Abstract
Neural circuits are remarkable in their ability to perform a diversity of dynamic tasks such as sensory processing, information transfer and storage. Unraveling how information travels through a network of neural circuits over time, and how it is being processed, will enable classification of functionality and wiring and will synthesize future dynamics. Typically, such networks are extremely challenging to study using traditional approaches since on top of their complex structure they exhibit intricate time-dependent dynamics. My talk will focus on our methods that leverage sampled time-series data from a network. I will describe how to fuse dynamical system theory with data analysis (e.g. phase space analysis, model reduction, optimization and probabilistic graphical modeling) to achieve efficient classification, use it for recognition and solve inverse problems for recovery of network wiring. Furthermore, their combination enables predictive modeling of dynamic networks. I will describe the methodology and provide examples of real neurobiological systems for which the developed tools were applied. These include olfaction in moths, C. elegans worm nervous system and sun-compass navigation in Monarch butterflies.
Biography
Eli Shlizerman is an Assistant Professor in the Department of Electrical Engineering and Department of Applied Mathematics at the University of Washington and Washington Research Foundation Professor. He received his Ph.D. in applied mathematics and computer science from the Weizmann Institute of Science, then spent three years as a postdoctoral researcher at Princeton and UW Applied Math, and was promoted to Assistant Professor in the same department. Eli’s research focuses on classification and modeling of dynamics of complex systems, for which he develops methods that combine data analysis and dynamical systems theory for real data. He has collaborating with UW Biology, U-Mass Neurobiology and the Allen Institute for Brain Science. The particular complex systems that Eli is studying are neuronal networks and the methods he has been developing include tools for derivation of reduced models, inference of connectivity in networks, and classification and recognition of dynamics. In addition to the analysis of neuronal networks, the Eli’s computational approaches are also aimed at transforming the design of in-silico prototypes for these systems. Eli has received the Boeing Research award and a joint NSF-NIGMS initiative award at the interface of Mathematical and Biological science. His work on the olfactory system was recently published in Science magazine and covered by the NY Times, BBC and others.
