Cargando…
A convolutional neural network provides a generalizable model of natural sound coding by neural populations in auditory cortex
Convolutional neural networks (CNNs) can provide powerful and flexible models of neural sensory processing. However, the utility of CNNs in studying the auditory system has been limited by their requirement for large datasets and the complex response properties of single auditory neurons. To address...
Autores principales: | , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10191263/ https://www.ncbi.nlm.nih.gov/pubmed/37146065 http://dx.doi.org/10.1371/journal.pcbi.1011110 |
_version_ | 1785043425081425920 |
---|---|
author | Pennington, Jacob R. David, Stephen V. |
author_facet | Pennington, Jacob R. David, Stephen V. |
author_sort | Pennington, Jacob R. |
collection | PubMed |
description | Convolutional neural networks (CNNs) can provide powerful and flexible models of neural sensory processing. However, the utility of CNNs in studying the auditory system has been limited by their requirement for large datasets and the complex response properties of single auditory neurons. To address these limitations, we developed a population encoding model: a CNN that simultaneously predicts activity of several hundred neurons recorded during presentation of a large set of natural sounds. This approach defines a shared spectro-temporal space and pools statistical power across neurons. Population models of varying architecture performed consistently and substantially better than traditional linear-nonlinear models on data from primary and non-primary auditory cortex. Moreover, population models were highly generalizable. The output layer of a model pre-trained on one population of neurons could be fit to data from novel single units, achieving performance equivalent to that of neurons in the original fit data. This ability to generalize suggests that population encoding models capture a complete representational space across neurons in an auditory cortical field. |
format | Online Article Text |
id | pubmed-10191263 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-101912632023-05-18 A convolutional neural network provides a generalizable model of natural sound coding by neural populations in auditory cortex Pennington, Jacob R. David, Stephen V. PLoS Comput Biol Research Article Convolutional neural networks (CNNs) can provide powerful and flexible models of neural sensory processing. However, the utility of CNNs in studying the auditory system has been limited by their requirement for large datasets and the complex response properties of single auditory neurons. To address these limitations, we developed a population encoding model: a CNN that simultaneously predicts activity of several hundred neurons recorded during presentation of a large set of natural sounds. This approach defines a shared spectro-temporal space and pools statistical power across neurons. Population models of varying architecture performed consistently and substantially better than traditional linear-nonlinear models on data from primary and non-primary auditory cortex. Moreover, population models were highly generalizable. The output layer of a model pre-trained on one population of neurons could be fit to data from novel single units, achieving performance equivalent to that of neurons in the original fit data. This ability to generalize suggests that population encoding models capture a complete representational space across neurons in an auditory cortical field. Public Library of Science 2023-05-05 /pmc/articles/PMC10191263/ /pubmed/37146065 http://dx.doi.org/10.1371/journal.pcbi.1011110 Text en © 2023 Pennington, David 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 Pennington, Jacob R. David, Stephen V. A convolutional neural network provides a generalizable model of natural sound coding by neural populations in auditory cortex |
title | A convolutional neural network provides a generalizable model of natural sound coding by neural populations in auditory cortex |
title_full | A convolutional neural network provides a generalizable model of natural sound coding by neural populations in auditory cortex |
title_fullStr | A convolutional neural network provides a generalizable model of natural sound coding by neural populations in auditory cortex |
title_full_unstemmed | A convolutional neural network provides a generalizable model of natural sound coding by neural populations in auditory cortex |
title_short | A convolutional neural network provides a generalizable model of natural sound coding by neural populations in auditory cortex |
title_sort | convolutional neural network provides a generalizable model of natural sound coding by neural populations in auditory cortex |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10191263/ https://www.ncbi.nlm.nih.gov/pubmed/37146065 http://dx.doi.org/10.1371/journal.pcbi.1011110 |
work_keys_str_mv | AT penningtonjacobr aconvolutionalneuralnetworkprovidesageneralizablemodelofnaturalsoundcodingbyneuralpopulationsinauditorycortex AT davidstephenv aconvolutionalneuralnetworkprovidesageneralizablemodelofnaturalsoundcodingbyneuralpopulationsinauditorycortex AT penningtonjacobr convolutionalneuralnetworkprovidesageneralizablemodelofnaturalsoundcodingbyneuralpopulationsinauditorycortex AT davidstephenv convolutionalneuralnetworkprovidesageneralizablemodelofnaturalsoundcodingbyneuralpopulationsinauditorycortex |