Article | Published:

Neural population dynamics during reaching

Nature volume 487, pages 5156 (05 July 2012) | Download Citation

Abstract

Most theories of motor cortex have assumed that neural activity represents movement parameters. This view derives from what is known about primary visual cortex, where neural activity represents patterns of light. Yet it is unclear how well the analogy between motor and visual cortex holds. Single-neuron responses in motor cortex are complex, and there is marked disagreement regarding which movement parameters are represented. A better analogy might be with other motor systems, where a common principle is rhythmic neural activity. Here we find that motor cortex responses during reaching contain a brief but strong oscillatory component, something quite unexpected for a non-periodic behaviour. Oscillation amplitude and phase followed naturally from the preparatory state, suggesting a mechanistic role for preparatory neural activity. These results demonstrate an unexpected yet surprisingly simple structure in the population response. This underlying structure explains many of the confusing features of individual neural responses.

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Acknowledgements

We are deeply grateful to K. Briggman and W. Kristan for providing data recorded from the leech. We thank M. Risch for animal care, S. Eisensee for administrative support, and D. Haven and B. Oskotsky for information technology support. We thank Z. Ghahramani and C. Rasmussen for discussion of jPCA and related methods. We thank D. Sussillo, S. Grossman and M. Sahani for analysis suggestions and commentary on the manuscript. This work was supported by a Helen Hay Whitney postdoctoral fellowship and National Institutes of Health (NIH) postdoctoral training fellowship (M.M.C.), the Burroughs Wellcome Fund Career Awards in the Biomedical Sciences (M.M.C., K.V.S.), Engineering and Physical Sciences Research Council grant EP/H019472/1 and the McDonnell Center (J.P.C.), a National Science Foundation graduate research fellowship (M.T.K.), a Texas Instruments Stanford Graduate Fellowship (J.D.F.), a Paul and Daisy Soros Fellowship (P.N.), the Stanford Medical Scientist Training Program (P.N.), and these awards to K.V.S.: NIH Director’s Pioneer Award (1DP1OD006409), NIH NINDS EUREKA Award (R01-NS066311), NIH NINDS BRP (R01-NS064318), NIH NINDS CRCNS (R01-NS054283), DARPA-DSO REPAIR (N66001-10-C-2010), Stanford Center for Integrated Systems, NSF Center for Neuromorphic Systems Engineering at Caltech, Office of Naval Research, and the Whitaker Foundation, the McKnight Foundation, the Sloan Foundation and the Weston Havens Foundation.

Author information

Author notes

    • Mark M. Churchland
    •  & John P. Cunningham

    These authors contributed equally to this work.

Affiliations

  1. Department of Neuroscience, Kavli Institute for Brain Science, David Mahoney Center, Columbia University Medical Center, New York, New York 10032, USA

    • Mark M. Churchland
  2. Department of Electrical Engineering, Stanford University, Stanford, California 94305, USA

    • Mark M. Churchland
    • , Matthew T. Kaufman
    • , Justin D. Foster
    • , Stephen I. Ryu
    •  & Krishna V. Shenoy
  3. Neurosciences Program, Stanford University, Stanford, California 94305, USA

    • Mark M. Churchland
    • , Matthew T. Kaufman
    •  & Krishna V. Shenoy
  4. Department of Biomedical Engineering, Washington University in St Louis, St Louis, Missouri 63130, USA

    • John P. Cunningham
  5. Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, UK

    • John P. Cunningham
  6. Department of Bioengineering, Stanford University, Stanford, California 94705, USA

    • Paul Nuyujukian
    •  & Krishna V. Shenoy
  7. Stanford University School of Medicine, Stanford, California 94305, USA

    • Paul Nuyujukian
  8. Department of Neurosurgery, Palo Alto Medical Foundation, Palo Alto, California 94301, USA

    • Stephen I. Ryu
  9. Department of Neurobiology, Stanford University School of Medicine, Stanford, California 94305, USA

    • Krishna V. Shenoy

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Contributions

The jPCA method was designed by J.P.C. and M.M.C. M.M.C. and M.T.K. collected data from the reaching monkeys. J.D.F. and P.N. collected data from the walking monkey. S.I.R. led the array implantation surgeries. K.V.S. contributed to all aspects of the work. All authors discussed the results and commented on the analyses and manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Mark M. Churchland.

Supplementary information

PDF files

  1. 1.

    Supplementary Information

    This file contains Supplementary Figures 1-11, Supplementary Text and Data, legends for Supplementary Movies 1-5 and additional references.

Videos

  1. 1.

    Supplementary Movie 1

    This move shows the neural trajectory in the walking monkey (2× real time) - see Supplementary Information file for full legend.

  2. 2.

    Supplementary Movie 2

    This movie Illustrates how the PCA axes were rotated to find the jPCA projection - see Supplementary Information file for full legend.

  3. 3.

    Supplementary Movie 3

    This movie shows the jPCA projections as a function of time - see Supplementary Information file for full legend.

  4. 4.

    Supplementary Movie 4

    This movie shows the jPCA projections as a function of time after applying the shuffle control - see Supplementary Information file for full legend.

  5. 5.

    Supplementary Movie 5

    This movie shows the jPCA projections as a function of time for populations of muscle recordings - see Supplementary Information file for full legend.

About this article

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DOI

https://doi.org/10.1038/nature11129

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