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