Inferring Hand Motion from Multi-Cell Recordings in Motor Cortex using a Kalman Filter

Abstract

This paper develops a control-theoretic approach to the problem of decoding neural activity in mo-tor cortex. Our goal is to infer the position and velocity of a subject's hand from the neural spik-ing activity of 25 cells simultaneously recorded in primary motor cortex. We propose to model the encoding and decoding of the neural data using a Kalman lter. Towards that end we specify a mea-surement model that assumes the ring rate of a cell within 50ms is a stochastic linear function of position, velocity, and acceleration of the hand. This model is learned from training data along with a system model that encodes how the hand moves. Experimental results show that the recon-structed trajectories are superior to those obtained by linear ltering. Additionally, the Kalman lter provides insight into the neural encoding of hand motion. For example, analysis of the measure-ment model suggests that, while the neural ring is closely related to the position and velocity of the hand, the acceleration is redundant. Further-more, the Kalman lter framework is exploited to recover the optimal lag time between hand move-ment and neural ring. 1.