Alexander Dimitrov

Center for Computational Biology
Montana State University

One of the steps toward understanding the neural basis of an animal's
behavior is characterizing the code with which its nervous system
represents information. We use tools from information theory
to achieve two goals towards characterizing the neural coding
scheme of a simple sensory system.  First, we represent the
functioning of a sensory system as a communication channel. We
demonstrate that in this context a coding scheme is an almost
deterministic relation between clusters of stimulus/response pairs.
Next, we develop a method to find high quality approximations of
such a coding scheme. To do this, we quantize the neural responses to
a small reproduction set and minimize an information-based distortion
function to optimize the quantization.

To use the method in cases, which involve complex, high dimensional
input stimuli, we model the stimulus/response relation in a way that
produces an upper bound to the information distortion, used in the
optimization problem. We use it to investigate coding properties of
several identified neurons in the cricket cercal sensory system.