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

This paper develops a control-theoretic approach to the problem of decoding neural activity in motor cortex. Our goal is to infer the position and velocity of a subject's hand from the neural spiking 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 measurement 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 reconstructed 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 measurement model suggests that, while the neural ring is closely related to the position and velocity of the hand, the acceleration is redundant. Furthermore, the Kalman lter framework is exploited to recover the optimal lag time between hand movement and neural ring.