Behtash Babadi presents Dynamic Network-level Analysis of Neural Data Underlying Behavior: Beyond the Linear, Static, and Gaussian Domains
Abstract: In this talk, I will present computational methodologies for extracting dynamic neural functional networks that underlie behavior. These methods aim at capturing the sparsity, dynamicity and stochasticity of these networks, by integrating techniques from high-dimensional statistics, point processes, state-space modeling, and adaptive filtering. I demonstrate their utility using several case studies involving auditory processing, including 1) functional auditory-prefrontal interactions during attentive behavior in the ferret brain, 2) network-level signatures of decision-making in the mouse primary auditory cortex, and 3) cortical dynamics of speech processing in the human brain.
Bio: Behtash Babadi is an Assistant Professor in the Department of Electrical & Computer Engineering and the Institute for Systems Research at the University of Maryland, College Park. He received the Ph.D. and M.Sc. degrees in Engineering Sciences from Harvard University in 2011 and 2008, respectively, and the B.Sc. degree in Electrical Engineering from Sharif University of Technology, Tehran, Iran in 2006. From 2011 to 2014, he was a postdoctoral fellow at the Department of Brain and Cognitive Sciences at the Massachusetts Institute of Technology as well as at the Department of Anesthesia, Critical Care and Pain Medicine at Massachusetts General Hospital. He received an NSF CAREER Award in 2016.
Wednesday, March 4 at 12:00pm to 1:00pm
Wegmans Hall, 1400
250 Hutchison Rd, Rochester, NY 14620