Motor Cortex Embeds Muscle-like Commands in an Untangled Population Response

Abigail A. Russo, Sean R. Bittner, Sean M. Perkins, Jeffrey S. Seely, Brian M. London, Antonio H. Lara, Andrew Miri, Najja J. Marshall, Adam Kohn, Thomas M. Jessell, Laurence F. Abbott, John P. Cunningham, Mark M. Churchland

Research output: Contribution to journalArticlepeer-review

125 Scopus citations


Primate motor cortex projects to spinal interneurons and motoneurons, suggesting that motor cortex activity may be dominated by muscle-like commands. Observations during reaching lend support to this view, but evidence remains ambiguous and much debated. To provide a different perspective, we employed a novel behavioral paradigm that facilitates comparison between time-evolving neural and muscle activity. We found that single motor cortex neurons displayed many muscle-like properties, but the structure of population activity was not muscle-like. Unlike muscle activity, neural activity was structured to avoid “tangling”: moments where similar activity patterns led to dissimilar future patterns. Avoidance of tangling was present across tasks and species. Network models revealed a potential reason for this consistent feature: low tangling confers noise robustness. Finally, we were able to predict motor cortex activity from muscle activity by leveraging the hypothesis that muscle-like commands are embedded in additional structure that yields low tangling. Using a novel extended movement task, Russo et al. show that neural activity in motor cortex is dominated by non-muscle-like signals. A computational approach reveals that these dominant features are expected and can be predicted given the constraint that neural activity produces muscle commands while obeying a smooth flow-field.

Original languageEnglish (US)
Pages (from-to)953-966.e8
Issue number4
StatePublished - Feb 21 2018


  • motor control
  • motor cortex
  • movement generation
  • neural dynamics
  • neural network
  • pattern generation
  • rhythmic movement

ASJC Scopus subject areas

  • Neuroscience(all)


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