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Modifying the structure of man-made and natural networked systems has become increasingly feasible due to recent technological advances. This flexibility offers great opportunities to save resources and improve controllability and energy efficiency. In contrast (and dual) to the well-studied optimal actuator placement problem, this work focuses on improving network controllability by adding and/or re-weighting network edges while keeping the actuation structure fixed. First a novel energy-based edge centrality measure is proposed and then its relationship with the gradient (with respect to edge weights) of the trace of the controllability Gramian is rigorously characterized. Finally, a network modification algorithm based on the proposed measure is proposed and its efficacy in terms of computational complexity and controllability enhancement is numerically demonstrated. 

Oscillations in the brain are one of the most ubiquitous and robust patterns of activity and correlate with various cognitive phenomena. In this work, we study the existence and properties of oscillations in simple mean-field models of brain activity with bounded linear-threshold rate dynamics. First, we obtain exact conditions for the existence of limit cycles in two-dimensional excitatory-inhibitory networks (E-I pairs) and provide generalizations for networks with one inhibitory and multiple excitatory nodes. Building on these results, we study networks of multiple E-I pairs, provide exact conditions for the lack of stable equilibria, and numerically show that this is a tight proxy for the existence of oscillatory behavior. Finally, we study cross-frequency coupling between pairs of oscillators each consisting of an E-I pair. We find that while both phase-phase coupling (synchronization) and phase-amplitude coupling (PAC) monotonically increase with inter-oscillator connection strength, there exists a tradeoff in increasing frequency mismatch between the oscillators as it de-synchronizes them while enhancing their PAC. 

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.