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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...

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Detalles Bibliográficos
Autores principales: Pennington, Jacob R., David, Stephen V.
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
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author Pennington, Jacob R.
David, Stephen V.
author_facet Pennington, Jacob R.
David, Stephen V.
author_sort Pennington, Jacob R.
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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.
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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
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