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Nat Neurosci. 2015 Jul;18(7):1025-33. doi: 10.1038/nn.4042. Epub 2015 Jun 15.

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

Author information

  • 1Department of Electrical Engineering and Neurosciences Program, Stanford University, Stanford, California, USA.
  • 2Department 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.
  • 31] Department of Electrical Engineering and Neurosciences Program, Stanford University, Stanford, California, USA. [2] Departments of Bioengineering and Neurobiology, Stanford Neurosciences Institute and Bio-X Program, Stanford University, Stanford, California, USA.

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.

PMID:
26075643
PMCID:
PMC5113297
DOI:
10.1038/nn.4042
[PubMed - indexed for MEDLINE]
Free PMC Article
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