Controlling Populations of Neural Oscillators
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Speaker
Abstract
Some brain disorders are hypothesized to have a dynamical origin; in particular, it has been been hypothesized that some symptoms of Parkinson's disease are due to pathologically synchronized neural activity in the motor control region of the brain. This talk will describe several different approaches for desynchronizing the activity of a group of neurons, including maximizing the Lyapunov exponent associated with their phase dynamics, optimal phase resetting, controlling the phase density, and controlling the population to have clustered dynamics. It is hoped that this work will ultimately lead to improved treatment of Parkinson's disease via targeted electrical stimulation.
Bio: Jeff Moehlis received a Ph.D. in Physics from UC Berkeley in 2000, and was a Postdoctoral Researcher in the Program in Applied and Computational Mathematics at Princeton University from 2000-2003. He joined the Department of Mechanical Engineering at the University of California, Santa Barbara in 2003. He has been a recipient of a Sloan Research Fellowship in Mathematics and a National Science Foundation CAREER Award, and was Program Director of the SIAM Activity Group in Dynamical Systems from 2008-2009. He received a Northrop Grumman Excellence in Teaching Award from UCSB in 2008. Jeff's current research includes the application of dynamical systems and control techniques to neuroscience, cardiac dynamics, and collective behavior.