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Engineering recurrent neural networks from task-relevant manifolds and dynamics
Many cognitive processes involve transformations of distributed representations in neural populations, creating a need for population-level models. Recurrent neural network models fulfill this need, but there are many open questions about how their connectivity gives rise to dynamics that solve a ta...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7446915/ https://www.ncbi.nlm.nih.gov/pubmed/32785228 http://dx.doi.org/10.1371/journal.pcbi.1008128 |
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author | Pollock, Eli Jazayeri, Mehrdad |
author_facet | Pollock, Eli Jazayeri, Mehrdad |
author_sort | Pollock, Eli |
collection | PubMed |
description | Many cognitive processes involve transformations of distributed representations in neural populations, creating a need for population-level models. Recurrent neural network models fulfill this need, but there are many open questions about how their connectivity gives rise to dynamics that solve a task. Here, we present a method for finding the connectivity of networks for which the dynamics are specified to solve a task in an interpretable way. We apply our method to a working memory task by synthesizing a network that implements a drift-diffusion process over a ring-shaped manifold. We also use our method to demonstrate how inputs can be used to control network dynamics for cognitive flexibility and explore the relationship between representation geometry and network capacity. Our work fits within the broader context of understanding neural computations as dynamics over relatively low-dimensional manifolds formed by correlated patterns of neurons. |
format | Online Article Text |
id | pubmed-7446915 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-74469152020-08-31 Engineering recurrent neural networks from task-relevant manifolds and dynamics Pollock, Eli Jazayeri, Mehrdad PLoS Comput Biol Research Article Many cognitive processes involve transformations of distributed representations in neural populations, creating a need for population-level models. Recurrent neural network models fulfill this need, but there are many open questions about how their connectivity gives rise to dynamics that solve a task. Here, we present a method for finding the connectivity of networks for which the dynamics are specified to solve a task in an interpretable way. We apply our method to a working memory task by synthesizing a network that implements a drift-diffusion process over a ring-shaped manifold. We also use our method to demonstrate how inputs can be used to control network dynamics for cognitive flexibility and explore the relationship between representation geometry and network capacity. Our work fits within the broader context of understanding neural computations as dynamics over relatively low-dimensional manifolds formed by correlated patterns of neurons. Public Library of Science 2020-08-12 /pmc/articles/PMC7446915/ /pubmed/32785228 http://dx.doi.org/10.1371/journal.pcbi.1008128 Text en © 2020 Pollock, Jazayeri 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 Pollock, Eli Jazayeri, Mehrdad Engineering recurrent neural networks from task-relevant manifolds and dynamics |
title | Engineering recurrent neural networks from task-relevant manifolds and dynamics |
title_full | Engineering recurrent neural networks from task-relevant manifolds and dynamics |
title_fullStr | Engineering recurrent neural networks from task-relevant manifolds and dynamics |
title_full_unstemmed | Engineering recurrent neural networks from task-relevant manifolds and dynamics |
title_short | Engineering recurrent neural networks from task-relevant manifolds and dynamics |
title_sort | engineering recurrent neural networks from task-relevant manifolds and dynamics |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7446915/ https://www.ncbi.nlm.nih.gov/pubmed/32785228 http://dx.doi.org/10.1371/journal.pcbi.1008128 |
work_keys_str_mv | AT pollockeli engineeringrecurrentneuralnetworksfromtaskrelevantmanifoldsanddynamics AT jazayerimehrdad engineeringrecurrentneuralnetworksfromtaskrelevantmanifoldsanddynamics |