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A neural network that finds a naturalistic solution for the production of muscle activity

Nature Neuroscience volume 18, pages 10251033 (2015) | Download Citation

Abstract

It remains an open question how neural responses in motor cortex relate to movement. We explored the hypothesis that motor cortex reflects dynamics appropriate for generating temporally patterned outgoing commands. To formalize this hypothesis, we trained recurrent neural networks to reproduce the muscle activity of reaching monkeys. Models had to infer dynamics that could transform simple inputs into temporally and spatially complex patterns of muscle activity. Analysis of trained models revealed that the natural dynamical solution was a low-dimensional oscillator that generated the necessary multiphasic commands. This solution closely resembled, at both the single-neuron and population levels, what was observed in neural recordings from the same monkeys. Notably, data and simulations agreed only when models were optimized to find simple solutions. An appealing interpretation is that the empirically observed dynamics of motor cortex may reflect a simple solution to the problem of generating temporally patterned descending commands.

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Acknowledgements

We thank L. Abbott and S. Ganguli for insightful conversations. We thank S.I. Ryu for electrode array implantation surgical assistance, M. Mazariegos and J. Aguayo for surgical assistance and veterinary care, B. Otskotsky for IT support, and S. Eisensee, B. Davis and E. Castaneda for administrative assistance. This work was supported by The Searle Scholars Program (M.M.C.), The Sloan Foundation (M.M.C.), The McKnight Foundation (M.M.C.), The Grossman Charitable Trust (M.M.C.), US National Institutes of Health (NIH) Director's New Innovator Award DP2 NS083037 (M.M.C.), an NIH R01 MH93338-02 grant (M.M.C.), Burroughs Wellcome Fund Career Awards in the Biomedical Sciences (M.M.C., K.V.S.), a National Science Foundation graduate research fellowship (M.T.K.), an NIH T-RO1 Award R01NS076460 (K.V.S.), an NIH Director's Pioneer Award 8DP1HD075623 (K.V.S.) and a DARPA REPAIR Award N66001-10-C-2010 (K.V.S.).

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Author notes

    • Matthew T Kaufman

    Present address: Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, USA.

Affiliations

  1. Department of Electrical Engineering and Neurosciences Program, Stanford University, Stanford, California, USA.

    • David Sussillo
    • , Matthew T Kaufman
    •  & Krishna V Shenoy
  2. Department of Neuroscience, Grossman Center for the Statistics of Mind, David Mahoney Center for Brain and Behavior Research, Kavli Institute for Brain Science, Columbia University Medical Center, New York, New York, USA.

    • Mark M Churchland
  3. Departments of Bioengineering and Neurobiology, Stanford Neurosciences Institute and Bio-X Program, Stanford University, Stanford, California, USA.

    • Krishna V Shenoy

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Contributions

D.C.S. and M.M.C. conceived the study and designed the simulations. D.C.S. performed the simulations and analyses. M.M.C. and M.T.K. designed and performed the experiments. D.C.S. and M.M.C. wrote the manuscript. K.V.S. supervised the study, analyses and manuscript writing.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to David Sussillo.

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DOI

https://doi.org/10.1038/nn.4042

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