Steven Schiff

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  Model-based Observation and Control for the Brain: From Control of Seizures and Migraines, to Reducing Infant Brain Infections in Africa
  Steven Schiff

  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 normal and disease states in the brain, the prospects for synergistic interaction between these fields are now strong. I will show recent examples of the use of nonlinear control theory for the assimilation and control of single neuron and network dynamics, a control framework for Parkinson’s disease, and the potential for unification in control of spreading depression and seizures. Recent results help explain why the subtle and deep intersection of symmetry, in brains and models, is important to take into account in this transdisciplinary fusion of computational models of the computational brain with real-time control. Lastly, I will describe how such symmetries apply to network optimization and control for the prevention of infant brain infections in Africa.