Controlling Populations of Neural Oscillators

Many challenging problems that consider the analysis and control of neural brain rhythms have been motivated by the advent of deep brain stimulation as a therapeutic treatment for a wide variety of neurological disorders. In a computational setting, neural rhythms are often modeled using large populations of coupled, conductance-based neurons. Control of such models comes with a long list of challenges: the underlying dynamics are nonnegligibly nonlinear, high dimensional, and subject to noise; hardware and biological limitations place restrictive constraints on allowable inputs; direct measurement of system observables is generally limited; and the resulting systems are typically highly underactuated. In this talk, I highlight a collection of recent analysis techniques and control frameworks that have been developed to contend with these difficulties. Particular emphasis is placed on the problem of desynchronization for a population of pathologically synchronized neural oscillators, a problem that is motivated by applications to Parkinson’s disease where pathological synchronization is thought to contribute to the associated motor control symptoms. This talk is based on the article “Recent advances in the analysis and control of large populations of neural oscillators” by D. Wilson and J. Moehlis, in Annual Reviews in Control, 54:327-351, 2022, and will also discuss recent efforts to use machine learning to control neural populations.