Jorge Cortés
Professor
Cymer Corporation Endowed Chair
Control of linear-threshold brain networks via reservoir computing
M. McCreesh,
J. Cortés
IEEE Open Journal of Control Systems 3 (2024), 325-341
Abstract
Learning is a key function in the brain to be
able to achieve the activity patterns required
to perform various activities. While specific
behaviors are determined by activity in
localized regions, the interconnections
throughout the entire brain play a key role in
enabling its ability to exhibit desired
activity. To mimic this setup, this paper
examines the use of reservoir computing to
control a linear-threshold network brain
model to a desired trajectory. We first
formally design open- and closed-loop
controllers that achieve reference tracking
under suitable conditions on the synaptic
connectivity. Given the impracticality of
evaluating closed-form control signals,
particularly with growing network complexity,
we provide a framework where a reservoir of a
larger size than the network is trained to
drive the activity to the desired pattern. We
illustrate the versatility of this setup in
two applications: selective recruitment and
inhibition of neuronal populations for
goal-driven selective attention, and network
intervention for the prevention of epileptic
seizures.
pdf
Mechanical and Aerospace Engineering,
University of California, San Diego
9500 Gilman Dr,
La Jolla, California, 92093-0411
Ph: 1-858-822-7930
Fax: 1-858-822-3107
cortes at ucsd.edu
Skype id:
jorgilliyo