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Nat Neurosci. 2018 Apr;21(4):607-616. doi: 10.1038/s41593-018-0095-3. Epub 2018 Mar 12.

Learning by neural reassociation.

Golub MD1,2,3, Sadtler PT2,4,5, Oby ER2,4,5, Quick KM2,4,5, Ryu SI3,6, Tyler-Kabara EC4,7,8, Batista AP2,4,5, Chase SM9,10, Yu BM11,12,13.

Author information

1
Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA.
2
Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, PA, USA.
3
Department of Electrical Engineering, Stanford University, Stanford, CA, USA.
4
Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA.
5
Systems Neuroscience Institute, University of Pittsburgh, Pittsburgh, PA, USA.
6
Department of Neurosurgery, Palo Alto Medical Foundation, Palo Alto, CA, USA.
7
Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA, USA.
8
Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA, USA.
9
Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, PA, USA. schase@cmu.edu.
10
Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA. schase@cmu.edu.
11
Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA. byronyu@cmu.edu.
12
Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, PA, USA. byronyu@cmu.edu.
13
Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA. byronyu@cmu.edu.

Abstract

Behavior is driven by coordinated activity across a population of neurons. Learning requires the brain to change the neural population activity produced to achieve a given behavioral goal. How does population activity reorganize during learning? We studied intracortical population activity in the primary motor cortex of rhesus macaques during short-term learning in a brain-computer interface (BCI) task. In a BCI, the mapping between neural activity and behavior is exactly known, enabling us to rigorously define hypotheses about neural reorganization during learning. We found that changes in population activity followed a suboptimal neural strategy of reassociation: animals relied on a fixed repertoire of activity patterns and associated those patterns with different movements after learning. These results indicate that the activity patterns that a neural population can generate are even more constrained than previously thought and might explain why it is often difficult to quickly learn to a high level of proficiency.

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