Networked Dynamical Systems for Function and Learning: Paradigms for Data-Driven Control and Learning in Neurosensory Systems
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Abstract
High-dimensional networked biological systems are ubiquitous and characterized by a large connectivity graph whose structure determines how the system operates as a whole. Typically the connectivity is so complex (and unknown as well) that the functionality, control and robustness of the network of interest is impossible to characterize using currently available methods. A full understanding of this computational process encoded throughout a nervous system that transforms sensory input into motor representations requires the ability to generate proxy models for the activity of sensory neurons, decision-making circuits, and motor circuits in a behaving animal. Our objective is to use emerging data-driven methods to extract the underlying engineering principles of cognitive capability, namely those that allow complex networks to learn and enact control and functionality in the robust manner observed in neurosensory systems. Mathematically, the challenges center around understanding how networked dynamical systems produce robust functionality and coordinated activity.
Bio
Professor Kutz was awarded the B.S. in Physics and Mathematics from the University of Washington in 1990 and the PhD in Applied Mathematics from Northwestern University in 1994. Following postdoctoral fellowships at the Institute for Mathematics and its Applications (University of Minnesota, 1994-1995) and Princeton University (1995-1997), he joined the faculty of applied mathematics and served as Chair from 2007-2015.
He is the author of the book Data-Driven Modeling & Scientific Computation: Methods for Complex Systems & Big Data (Oxford Univ. Press, 2013). He also delivers on a regular basis Scientific Computing and Computational Methods for Data Analysis as Massive Open Online Courses (MOOCs) in the Coursera platform.
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