Volitional motor control is inherently a neural population phenomenon: to generate movements, neural activity from collections of neurons across multiple brain areas must be coordinated to result in precisely timed muscle activation patterns. This coordination may be characterized by statistical dependencies in the tuning of groups of neurons, the so-called signal correlation, which arises from network constraints such as common inputs. In motor control, these common inputs relate to the cognitive and behavioral factors underlying movement generation. A major thrust of our lab is to investigate how these signal correlations relate to various features of the task, like feedback, redundancy, and behavioral constraints. Using a coupling of experimental and analytical techniques, including operant conditioning, latent variable analysis, and brain-computer interface decoding, we are building statistical models of the correlated structure of neural activity.

Funded by the PA Department of Health Commonwealth Universal Research Enhancement (CURE) Program