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Learning by neural reassociation

Nature Neuroscience volume 21, pages 607–616 (2018)Cite this article

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A Publisher Correction to this article was published on 05 July 2018

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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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Fig. 1: BCI learning experiment.
Fig. 2: Conceptual illustrations of three hypothesized neural strategies of learning.
Fig. 3: Visualization of population activity patterns from an example experiment (N20160728).
Fig. 4: Consistent with reassociation, the overall neural repertoire shows minimal changes during short-term learning.
Fig. 5: Consistent with reassociation, population covariability does not change along key dimensions of the intrinsic manifold.
Fig. 6: Consistent with reassociation, population covariability does not track perturbations to the BCI mapping.
Fig. 7: Behavioral learning is consistent with reassociation.
Fig. 8: Partial realignment and subselection are not consistent with the data.

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Change history

  • 05 July 2018

    In the version of this article initially published, equation (10) contained cos Θ instead of sin Θ as the bottom element of the right-hand vector. The error has been corrected in the HTML and PDF versions of the article.

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Acknowledgements

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.).

Author information

Author notes

  1. These authors contributed equally: Steven M. Chase, Byron M. Yu.

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA

    Matthew D. Golub & Byron M. Yu

  2. Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, PA, USA

    Matthew D. Golub, Patrick T. Sadtler, Emily R. Oby, Kristin M. Quick, Aaron P. Batista, Steven M. Chase & Byron M. Yu

  3. Department of Electrical Engineering, Stanford University, Stanford, CA, USA

    Matthew D. Golub & Stephen I. Ryu

  4. Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA

    Patrick T. Sadtler, Emily R. Oby, Kristin M. Quick, Elizabeth C. Tyler-Kabara & Aaron P. Batista

  5. Systems Neuroscience Institute, University of Pittsburgh, Pittsburgh, PA, USA

    Patrick T. Sadtler, Emily R. Oby, Kristin M. Quick & Aaron P. Batista

  6. Department of Neurosurgery, Palo Alto Medical Foundation, Palo Alto, CA, USA

    Stephen I. Ryu

  7. Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA, USA

    Elizabeth C. Tyler-Kabara

  8. Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA, USA

    Elizabeth C. Tyler-Kabara

  9. Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA

    Steven M. Chase & Byron M. Yu

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Contributions

M.D.G., B.M.Y., S.M.C. and A.P.B. designed the analyses and discussed the results. M.D.G. performed all analyses and wrote the paper. P.T.S., K.M.Q., M.D.G., S.M.C., B.M.Y. and A.P.B. designed the animal experiments. P.T.S. and E.R.O. performed the animal experiments. S.I.R., E.C.T.-K. and E.R.O. performed the animal surgeries. All authors commented on the manuscript. B.M.Y. and S.M.C. contributed equally to this work.

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Correspondence to Steven M. Chase or Byron M. Yu.

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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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