Neural manifold under plasticity in a goal driven learning behaviour.

Neural activity is often low dimensional and dominated by only a few prominent neural covariation patterns. It has been hypothesised that these covariation patterns could form the building blocks used for fast and flexible motor control. Supporting this idea, recent experiments have shown that monke...

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Main Authors: Barbara Feulner, Claudia Clopath
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2021-02-01
Series:PLoS Computational Biology
Online Access:https://doi.org/10.1371/journal.pcbi.1008621
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author Barbara Feulner
Claudia Clopath
author_facet Barbara Feulner
Claudia Clopath
author_sort Barbara Feulner
collection DOAJ
description Neural activity is often low dimensional and dominated by only a few prominent neural covariation patterns. It has been hypothesised that these covariation patterns could form the building blocks used for fast and flexible motor control. Supporting this idea, recent experiments have shown that monkeys can learn to adapt their neural activity in motor cortex on a timescale of minutes, given that the change lies within the original low-dimensional subspace, also called neural manifold. However, the neural mechanism underlying this within-manifold adaptation remains unknown. Here, we show in a computational model that modification of recurrent weights, driven by a learned feedback signal, can account for the observed behavioural difference between within- and outside-manifold learning. Our findings give a new perspective, showing that recurrent weight changes do not necessarily lead to change in the neural manifold. On the contrary, successful learning is naturally constrained to a common subspace.
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spelling doaj.art-29f2fbf77d524cb29256c5ebba2fd0542022-12-21T19:21:49ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582021-02-01172e100862110.1371/journal.pcbi.1008621Neural manifold under plasticity in a goal driven learning behaviour.Barbara FeulnerClaudia ClopathNeural activity is often low dimensional and dominated by only a few prominent neural covariation patterns. It has been hypothesised that these covariation patterns could form the building blocks used for fast and flexible motor control. Supporting this idea, recent experiments have shown that monkeys can learn to adapt their neural activity in motor cortex on a timescale of minutes, given that the change lies within the original low-dimensional subspace, also called neural manifold. However, the neural mechanism underlying this within-manifold adaptation remains unknown. Here, we show in a computational model that modification of recurrent weights, driven by a learned feedback signal, can account for the observed behavioural difference between within- and outside-manifold learning. Our findings give a new perspective, showing that recurrent weight changes do not necessarily lead to change in the neural manifold. On the contrary, successful learning is naturally constrained to a common subspace.https://doi.org/10.1371/journal.pcbi.1008621
spellingShingle Barbara Feulner
Claudia Clopath
Neural manifold under plasticity in a goal driven learning behaviour.
PLoS Computational Biology
title Neural manifold under plasticity in a goal driven learning behaviour.
title_full Neural manifold under plasticity in a goal driven learning behaviour.
title_fullStr Neural manifold under plasticity in a goal driven learning behaviour.
title_full_unstemmed Neural manifold under plasticity in a goal driven learning behaviour.
title_short Neural manifold under plasticity in a goal driven learning behaviour.
title_sort neural manifold under plasticity in a goal driven learning behaviour
url https://doi.org/10.1371/journal.pcbi.1008621
work_keys_str_mv AT barbarafeulner neuralmanifoldunderplasticityinagoaldrivenlearningbehaviour
AT claudiaclopath neuralmanifoldunderplasticityinagoaldrivenlearningbehaviour