Tuesday, 23 March 2004, 2pm:

<CENTER><B>Statistical Models of Neural Coding in Motor Cortex and Their Applications
in Neural Prostheses </B>

<P>
Wei Wu <BR>
Brown University
</CENTER>

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 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.