Jorge Cortés
Professor
Cymer Corporation Endowed Chair
Data-driven reconstruction of firing rate dynamics in brain networks
X. Wang, J. Cortés
Proceedings of the IEEE Conference on Decision and Control, Austin, Texas, 2021, pp. 6456-6461
Abstract
This paper studies the reconstruction from data of
firing rate dynamics in linear-threshold network models
of brain activity. Instead of identifying the system
paramaters directly, which would lead to a large number
of variables and a highly non-convex objective function,
the novelty of our approach stems from reformulating the
identification problem as a scalar variable optimization
of a discontinuous, nonconvex objective function. We
formally show that the reformulated optimization problem
has a unique solution and establish that it leads to the
identification of all the desired system
parameters. These results form the basis for the
introduction of an algorithm to find the optimizer that
identifies the different regions in the domain of
definition of the objective function. The results not
only validate the system identifiability but also
provide the foundation for further research on
data-driven control of firing rate dynamics. We
demonstrate the algorithm effectiveness in simulation.
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