Automatically inferring mesoscale models of neural computation - Tom Dean

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Thursday, October 13, 2016
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3:30pm to 4:15pm PDT
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Tom Dean, PhD

Research Scientist
Google Research

Automatically inferring meso-scale models of neural computation 

Abstract: We examine the idea of an intermediate or meso-scale computational theory that connects a molecular (micro-scale) account of neural function to a behavioral (macro-scale) account of animal cognition and environmental complexity. Just as digital accounts of computation in conventional computers abstract from the non-essential dynamics of the analog circuits implementing gates and registers, so too a computational account of animal cognition can afford to abstract from the non-essential dynamics of neurons. We argue that the geometry of neural circuits is essential in explaining the computational limitations and technological innovations inherent in biological information processing. Finally, we consider how we might employ tools from machine learning to automatically infer a satisfying meso-scale account of neural computation that combines functional and structural data in the form of neural recordings and morphological analyses with physiological and environmental data in terms of behavioral recordings.

Bio: Tom Dean is a research scientist at Google in Mountain View, California. From 1993 to 2007 he was Professor of Computer Science and Cognitive and Linguistic Sciences at Brown University. He received his B.A. in mathematics from Virginia Polytechnic Institute & State University in 1982 and his M.Sc. and Ph.D. in computer science from Yale University in 1984 and 1986 respectively. Dean was named a fellow of AAAI in 1994 and an ACM fellow in 2009. He served as the Deputy Provost of Brown University from 2003 to 2005, as the chair of Brown's Computer Science Department from 1997 until 2002, and as the Acting Vice President for Computing and Information Services from 2001 until 2002. He has published three books including a popular AI textbook and a graduate text in control theory, as well as over a hundred journal and conference papers in artificial intelligence, automated planning, computer vision, computational neuroscience, machine learning and robotics. Dean is a Consulting Faculty in Computer Science at Stanford University where he been teaching courses in computational neuroscience since 2007.