Motor cortex activity across movement speeds is predicted by network-level strategies for generating muscle activity

Abstract
Data availability
Article and author information
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Abstract

Learned movements can be skillfully performed at different paces. What neural strategies produce this flexibility? Can they be predicted and understood by network modeling? We trained monkeys to perform a cycling task at different speeds, and trained artificial recurrent networks to generate the empirical muscle-activity patterns. Network solutions reflected the principle that smooth well-behaved dynamics require low trajectory tangling. Network solutions had a consistent form, which yielded quantitative and qualitative predictions. To evaluate predictions, we analyzed motor cortex activity recorded during the same task. Responses supported the hypothesis that the dominant neural signals reflect not muscle activity, but network-level strategies for generating muscle activity. Single-neuron responses were better accounted for by network activity than by muscle activity. Similarly, neural population trajectories shared their organization not with muscle trajectories, but with network solutions. Thus, cortical activity could be understood based on the need to generate muscle activity via dynamics that allow smooth, robust control over movement speed.

Data availability

Neural and EMG data have been deposited in figshare: https://figshare.com/s/b2a0557c239a1010d8ea

The following data sets were generated

1. Russo AA
2. Churchland MM
(2022) Cousteau_cn_new.mat
figshare.

https://figshare.com/s/b2a0557c239a1010d8ea
1. Russo AA
2. Churchland MM
(2022) Drake_cn_new.mat
figshare.

https://figshare.com/s/b2a0557c239a1010d8ea

Article and author information

Author details

Shreya Saxena

Department of Electrical and Computer Engineering, University of Florida, Gainesville, United States

Competing interests
The authors declare that no competing interests exist.
Abigail A Russo

Department of Neuroscience, Columbia University, New York, United States

Competing interests
The authors declare that no competing interests exist.
John Cunningham

Center for Theoretical Neuroscience, Columbia University, New York, United States

Competing interests
The authors declare that no competing interests exist.
Mark M Churchland

Department of Neuroscience, Columbia University, New York, United States

For correspondence
mc3502@columbia.edu

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0001-9123-6526

Funding

Grossman Center for the Statistics of Mind

Mark M Churchland

Alfred P. Sloan Foundation (FG-2015-65496)

Mark M Churchland

Simons Foundation (542963)

Mark M Churchland

NIH (1U19NS104649)

Mark M Churchland

NIH (5T32NS064929)

Mark M Churchland

Kavli Foundation

Mark M Churchland

Simons Foundation (325171)

Mark M Churchland

Swiss National Science Foundation (P2SKP2 178197)

Mark M Churchland

Swiss National Science Foundation (P400P2 186759)

Mark M Churchland

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Animal experimentation: All protocols were in accord with the National Institutes of Health guidelines and approved by the Columbia University Institutional Animal Care and Use Committee. (Protocol number AC-AABE3550)

Copyright

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.