CDS Invited Lecture: Steve Marcus, "An Optimization-based Approach to Breaking the Neural Code"
Friday, February 19, 2021
Control and Dynamical Systems Invited Lecture
An Optimization-based Approach to Breaking the Neural Code
Professor Steve Marcus
Department of Electrical and Computer Engineering
Institute for Systems Research
University of Maryland
We discuss some recent results that have been obtained in a collaborative project that has developed optimization techniques to address problems in auditory neuroscience. Recent technological advances in neural data acquisition, in particular magnetoencephalography (MEG), have resulted in an abundance of large neural data sets, spanning different spatial scales, time scales, conditions, and recording methodologies. The high temporal resolution and noninvasive nature of MEG neural recordings in human subjects opens a unique window of opportunity to decipher the complex dynamic cortical interactions underlying sophisticated behavior, and thereby to break the so-called `neural code'. In order to exploit the potential of these data sets, we have worked to develop computationally efficient and robust optimization techniques capable of capturing the dynamics and statistical characteristics of the recorded brain activity. These techniques aim to address shortcomings of existing methods, including scalability of neural system identification, real-time processing of the neural data, dimensionality reduction techniques that capture the geometric/topological organization upon which cortical function is based, and the nonlinearity and non-Gaussian nature of cortical activity. We present results of these algorithms applied to real MEG data acquired in our auditory experiments.
This lecture is based on joint work with Behtash Babadi (ECE/ISR), Michael Fu (BMGT/ISR), and Jonathan Simon (ECE/Biology/ISR).
Steve Marcus received the B.A. degree from Rice University in 1971 and the S.M. and Ph.D. degrees from M.I.T. in 1972 and 1975, respectively. From 1975 to 1991, he was with the Department of Electrical and Computer Engineering at the University of Texas at Austin. In 1991, he joined the University of Maryland, College Park, as Professor in the Electrical and Computer Engineering Department and the Institute for Systems Research. He was Director of the Institute for Systems Research from 1991 to 1996 and Chair of the Electrical and Computer Engineering Department from 2000 to 2005. He has also served as Associate Provost for Faculty Affairs. Currently, his research is focused on stochastic control, estimation, cyber-physical systems, and optimization. He is a Fellow of the Society for Industrial and Applied Mathematics and the Institute of Electrical and Electronics Engineers.