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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.
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Selective recruitment in hierarchical complex dynamical networks with linear-threshold rate dynamics
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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- Award ID(s):
- 1826065
- PAR ID:
- 10120566
- Date Published:
- Journal Name:
- Proceedings of the IEEE Conference on Decision and Control
- Page Range / eLocation ID:
- 5227-5232
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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- The DOI is not currently available.
