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Colloquia

Data Mining Methods for Neuroinformatics

Dr. K. P. Unnikrishnan
General Motors R&D Center

October 16, 2008
JEC 3117, 4:00 p.m. to 5:00 p.m.
Refreshments at 3:30 p.m.

Abstract:


We describe methods to discover structural properties of complex, dynamical networks from observed data streams. By discovering patterns in multi-neuronal spike trains, we are able to uncover the functional connectivity (graphical structure) of the underlying neuronal networks and observe their time-evolutions. We illustrate the usefulness of these methods on simulated and real datasets and compare their performance with model-based estimation approaches. We conclude with a brief discussion of Neural Codes and how Data Mining can help discover them.

Bio:

Dr. Unnikrishnan received the PhD degree in Physics (biophysics) from Syracuse University, Syracuse, New York, in 1987. He is currently a staff research scientist at the General Motors R&D Center, Warren, Michigan. Before joining GM, he was a postdoctoral member of the technical staff at AT&T Bell Laboratories, Murray Hill, New Jersey. He has also been an adjunct assistant professor at the University of Michigan, Ann Arbor, a visiting associate at the California Institute of Technology (Caltech), Pasadena, and a visiting scientist at the Indian Institute of Science, Bangalore. His research interests concern neural computation in sensory systems, correlation-based algorithms for learning and adaptation, dynamical neural networks, and temporal data mining.

Hosted by: Dr. Mohammed J. Zaki (x6340)

Last updated: September 11, 2008



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