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Flexible Modulation of Neural Variance Facilitates Neuroprosthetic Skill Learning

Albert K. You, Bing Liu, Abhimanyu Singhal, Suraj Gowda, Helene Moorman, Amy Orsborn, Karunesh Ganguly, Jose M. Carmena
doi: https://doi.org/10.1101/817346
Albert K. You
1UC Berkeley-UCSF Joint Graduate Program in Bioengineering, University of California, Berkeley, Berkeley, CA 94720, USA
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Bing Liu
2Department of Neurobiology, Duke University, Durham, NC 27710, USA
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Abhimanyu Singhal
3Department of Physics, University of California, Berkeley, Berkeley, CA 94720, USA
4Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA 94720, USA
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Suraj Gowda
4Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA 94720, USA
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Helene Moorman
5Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA 94720, USA
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Amy Orsborn
6Department of Electrical and Computer Engineering, University of Washington, Seattle, WA 98195, USA
7Department of Bioengineering, University of Washington, Seattle, WA 98195, USA
8Washington National Primate Research Center, Seattle, WA 98121, USA
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Karunesh Ganguly
9Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94143, USA
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Jose M. Carmena
1UC Berkeley-UCSF Joint Graduate Program in Bioengineering, University of California, Berkeley, Berkeley, CA 94720, USA
4Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA 94720, USA
5Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA 94720, USA
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  • For correspondence: jcarmena@berkeley.edu
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SUMMARY

One hallmark of natural motor control is the brain’s ability to adapt to perturbations ranging from temporary visual-motor rotations to paresis caused by stroke. These adaptations require modifications of learned neural patterns that can span the time-course of minutes to months. Previous work has shown that populations of neurons fire on coordinated low-dimensional subspaces that are resistant to changes, and perturbations requiring neural activity to move outside of these subspaces are difficult to learn. Subsequently, perturbations that remain within the neural subspace are easier to adapt to. However, it is unclear how motor cortex might respond to perturbations whilst still learning. To answer this question, five nonhuman primates were used in three brain-machine interface (BMI) experiments, which allowed us to track how specific populations of neurons changed firing patterns as task performance improved. In each experiment, neural intentions were estimated with biomimetic decoders that were periodically refit, creating perturbations throughout learning. We found that decoder perturbations caused neurons to increase exploratory patterns on within-day timescales without hindering previously consolidated patterns regardless of task performance. The flexible modulation of these exploratory patterns in contrast to relatively stable consolidated activity suggests a concomitant exploration-exploitation strategy that adapts existing neural patterns during learning.

Copyright 
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 October 24, 2019.
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Flexible Modulation of Neural Variance Facilitates Neuroprosthetic Skill Learning
Albert K. You, Bing Liu, Abhimanyu Singhal, Suraj Gowda, Helene Moorman, Amy Orsborn, Karunesh Ganguly, Jose M. Carmena
bioRxiv 817346; doi: https://doi.org/10.1101/817346
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Flexible Modulation of Neural Variance Facilitates Neuroprosthetic Skill Learning
Albert K. You, Bing Liu, Abhimanyu Singhal, Suraj Gowda, Helene Moorman, Amy Orsborn, Karunesh Ganguly, Jose M. Carmena
bioRxiv 817346; doi: https://doi.org/10.1101/817346

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