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

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1 Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, United States
2 Department of Neurosurgery, Emory University, Atlanta, GA, United States
3 Department of Neuroscience, Columbia University, New York, NY, United States
4 Center for Theoretical Neuroscience, Columbia University, New York, NY, United States
5 Grossman Center for the Statistics of Mind, Columbia University, New York, NY, United States
6 Zuckerman Institute, Columbia University, New York, NY, United States
7 Department of Physiology, Northwestern University, Chicago, IL, United States
8 Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States
9 Department of Electrical and Computer Engineering, University of California, Los Angeles, Los Angeles, CA, United States
10 Neurosciences Program, University of California, Los Angeles, Los Angeles, CA, United States
DOI
10.7287/peerj.preprints.27217v1
Published
Accepted
Subject Areas
Bioengineering, Neuroscience, Computational Science, Data Mining and Machine Learning
Keywords
Motor cortex, Neural population dynamics, Brain-machine interface
Copyright
© 2018 Pandarinath et al.
Licence
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
Cite this article
Pandarinath C, Ames KC, Russo AA, Farshchian A, Miller LE, Dyer EL, Kao JC. 2018. Latent factors and dynamics in motor cortex and their application to brain-machine interfaces. PeerJ Preprints 6:e27217v1

Abstract

In the fifty years since Evarts first recorded single neurons in motor cortex of behaving monkeys, great effort has been devoted to understanding their relation 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 network-level phenomena 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 leverage these factors to improve the performance of brain-machine interfaces, promising to make these findings broadly relevant to neuroengineering as well as systems neuroscience.

Author Comment

This is a preprint submission to PeerJ Preprints.