Redwood Neuroscience
Title: “High-Performance
Neural Prosthetic Systems”
Department of Electrical
Engineering
Abstract:
The prospect of helping
disabled patients, by translating neural activity from the brain into control
signals for prosthetic devices, has flourished in recent years. Rapid progress
on such neural prosthetic systems has been possible because of systems neuroscience
discoveries and major advances in computational and neural-recording
technologies over the past decade. For example, several research groups have
now demonstrated that monkeys can learn to move a computer icon to various
target locations simply by activating neural populations that participate in
natural arm movements. To move beyond these proof-of-concept systems we must
increase system performance (move prosthetic devices more quickly and
accurately), thereby providing the patient with more useful movements. I will
describe our recent computational and experimental work aimed at increasing
neural prosthetic system performance: experiments to better understand what
neurons in pre-motor cortex are coding, new estimation algorithms for
translating neural activity into prosthetic control signals, and
experimental results from our neural prosthetic system
demonstrating high information-transmission rates.