Control of Linear-Threshold Brain Networks via Reservoir Computing

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.