Scalable Approaches to New Large-Scale Neuroscience
This meeting is hosted/sponsored by IEEE SPS Chapter and co-sponsored by IEEE WIE
Dr. Alyson "Allie" Fletcher
Assistant Professor of Electrical Engineering at UC Santa Cruz
AMD Commons C-6/7/8, 991 Stewart Dr., Sunnyvale, CA (map or Google Maps)
6:30pm: Networking/Light Dinner
Free. Donation accepted for food.
Recent technological advances offer unprecedented possibilities for observing and manipulating neural activity. Characterizing the structure of neural circuits is a fundamental problem in brain science, however, unraveling the connectivity and interactions among populations of neurons remains a daunting challenge. In this talk, I address two problems: learning the neural response in retinal ganglion cells, and the estimation of large neuronal networks from calcium imaging data. Utilizing a general computationally scalable framework for estimation of large dynamical structured nonlinear systems, the approach significantly improves over existing methods in both computational cost and performance. This methodology offers provable guarantees in consistency and convergence and is applicable across a wide variety of settings.
Alyson "Allie" Fletcher received her graduate degrees in mathematics and electrical engineering from the University of California, Berkeley, where she received the Eugene L. Lawler Award. Her other awards include the NSF CAREER, the Clare Luce Boothe Fellowship, and a UC President's Postdoctoral Fellowship. She is currently an Assistant Professor of Electrical Engineering at UC Santa Cruz, and she will move to UCLA in January 2016. Her research interests include high dimensional inference, statistical signal processing, graphical models, machine learning, and computational neuroscience.