Inferring Structure in Large Neural Systems

11:45am lunch 12noon lectureAbstract:Characterizing the structure of neural circuits is a fundamental problem in brain science. Recent technological advances offer unprecedented possibilities for observing and manipulating neural activity. However, unraveling the connectivity and interactions among populations of neurons remains a daunting challenge. In this talk, I present a computationally scalable approach for estimation of large dynamical structured nonlinear systems. This is an instance of a general framework that offers provable guarantees in consistency and convergence and is applicable in a wide variety of settings. For estimation of large neuronal networks from calcium imaging data, this work improves on current methods in both computational cost and accuracy.Biography:Alyson "Allie" Fletcher received her MS in mathematics, and her MS and doctorate in electrical engineering, all from the University of California, Berkeley, where she received the Eugene L. Lawler Award. Her other awards include the NSF CAREER, Clare Luce Boothe Fellowship, and UC President's Postdoctoral Fellowship. She is currently an Assistant Professor of Electrical Engineering at UC Santa Cruz. Her research interests include high dimensional inference, statistical signal processing, machine learning, and computational neuroscience.