Latent factors and dynamics in motor cortex and their application to brain–machine interfaces

Chethan Pandarinath*, K. Cora Ames, Abigail A. Russo, Ali Farshchian, Lee E. Miller, Eva L. Dyer, Jonathan C. Kao

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

16 Scopus citations


In the 1960s, Evarts first recorded the activity of single neurons in motor cortex of behaving monkeys (Evarts, 1968). In the 50 years since, great effort has been devoted to understanding how single neuron activity relates to movement. Yet these single neurons exist within a vast network, the nature of which has been largely inaccessible. With advances in recording technologies, algorithms, and computational power, the ability to study these networks is increasing exponentially. Recent experimental results suggest that the dynamical properties of these networks are critical to movement planning and execution. Here we discuss this dynamical systems perspective and how it is reshaping our understanding of the motor cortices. Following an overview of key studies in motor cortex, we discuss techniques to uncover the “latent factors” underlying observed neural population activity. Finally, we discuss efforts to use these factors to improve the performance of brain–machine interfaces, promising to make these findings broadly relevant to neuroengineering as well as systems neuroscience.

Original languageEnglish (US)
Pages (from-to)9390-9401
Number of pages12
JournalJournal of Neuroscience
Issue number44
StatePublished - Oct 31 2018


  • Brain-machine interfaces
  • Dynamical systems
  • Machine learning
  • Motor control
  • Motor cortex
  • Neural population dynamics

ASJC Scopus subject areas

  • Neuroscience(all)

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