Computing with Neural Spikes

Post date: Sep 26, 2012 3:07:23 PM

Jennie Si, Professor, School of Electrical, Computer and Energy Engineering, Arizona State University

How interacting neurons give rise to meaningful behavior is an ultimate challenge to neuroscientists. To make the problem tractable, in my lab, a rat model is used to elucidate how cortical neural activities lead to conscious, goal-directed movement and control. However we allow rats to freely move about in the experimental apparatus so to capture their natural movement and mental conditions.This talk discusses findings based on single unit, multi-channel simultaneous recordings from rat’s frontal areas while they learned to perform a decision and control task. By exploring the neural activities from the rat’s cortical regions, we developed and utilized analytical techniques to uncover the interactions between neurons at different time scales. The findings provide interesting neural substrate to rat’s learning control behavior. The work involves both experimental and computational studies. Our results entail both high level statistical snapshots of the neural data and more detailed dynamic modeling with functional synaptic efficacies to capture before and after learning neural characteristics and their relationships to behavior. While performing the analyses, we aimed at providing mechanistic account of how brains generate meaningful behaviors under our designed experimental condition using biologically plausible computational models. How ideas from computational intelligence can be applied to the discovery of such neural code will be discussed as well.

Bio

Jennie Si received her B.S. and M.S. degrees from Tsinghua University, Beijing, China, and her Ph.D. from the University of Notre Dame. She has been on the faculty in the Department of Electrical Engineering at Arizona State University since 1991. Dr. Si's research focuses on dynamic optimization using learning and neural network approximation approaches, namely approximate dynamic programming. In 2006, she started building a neuroscience lab to study the neural mechanism of adaptation and control using multichannel single unit recordings from rat’s motor cortical areas. She received the NSF/White House Presidential Faculty Fellow Award in 1995, and Motorola Engineering Excellence Award the same year. She is a Fellow of the IEEE, and a distinguished lecturer for the IEEE Computational Intelligence Society. She is past Associate Editor of theIEEE Trans. on Semiconductor Manufacturing; IEEE Trans. on Automatic Control, andIEEE Trans. on Neural Networks. She is current Action Editor of Neural Networks. Dr. Si has served on several professional organizations' executive boards and international conference committees. She is the Vice President for Education in the IEEE Computation Intelligence Society (2009-2012). Dr. Si was an advisor to the NSF Social Behavioral and Economical directory, and served on several proposal review panels. She consulted for Intel, Arizona Public Service, and Medtronic.