spiking response of each ganglion cell to a repeated stimulus, and a few pairwise recordings, we could predict the noise correlations using copulas, and then the full activity of a large population of ganglion cells of the same type using maximum entropy modeling. Remarkably, we could generalize to predict the population responses to different stimuli with similar light conditions and even to different experiments. We could therefore use our method to construct a very large population merging cells’ responses from different experiments. We predicted that synchronous activity in ganglion cell populations saturates only for patches larger than 1.5mm in radius, beyond what is today experimentally accessible. Public Library of Science 2021-01-28 /pmc/articles/PMC7891787/ /pubmed/33507938 http://dx.doi.org/10.1371/journal.pcbi.1008501 Text en © 2021 Sorochynskyi et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://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 Sorochynskyi, Oleksandr Deny, Stéphane Marre, Olivier Ferrari, Ulisse Predicting synchronous firing of large neural populations from sequential recordings |
title | Predicting synchronous firing of large neural populations from sequential recordings |
title_full | Predicting synchronous firing of large neural populations from sequential recordings |
title_fullStr | Predicting synchronous firing of large neural populations from sequential recordings |
title_full_unstemmed | Predicting synchronous firing of large neural populations from sequential recordings |
title_short | Predicting synchronous firing of large neural populations from sequential recordings |
title_sort | predicting synchronous firing of large neural populations from sequential recordings |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7891787/ https://www.ncbi.nlm.nih.gov/pubmed/33507938 http://dx.doi.org/10.1371/journal.pcbi.1008501 |
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