When: November 5, 2015, 12:30 PM
Location: 3rd Floor Orchard View Room , Discovery Building
Contact: 608-316-4401, firstname.lastname@example.org
The big (brain) data cometh: Low-dimensional models for understanding neural systems
The recent investment in neurotechnology development has spurred tremendous excitement about the potential to uncover the operating principles of biological neural circuits. However, a storm is brewing. If the neuroengineering community is able to achieve their goals of developing technologies that increase the number of interfaced neurons by orders of magnitude, what comes next? How do we acquire, transmit and store this data in an extremely constrained hardware environment? What are the theoretical models of neural coding that should be tested and refined? What experimental paradigms are most valuable for increasing our understanding of neural circuits? Modern data science has shown that low-dimensional models (e.g., sparsity, manifolds, attractors) have been a powerful way to approximately capture the information in high-dimensional data.
Given the power of these approaches, it is likely that they can contribute both to designing efficient engineering tools for the electrophysiology data pipeline as well as modeling the sensory neural systems that process information about environmental stimuli. In this talk I will discuss our recent progress on these problems, including new algoritms and analysis for dimensionality reduction and inference in sparsity, manifold and dynamical system models. We will show that these results can provide 1) powerful algorithms to aid large-scale electrophysiology data acquisition, 2) models of neural coding and perception in the visual pathway, and 3) novel experimental paradigms that leverage new neurotechnologies to uncover the fundamental operating principles of neural systems.
Christopher J. Rozell received a B.S.E. degree in Computer Engineering and a B.F.A. degree in Music (Performing Arts Technology) in 2000 from the University of Michigan. He attended graduate school at Rice University, receiving the M.S. and Ph.D. degrees in Electrical Engineering in 2002 and 2007, respectively. Following graduate school he joined the Redwood Center for Theoretical Neuroscience at the University of California, Berkeley as a postdoctoral scholar. In 2008 Dr. Rozell joined the faculty at the Georgia Institute of Technology where he is currently an Associate Professor in Electrical and Computer Engineering.
His research interests live at the intersection of machine learning, signal processing, complex systems and computational neuroscience. His research lab is affiliated with both the Center for Signal and Information Processing as well as the Laboratory for Neuroengineering at Georgia Tech, where he previously held the Demetrius T. Paris Junior Professorship. In 2014, Dr. Rozell was one of six international recipients of the Scholar Award in Studying Complex Systems from the James S. McDonnell Foundation 21st Century Science Initiative, as well as receiving a National Science Foundation CAREER Award and a Sigma Xi Young Faculty Research Award. In addition to his research activity, Dr. Rozell was awarded the CETL/BP Junior Faculty Teaching Excellence Award at Georgia Tech in 2013.
SILO is a lecture series with speakers from the UW faculty, graduate students or invited researchers that discuss mathematical related topics. The seminars are organized by WID’s Optimization research group.
SILO’s purpose is to provide a forum that helps connect and recruit mathematically-minded graduate students. SILO is a lunch-and-listen format, where speakers present interesting math topics while the audience eats lunch.