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

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

SUBMITTER: Saxena S 

PROVIDER: S-EPMC9197394 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature