Latent factors and dynamics in motor cortex and their application to brain-machine interfaces
A peer-reviewed article of this Preprint also exists.
Author and article information
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.
Cite this as
2018. Latent factors and dynamics in motor cortex and their application to brain-machine interfaces. PeerJ Preprints 6:e27217v1 https://doi.org/10.7287/peerj.preprints.27217v1note This preprint is not peer-reviewed. You may wish to reference the subsequent peer-reviewed version of this article.
Author comment
This is a preprint submission to PeerJ Preprints.
Sections
Additional Information
Competing Interests
The authors declare that they have no competing interests.
Author Contributions
Chethan Pandarinath prepared figures and/or tables, authored or reviewed drafts of the paper, approved the final draft.
K. Cora Ames prepared figures and/or tables, authored or reviewed drafts of the paper, approved the final draft.
Abigail A Russo prepared figures and/or tables, authored or reviewed drafts of the paper, approved the final draft.
Ali Farshchian prepared figures and/or tables, authored or reviewed drafts of the paper, approved the final draft.
Lee E Miller authored or reviewed drafts of the paper, approved the final draft.
Eva L Dyer prepared figures and/or tables, authored or reviewed drafts of the paper, approved the final draft.
Jonathon C Kao prepared figures and/or tables, authored or reviewed drafts of the paper, approved the final draft.
Data Deposition
The following information was supplied regarding data availability:
The preparation of this review article did not generate any data or code.
Funding
This work was supported by a Burroughs Wellcome Fund Collaborative Research Travel Grant and NSF NCS 1835364 (CP), and NIH NINDS R01NS053603 and NSF NCS 1835345 (LEM). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
