Towards model-based control of Parkinson's disease: A perspective

Since the 1950s, we have developed mature theories of modern control theory and computational neuroscience with almost no interaction between these disciplines. With the advent of computationally efficient nonlinear Kalman filtering techniques, along with improved neuroscience models that provide increasingly accurate reconstruction of dynamics in a variety of important normal and disease states in the brain, the prospects for a synergistic interaction between these fields are now strong. I show recent examples of the use of nonlinear control theory for the assimilation and control of single neuron and network dynamics, as well as the modulation of oscillatory waves in the cortex, and the assimilation of epileptic seizures. A control framework for modulating Parkinsonian dynamics is presented, and a perspective offered. As the computational models of dynamical diseases such as Parkinson's disease improve, embedding those models within rigorous model-based control frameworks is now feasible.