Mathematical Biology Seminar

Wei Wu
University of Chicago
Wednesday, Feb 8, 2006

"Bayesian Population Decoding of Motor Cortical Activity and its Applications in Neural Prostheses"

Effective neural motor prostheses require a method for decoding neural activity representing desired movement. In particular, the accurate reconstruction of a continuous motion signal is necessary for the control of devices such as computer cursors, robots, or a patient's own paralyzed limbs. In this talk, I will present our real-time system for such applications that uses statistical Bayesian inference techniques to estimate hand motion from the firing rates of multiple neurons in a monkey's primary motor cortex. The Bayesian model is formulated in terms of the product of a likelihood and a prior. The likelihood term models the probability of neural firing rates given a particular hand motion. The prior term defines a probabilistic model of hand kinematics. Decoding was performed using a Kalman filter as well as a more sophisticated Switching Kalman filter. Off-line reconstructions of hand trajectories were relatively accurate and an analysis of these results provides insights into the nature of neural coding. Furthermore, I will show on-line neural control results in which a monkey exploits the Kalman filter to move a computer cursor with its brain.