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The brain uses invariant dynamics to generalize outputs across movements

View ORCID ProfileVivek R. Athalye, View ORCID ProfilePreeya Khanna, Suraj Gowda, View ORCID ProfileAmy L. Orsborn, Rui M. Costa, Jose M. Carmena
doi: https://doi.org/10.1101/2021.08.27.457931
Vivek R. Athalye
1Zuckerman Mind Brain Behavior Institute, Departments of Neuroscience and Neurology, Columbia University; New York, NY, USA
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  • ORCID record for Vivek R. Athalye
Preeya Khanna
2Department of Neurology, University of California, San Francisco; San Francisco, CA, USA
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Suraj Gowda
4Department of Electrical Engineering and Computer Sciences, University of California, Berkeley; Berkeley, CA, USA
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Amy L. Orsborn
3Departments of Bioengineering, Electrical and Computer Engineering, University of Washington, Seattle; Seattle, WA, USA
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  • ORCID record for Amy L. Orsborn
Rui M. Costa
1Zuckerman Mind Brain Behavior Institute, Departments of Neuroscience and Neurology, Columbia University; New York, NY, USA
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  • For correspondence: rc3031@columbia.edu jcarmena@berkeley.edu
Jose M. Carmena
4Department of Electrical Engineering and Computer Sciences, University of California, Berkeley; Berkeley, CA, USA
5Helen Wills Neuroscience Institute, University of California, Berkeley; Berkeley, CA, USA
6UC Berkeley-UCSF Joint Graduate Program in Bioengineering, University of California, Berkeley; Berkeley, CA, USA
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  • For correspondence: rc3031@columbia.edu jcarmena@berkeley.edu
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Abstract

The nervous system uses a repertoire of outputs to produce diverse movements. Thus, the brain must solve how to issue and transition the same outputs in different movements. A recent proposal states that network connectivity constrains the transitions of neural activity to follow invariant rules across different movements, which we term ‘invariant dynamics’. However, it is unknown whether invariant dynamics are actually used to drive and generalize outputs across movements, and what advantage they provide for controlling movement. Using a brain-machine interface that transformed motor cortex activity into outputs for a neuroprosthetic cursor, we discovered that the same output is issued by different activity patterns in different movements. These distinct patterns then transition according to a model of invariant dynamics, leading to patterns that drive distinct future outputs. Optimal control theory revealed this use of invariant dynamics reduces the feedback input needed to control movement. Our results demonstrate that the brain uses invariant dynamics to generalize outputs across movements.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • ↵‡ Senior author

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted August 28, 2021.
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The brain uses invariant dynamics to generalize outputs across movements
Vivek R. Athalye, Preeya Khanna, Suraj Gowda, Amy L. Orsborn, Rui M. Costa, Jose M. Carmena
bioRxiv 2021.08.27.457931; doi: https://doi.org/10.1101/2021.08.27.457931
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The brain uses invariant dynamics to generalize outputs across movements
Vivek R. Athalye, Preeya Khanna, Suraj Gowda, Amy L. Orsborn, Rui M. Costa, Jose M. Carmena
bioRxiv 2021.08.27.457931; doi: https://doi.org/10.1101/2021.08.27.457931

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