Cargando…
Constraints on neural redundancy
Millions of neurons drive the activity of hundreds of muscles, meaning many different neural population activity patterns could generate the same movement. Studies have suggested that these redundant (i.e. behaviorally equivalent) activity patterns may be beneficial for neural computation. However,...
Autores principales: | , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
eLife Sciences Publications, Ltd
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6130976/ https://www.ncbi.nlm.nih.gov/pubmed/30109848 http://dx.doi.org/10.7554/eLife.36774 |
_version_ | 1783354032713105408 |
---|---|
author | Hennig, Jay A Golub, Matthew D Lund, Peter J Sadtler, Patrick T Oby, Emily R Quick, Kristin M Ryu, Stephen I Tyler-Kabara, Elizabeth C Batista, Aaron P Yu, Byron M Chase, Steven M |
author_facet | Hennig, Jay A Golub, Matthew D Lund, Peter J Sadtler, Patrick T Oby, Emily R Quick, Kristin M Ryu, Stephen I Tyler-Kabara, Elizabeth C Batista, Aaron P Yu, Byron M Chase, Steven M |
author_sort | Hennig, Jay A |
collection | PubMed |
description | Millions of neurons drive the activity of hundreds of muscles, meaning many different neural population activity patterns could generate the same movement. Studies have suggested that these redundant (i.e. behaviorally equivalent) activity patterns may be beneficial for neural computation. However, it is unknown what constraints may limit the selection of different redundant activity patterns. We leveraged a brain-computer interface, allowing us to define precisely which neural activity patterns were redundant. Rhesus monkeys made cursor movements by modulating neural activity in primary motor cortex. We attempted to predict the observed distribution of redundant neural activity. Principles inspired by work on muscular redundancy did not accurately predict these distributions. Surprisingly, the distributions of redundant neural activity and task-relevant activity were coupled, which enabled accurate predictions of the distributions of redundant activity. This suggests limits on the extent to which redundancy may be exploited by the brain for computation. |
format | Online Article Text |
id | pubmed-6130976 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | eLife Sciences Publications, Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-61309762018-09-12 Constraints on neural redundancy Hennig, Jay A Golub, Matthew D Lund, Peter J Sadtler, Patrick T Oby, Emily R Quick, Kristin M Ryu, Stephen I Tyler-Kabara, Elizabeth C Batista, Aaron P Yu, Byron M Chase, Steven M eLife Neuroscience Millions of neurons drive the activity of hundreds of muscles, meaning many different neural population activity patterns could generate the same movement. Studies have suggested that these redundant (i.e. behaviorally equivalent) activity patterns may be beneficial for neural computation. However, it is unknown what constraints may limit the selection of different redundant activity patterns. We leveraged a brain-computer interface, allowing us to define precisely which neural activity patterns were redundant. Rhesus monkeys made cursor movements by modulating neural activity in primary motor cortex. We attempted to predict the observed distribution of redundant neural activity. Principles inspired by work on muscular redundancy did not accurately predict these distributions. Surprisingly, the distributions of redundant neural activity and task-relevant activity were coupled, which enabled accurate predictions of the distributions of redundant activity. This suggests limits on the extent to which redundancy may be exploited by the brain for computation. eLife Sciences Publications, Ltd 2018-08-15 /pmc/articles/PMC6130976/ /pubmed/30109848 http://dx.doi.org/10.7554/eLife.36774 Text en © 2018, Hennig et al http://creativecommons.org/licenses/by/4.0/ http://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited. |
spellingShingle | Neuroscience Hennig, Jay A Golub, Matthew D Lund, Peter J Sadtler, Patrick T Oby, Emily R Quick, Kristin M Ryu, Stephen I Tyler-Kabara, Elizabeth C Batista, Aaron P Yu, Byron M Chase, Steven M Constraints on neural redundancy |
title | Constraints on neural redundancy |
title_full | Constraints on neural redundancy |
title_fullStr | Constraints on neural redundancy |
title_full_unstemmed | Constraints on neural redundancy |
title_short | Constraints on neural redundancy |
title_sort | constraints on neural redundancy |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6130976/ https://www.ncbi.nlm.nih.gov/pubmed/30109848 http://dx.doi.org/10.7554/eLife.36774 |
work_keys_str_mv | AT hennigjaya constraintsonneuralredundancy AT golubmatthewd constraintsonneuralredundancy AT lundpeterj constraintsonneuralredundancy AT sadtlerpatrickt constraintsonneuralredundancy AT obyemilyr constraintsonneuralredundancy AT quickkristinm constraintsonneuralredundancy AT ryustepheni constraintsonneuralredundancy AT tylerkabaraelizabethc constraintsonneuralredundancy AT batistaaaronp constraintsonneuralredundancy AT yubyronm constraintsonneuralredundancy AT chasestevenm constraintsonneuralredundancy |