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Learning is shaped by abrupt changes in neural engagement

View ORCID ProfileJay A. Hennig, Emily R. Oby, Matthew D. Golub, Lindsay A. Bahureksa, Patrick T. Sadtler, Kristin M. Quick, Stephen I. Ryu, Elizabeth C. Tyler-Kabara, Aaron P. Batista, Steven M. Chase, Byron M. Yu
doi: https://doi.org/10.1101/2020.05.24.112714
Jay A. Hennig
1Neuroscience Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
2Center for the Neural Basis of Cognition, Pittsburgh, Pennsylvania 15213, USA
3Machine Learning Department, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
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  • ORCID record for Jay A. Hennig
  • For correspondence: jhennig@cmu.edu
Emily R. Oby
2Center for the Neural Basis of Cognition, Pittsburgh, Pennsylvania 15213, USA
4Department of Neurobiology, University of Pittsburgh, Pittsburgh, Pennsylvania 15213, USA
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Matthew D. Golub
2Center for the Neural Basis of Cognition, Pittsburgh, Pennsylvania 15213, USA
5Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
6Department of Electrical Engineering, Stanford University, Stanford, California 94305, USA
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Lindsay A. Bahureksa
2Center for the Neural Basis of Cognition, Pittsburgh, Pennsylvania 15213, USA
7Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
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Patrick T. Sadtler
2Center for the Neural Basis of Cognition, Pittsburgh, Pennsylvania 15213, USA
8Department of Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15213, USA
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Kristin M. Quick
2Center for the Neural Basis of Cognition, Pittsburgh, Pennsylvania 15213, USA
8Department of Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15213, USA
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Stephen I. Ryu
6Department of Electrical Engineering, Stanford University, Stanford, California 94305, USA
9Department of Neurosurgery, Palo Alto Medical Foundation, Palo Alto, California 94301, USA
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Elizabeth C. Tyler-Kabara
2Center for the Neural Basis of Cognition, Pittsburgh, Pennsylvania 15213, USA
10Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, Pennsylvania 15213, USA
11Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, Pennsylvania 15213, USA
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Aaron P. Batista
2Center for the Neural Basis of Cognition, Pittsburgh, Pennsylvania 15213, USA
8Department of Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15213, USA
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Steven M. Chase
1Neuroscience Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
2Center for the Neural Basis of Cognition, Pittsburgh, Pennsylvania 15213, USA
7Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
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Byron M. Yu
1Neuroscience Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
2Center for the Neural Basis of Cognition, Pittsburgh, Pennsylvania 15213, USA
5Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
7Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
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Abstract

Internal states such as arousal, attention, and motivation are known to modulate brain-wide neural activity, but how these processes interact with learning is not well understood. During learning, the brain must modify the neural activity it produces to improve behavioral performance. How do internal states affect the evolution of this learning process? Using a brain-computer interface (BCI) learning paradigm in non-human primates, we identified large fluctuations in neural population activity in motor cortex (M1) indicative of arousal-like internal state changes. These fluctuations drove population activity along dimensions we term neural engagement axes. Neural engagement increased abruptly at the start of learning, and then gradually retreated. In a BCI, the causal relationship between neural activity and behavior is known. This allowed us to understand how these changes impacted behavioral performance for different task goals. We found that neural engagement interacted with learning, helping to explain why animals learned some task goals more quickly than others.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted May 25, 2020.
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Learning is shaped by abrupt changes in neural engagement
Jay A. Hennig, Emily R. Oby, Matthew D. Golub, Lindsay A. Bahureksa, Patrick T. Sadtler, Kristin M. Quick, Stephen I. Ryu, Elizabeth C. Tyler-Kabara, Aaron P. Batista, Steven M. Chase, Byron M. Yu
bioRxiv 2020.05.24.112714; doi: https://doi.org/10.1101/2020.05.24.112714
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Learning is shaped by abrupt changes in neural engagement
Jay A. Hennig, Emily R. Oby, Matthew D. Golub, Lindsay A. Bahureksa, Patrick T. Sadtler, Kristin M. Quick, Stephen I. Ryu, Elizabeth C. Tyler-Kabara, Aaron P. Batista, Steven M. Chase, Byron M. Yu
bioRxiv 2020.05.24.112714; doi: https://doi.org/10.1101/2020.05.24.112714

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