Towards Model-Based Observation and Control of Brain Networks

Since the 1950s, we have developed mature theories of modern control theory and computational neuroscience with little interaction between these disciplines. With the advent of computationally efficient nonlinear Kalman filtering techniques (developed in robotics and weather prediction), 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 dynamics, the modulation of oscillatory wave dynamics in brain cortex, a control framework for Parkinson’s disease dynamics and seizures, the ongoing work seeking to control the underlying physiology responsible for migraines (spreading depression), and the use of optimized parameter model networks to assimilate complex network data – the ‘consensus set’. The subtle and deep intersection of symmetry, in brains and models, is important to take into account. Lastly, the limitations of this approach are now being increasingly subject to accurate characterization. The transdisciplinary fusion of computational models of the computational brain, with real time sensing from the brain using control theory and engineering, opens new frontiers in our ability to observe and control dynamical disease states of the brain.