Practice often and always get ready: a spiking mechanistic model for voluntary motor control

Abstract

In many voluntary movement, neural activities ranging from cortex to spinal cord can be roughly described as the stages of motor intention, preparation, and execution. Recent advances in neuroscience have proposed many theories to understand how motor intention can be transformed into action following these stages, but they still lack a holistic and mechanistic theory to account for the whole process. Here, we try to formulate this question by abstracting two underlying principles: 1) the neural system is specializing the final motor command through a hierarchical network by multitudes of training supervised by the action feedback ("practice often"); 2) prediction is a general mechanism throughout the whole process by providing feedback control for each local layer ("always get ready"). Here we present a theoretical model to regularize voluntary motor control based on these two principles. The model features hierarchical organization and is composed of spiking building blocks based on the previous work in predictive coding and adaptive control theory. By simulating our manual interception paradigm, we show that the network could demonstrate motor preparation and execution, generate desired output trajectory following intention inputs, and exhibit comparable cortical and endpoint dynamics with the empirical data.