Graduate Training in Neuroscience
University of Washington
Associate Professor, Department of Physiology and Biophysics
I am interested in the computational principles underlying information processing in the nervous system. One can think about spiking neurons as computational elements that perform a transformation on their inputs to produce a discrete spiking output. I am interested in general methods for building reduced models for this neural computation in terms of extracting relevant features from a complex input. The methods I am exploring start both from experimental data and from biophysical descriptions of neural dynamics. We aim to relate the functional models determined from spiking data directly with the underlying channel dynamics. The brain is a highly adaptive organism, assimilating and adjusting to changes in the environment on a multiplicity of temporal and spatial scales. Thus, the neural code of several systems has been shown to be adaptive with respect to changes in the statistical distribution of the inputs. In my work I am seeking to eludicate how such adaptation may be beneficial for neural information processing, and to explore potential mechanisms underlying adaptation to statistics at the level of single neuron computation.