Modeling dynamics of local information processing in human brain
by
Amir Assadi
Professor of Mathematics, University of Wisconsin

Brain is the most thoroughly studied complex dynamical system. "Information processing" compares the brain with an input-output computing device; biologically, it means neuronal response to stimuli/behavior. Realistic models of brain function require extraction of statistically robust patterns from data measuring ion currents in brain tissue during processes like hearing a tone, or sensation of pain. The multitude of neurons and other cells form the tissue that accommodates local neuronal circuits, while flows of ion currents allow local and global communication in a vast grid of local information processing units. The key theoretical assumption in this research is that the mathematical constructs that measure the information contents of brain signals due to evoked potential are indeed quantitative representation of biological information processing in local circuits. This assumption has been supported by a host of experiments in alternative formulations. We present a mathematical framework to classify patterns of dynamics in local circuits of the cortical tissue of the animal and the human brain. The main contribution of this research is to introduce a new mathematical approach to classify patterns of dynamics in information-theoretic constructions from brain data.

Date received: October 7, 2003

Copyright © 2003 by the author(s). The author(s) of this document and the organizers of the conference have granted their consent to include this abstract in Atlas Conferences Inc. Document # calu-14.