Events for January 21, 2015
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Communications, Networks & Systems (CommNetS) Seminar
Wed, Jan 21, 2015 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Joel Zylberberg, University of Washington
Talk Title: Signal and Noise in the Nervous System
Series: CommNetS
Abstract: The nervous system is a surprisingly noisy place. For example, if one presents the exact same stimulus to an animal many times, and records the activities of their sensory neurons, the responses of those neurons show high levels of trial-to-trial variability. Similar levels of variability are observed elsewhere in the nervous system. At the same time, we have the experience of having robust thoughts and perceptions. So how do our brains generate this robustness from systems of inherently unreliable components? In my talk, I will discuss my work on the retina, the visual cortex, and the hippocampus, each of which reveals strategies that the nervous system appears to use in solving this problem. Along the way, I'll highlight the implications of these results for other neuronal systems, and for the creation of biomimetic technologies. Importantly, I will assume no specialized knowledge on the part of the listener.
Biography: During my undergraduate studies in Physics at Simon Fraser University (Canada), I published papers in inorganic chemistry, nuclear physics, and physics education, before receiving the B.Sc. degree in 2008. Supported by a Fulbright Science and Technology PhD fellowship, I then moved to UC Berkeley to pursue my PhD in Physics. I spent the first 2 years of my graduate training studying cosmology, before transitioning into neuroscience. My early work in neuroscience won me a student research fellowship from the Howard Hughes Medical Institute, which supported my final (4th) year of doctoral studies. I received my PhD from UC Berkeley in 2012, and then took up my current position as Acting Assistant Professor at the University of Washington. In my research, I combine tools from information theory, physics, and computer science, to reveal the circuitry underlying the robust perception and memory functions of the nervous system.
Host: Dr. Paul Bogdan and the Ming Hsieh Institute
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Annie Yu
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Communications, Networks & Systems (CommNetS) Seminar
Wed, Jan 21, 2015 @ 03:30 PM - 04:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Richard J. La, University of Maryland
Talk Title: Convergence of a class of simple learning rules to pure-strategy Nash equilibria
Series: CommNetS
Abstract: Recently, there has been a growing interest in applying a game theoretic framework to various distributed engineering systems, including communication networks and distributed control systems.
Oftentimes, Nash equilibria are taken as an approximation to the expected operating point of these systems. In this talk, we examine the convergence of a class of simple learning rules to pure-strategy Nash equilibria (PSNEs). First, we demonstrate that if all agents adopt a learning rule from this class, when there exists at least one PSNE, they converge to a PSNE almost surely even in the presence of heterogeneous or time-varying feedback or observation delays under mild conditions on the games, which we call generalized weakly acyclic games (GWAGs). Second, we show that GWAGs are the only games for which the learning rules are guaranteed to converge to a PSNE. In other words, for a non-GWAG, there is an initial condition, starting with which the learning rules do not converge to a PSNE. Finally, we consider the case where the agents do not correctly determine their payoffs and make errors in their decisions. We illustrate that, if the probability of making a mistake diminishes to zero arbitrarily slow, the probability that the strategy profile of the agents belongs to the set of PSNEs tends to one over time.
This is a joint work with Siddharth Pal.
Biography: Richard J. La received his B.S.E.E. from the University of Maryland, College Park in 1994 and M.S. and Ph.D. degrees in Electrical Engineering from the University of California, Berkeley in 1997 and 2000, respectively. From 2000 to 2001 he was with the Mathematics of Communication Networks group at Motorola Inc,. Since 2001 he has been on the faculty of the Department of Electrical and Computer Engineering at the University of Maryland, where he is currently an Associate Professor.
Host: Prof. Rahul Jain and the Ming Hsieh Institute
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Annie Yu
