What features of the activity of individual neurons and neural populations are used to represent and process information? How are unambiguous percepts generated from individual neural spike trains and the activity of neural populations?
How are form, color, motion, and symmetry extracted from the visual image, and how do these submodalities interact?
What are realistic models for how visual information is transformed by neurons of the mammalian visual system?
How does disease of the nervous system affect these processes? What are the intrinsic dynamics of networks of neurons in normal brain and in epilepsy? What are the implications for novel diagnostics and therapeutics?
We are addressing these questions at multiple levels: behavior (via psychophysical studies in man), cell populations (via surface and depth evoked potentials), and at the cellular level (via single-unit recording). These experimental studies are accompanied by theoretical investigations. What are appropriate mathematical models for complex biological systems, and how can they be tested empirically? What are the general rules for how the properties of a large, complex system derive from the properties of its constituents and their connections?
We are also interested in theoretical and computational issues related to the application of ideas from information theory to neuroscience, and especially, novel methods for the estimation of entropy and mutual information from neurophysiologic recordings of single- and multi-unit spike trains.
Ongoing collaboratorive work includes studies of neural coding in the gustatory system (with Patricia Di Lorenzo), the creation of an open resource to facilitate application of information-theoretic analyses to neural data (with David Goldberg and Dan Gardner at the Laboratory of Neuroinformatics), and the creation of an integrated software suite for exploratory and spectral data analysis (with Partha Mitra ).