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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.


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.


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