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Author Notes:

Chethan Pandarinath, Emory University, 101Woodruff Circle Northeast,Atlanta, GA 30322-0001. E-mail: chethan@gatech.edu.

We thank Steven Chase, Chandramouli Chandrasekaran, Juan Gallego, Matthew Kaufman, Daniel O'Shea, David Sussillo, Sergey Stavisky, Xulu Sun, Eric Trautmann, Jessica Verhein, Saurabh Vyas, Megan Wang, and Byron Yu for their feedback on the paper.

The authors declare no competing financial interests.

Subjects:

Research Funding:

This work was supported by a Burroughs Wellcome Fund Collaborative Research Travel Grant (C.P.); and NIH NINDS R01NS053603 (L.E.M.).

Keywords:

  • Science & Technology
  • Life Sciences & Biomedicine
  • Neurosciences
  • Neurosciences & Neurology
  • motor control
  • motor cortex
  • dynamical systems
  • brain-machine interfaces
  • neural population dynamics
  • machine learning
  • PERFORMANCE NEURAL PROSTHESIS
  • OPTIMAL FEEDBACK-CONTROL
  • MOVEMENT PREPARATION
  • ARM MOVEMENTS
  • POPULATION-DYNAMICS
  • CORTICAL ACTIVITY
  • STATE-SPACE
  • PREPARATORY ACTIVITY
  • ALPHA-MOTONEURONES
  • INTENDED MOVEMENT

Latent Factors and Dynamics in Motor Cortex and Their Application to Brain-Machine Interfaces

Tools:

Journal Title:

Journal of Neuroscience Nursing

Volume:

Volume 38, Number 44

Publisher:

, Pages 9390-9401

Type of Work:

Article | Final Publisher PDF

Abstract:

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.

Copyright information:

© 2018 The Authors.

This is an Open Access work distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).
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