Cargando…
Constrained brain volume in an efficient coding model explains the fraction of excitatory and inhibitory neurons in sensory cortices
The number of neurons in mammalian cortex varies by multiple orders of magnitude across different species. In contrast, the ratio of excitatory to inhibitory neurons (E:I ratio) varies in a much smaller range, from 3:1 to 9:1 and remains roughly constant for different sensory areas within a species....
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8809590/ https://www.ncbi.nlm.nih.gov/pubmed/35061666 http://dx.doi.org/10.1371/journal.pcbi.1009642 |
_version_ | 1784644050563891200 |
---|---|
author | Alreja, Arish Nemenman, Ilya Rozell, Christopher J. |
author_facet | Alreja, Arish Nemenman, Ilya Rozell, Christopher J. |
author_sort | Alreja, Arish |
collection | PubMed |
description | The number of neurons in mammalian cortex varies by multiple orders of magnitude across different species. In contrast, the ratio of excitatory to inhibitory neurons (E:I ratio) varies in a much smaller range, from 3:1 to 9:1 and remains roughly constant for different sensory areas within a species. Despite this structure being important for understanding the function of neural circuits, the reason for this consistency is not yet understood. While recent models of vision based on the efficient coding hypothesis show that increasing the number of both excitatory and inhibitory cells improves stimulus representation, the two cannot increase simultaneously due to constraints on brain volume. In this work, we implement an efficient coding model of vision under a constraint on the volume (using number of neurons as a surrogate) while varying the E:I ratio. We show that the performance of the model is optimal at biologically observed E:I ratios under several metrics. We argue that this happens due to trade-offs between the computational accuracy and the representation capacity for natural stimuli. Further, we make experimentally testable predictions that 1) the optimal E:I ratio should be higher for species with a higher sparsity in the neural activity and 2) the character of inhibitory synaptic distributions and firing rates should change depending on E:I ratio. Our findings, which are supported by our new preliminary analyses of publicly available data, provide the first quantitative and testable hypothesis based on optimal coding models for the distribution of excitatory and inhibitory neural types in the mammalian sensory cortices. |
format | Online Article Text |
id | pubmed-8809590 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-88095902022-02-03 Constrained brain volume in an efficient coding model explains the fraction of excitatory and inhibitory neurons in sensory cortices Alreja, Arish Nemenman, Ilya Rozell, Christopher J. PLoS Comput Biol Research Article The number of neurons in mammalian cortex varies by multiple orders of magnitude across different species. In contrast, the ratio of excitatory to inhibitory neurons (E:I ratio) varies in a much smaller range, from 3:1 to 9:1 and remains roughly constant for different sensory areas within a species. Despite this structure being important for understanding the function of neural circuits, the reason for this consistency is not yet understood. While recent models of vision based on the efficient coding hypothesis show that increasing the number of both excitatory and inhibitory cells improves stimulus representation, the two cannot increase simultaneously due to constraints on brain volume. In this work, we implement an efficient coding model of vision under a constraint on the volume (using number of neurons as a surrogate) while varying the E:I ratio. We show that the performance of the model is optimal at biologically observed E:I ratios under several metrics. We argue that this happens due to trade-offs between the computational accuracy and the representation capacity for natural stimuli. Further, we make experimentally testable predictions that 1) the optimal E:I ratio should be higher for species with a higher sparsity in the neural activity and 2) the character of inhibitory synaptic distributions and firing rates should change depending on E:I ratio. Our findings, which are supported by our new preliminary analyses of publicly available data, provide the first quantitative and testable hypothesis based on optimal coding models for the distribution of excitatory and inhibitory neural types in the mammalian sensory cortices. Public Library of Science 2022-01-21 /pmc/articles/PMC8809590/ /pubmed/35061666 http://dx.doi.org/10.1371/journal.pcbi.1009642 Text en © 2022 Alreja 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 Alreja, Arish Nemenman, Ilya Rozell, Christopher J. Constrained brain volume in an efficient coding model explains the fraction of excitatory and inhibitory neurons in sensory cortices |
title | Constrained brain volume in an efficient coding model explains the fraction of excitatory and inhibitory neurons in sensory cortices |
title_full | Constrained brain volume in an efficient coding model explains the fraction of excitatory and inhibitory neurons in sensory cortices |
title_fullStr | Constrained brain volume in an efficient coding model explains the fraction of excitatory and inhibitory neurons in sensory cortices |
title_full_unstemmed | Constrained brain volume in an efficient coding model explains the fraction of excitatory and inhibitory neurons in sensory cortices |
title_short | Constrained brain volume in an efficient coding model explains the fraction of excitatory and inhibitory neurons in sensory cortices |
title_sort | constrained brain volume in an efficient coding model explains the fraction of excitatory and inhibitory neurons in sensory cortices |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8809590/ https://www.ncbi.nlm.nih.gov/pubmed/35061666 http://dx.doi.org/10.1371/journal.pcbi.1009642 |
work_keys_str_mv | AT alrejaarish constrainedbrainvolumeinanefficientcodingmodelexplainsthefractionofexcitatoryandinhibitoryneuronsinsensorycortices AT nemenmanilya constrainedbrainvolumeinanefficientcodingmodelexplainsthefractionofexcitatoryandinhibitoryneuronsinsensorycortices AT rozellchristopherj constrainedbrainvolumeinanefficientcodingmodelexplainsthefractionofexcitatoryandinhibitoryneuronsinsensorycortices |