AI Seminar

Data Mining Methods for Graph Discovery in Neuroinformatics

K. P. Unnikrishnan

Staff Research Scientist
General Motors Research
Tuesday, March 10, 2009
4:00pm - 5:30pm
Stained-Glass Conference Room (3725 Beyster Bldg.)

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About the Event

Neuroscientists are beginning to collect activation (spike-train) data from hundreds of neurons with millisecond precision. Analysis of these large data sets poses interesting data mining challenges. We describe computational methods and associated significance tests to discover sequential patterns in multi-neuronal spike trains. By discovering these patterns, we are able to uncover the functional connectivity (graphical structure) of the underlying neuronal networks and observe their time-evolutions. We illustrate these on simulated and real data sets and compare the data mining methods with model-based estimation methods. We conclude with a brief discussion of Hebb cell assemblies and neural codes and how data mining can help discover them.


Unnikrishnan is a staff research scientist at the General Motors R&D Center, Warren, Michigan. His research interests concern neural computation in sensory systems, correlation-based algorithms for learning and adaptation, dynamical neural networks, and temporal data mining. 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. He received the PhD degree in Physics (Biophysics) from Syracuse University, Syracuse, New York, in 1987.

Additional Information

Contact: Jonathan Sorg

Email: jdsorg@umich.edu

Sponsor(s): AI Seminar Series

Open to: Public