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.
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Mathematical Biology Program
Department of Mathematics
University of Utah
155 South 1400 East Room 233
Salt Lake City, UT 84112
rasmusse@math.utah.edu
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