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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...

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Autores principales: Sorochynskyi, Oleksandr, Deny, Stéphane, Marre, Olivier, Ferrari, Ulisse
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
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.
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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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