Wednesday March 18, 3:30pm — PAA 110
Dynamic Data meets Neuronal Networks
Assistant Professor, University of Washington
Neuronal networks 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 these networks over time, and how it is being processed, will enable classification of functionality, network wiring, and 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 recently developed 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.
Eli Shlizerman is an Assistant Professor in the Department of Applied Mathematics at the University of Washington. He received his PhD degree in applied mathematics and computer science from the Weizmann Institute of Science, then spent three years as a postdoctoral researcher at UW Applied Math, and promoted to Assistant Professor in the same department. Eli's research focuses on classification and modeling of dynamics of complex systems. For this purpose he develops methods that combine data analysis and dynamical systems theory for real data and thereby collaborating with UW Biology, U-Mass Neurobiology and Allen Institute for Brain Science. Complex systems that are being studied are neuronal networks and among the methods are tools for derivation of reduced models, inference of connectivity in networks, classification and recognition of dynamics. Eli received the Boeing Research award, 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, etc.