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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This work was supported by NIH R01 HD071686 (A.P.B., B.M.Y. and S.M.C.), NSF NCS BCS1533672 (S.M.C., B.M.Y. and A.P.B.), NSF CAREER award IOS1553252 (S.M.C.), NIH CRCNS R01 NS105318 (B.M.Y. and A.P.B.), Craig H. Neilsen Foundation 280028 (B.M.Y., S.M.C. and A.P.B.), Pennsylvania Department of Health Research Formula Grant SAP 4100077048 under the Commonwealth Universal Research Enhancement program (S.M.C. and B.M.Y.) and Simons Foundation 364994 (B.M.Y.).
The authors declare no competing interests.
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Golub, M.D., Sadtler, P.T., Oby, E.R. et al. Learning by neural reassociation. Nat Neurosci 21, 607–616 (2018). https://doi.org/10.1038/s41593-018-0095-3
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