Jorge Cortés

Professor





Selective recruitment in hierarchical complex dynamical networks with linear-threshold rate dynamics
E. Nozari, J. Cortés
Proceedings of the IEEE Conference on Decision and Control, Miami Beach, Florida, 2018, to appear
2018 CDC Best Student Paper Award Finalist


Abstract

Understanding how the complex network dynamics of the brain support cognition constitutes one of the most challenging and impactful problems ahead of systems and control theory. In this paper, we study the problem of selective recruitment, namely, the simultaneous selective inhibition of activity in one subnetwork and top-down recruitment of another by a cognitively-higher level subnetwork, using the class of linear-threshold rate (LTR) models. We first use singular perturbation theory to provide a theoretical framework for selective recruitment in a bilayer hierarchical LTR network using both feedback and feedforward control. We then generalize this framework to arbitrary number of layers and provide conditions on the joint structure of subnetworks that guarantee simultaneous selective inhibition and top-down recruitment at all layers. We finally illustrate an application of this framework in a realistic scenario where simultaneous stabilization and control of a lower level excitatory subnetwork is achieved through proper oscillatory activity in a higher level inhibitory subnetwork.



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Mechanical and Aerospace Engineering, University of California, San Diego
9500 Gilman Dr, La Jolla, California, 92093-0411

Ph: 1-858-822-7930
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cortes at ucsd.edu
Skype id: jorgilliyo