Dynamic Structure Of Motor Cortical Neuron Co-Activity Carries Behaviorally Relevant Information

Abstract

Skillful, voluntary movements are underpinned by computations performed by networks of interconnected neurons in the primary motor cortex (M1). Computations are reflected by patterns of co-activity between neurons. Using spike time correlations, co-activity can be represented as functional networks (FNs). Here, we show that the structure of FNs constructed from instructed-delay reach trials in non-human primates are behaviorally specific: low dimensional embedding and graph alignment scores show that FNs constructed from closer target reach distances are also closer in network space. We next constructed temporal FNs using short intervals across a trial. We find that temporal FNs traverse a low-dimensional subspace in a reach-specific trajectory. Alignment scores show that FNs become separable and correspondingly, decodable shortly after the instruction cue. Finally, we observe that reciprocal connections in FNs transiently decrease following the instruction cue, suggesting the network momentarily switches from a recurrent system to one that is more feedforward.