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Alexander G. Dimitrov and John P. Miller

Center for Computational Biology

Montana State University

We present a novel analytical approach for studying neural encoding. As a first step we model a neural sensory system as a communication channel. Using the method of typical sequence in this context, we show that a coding scheme is an almost bijective relation between equivalence classes of stimulus/response pairs. The analysis allows a quantitative determination of the type of information encoded in neural activity patterns and, at the same time, identifying the code with which that information is represented. Due to the high dimensionality of the sets involved, such a relation is extremely difficult to quantify. To circumvent this problem, and to use whatever limited data set is available most efficiently, we quantize the neural responses to a reproduction set of a small finite size. We optimize the quantization to minimize an information-based distortion function. This method allows us to study coarse but highly informative models of a coding scheme, and then to refine them automatically when more data becomes available.

We apply this method to the analysis of coding in the cricket cercal sensory system. To cope with the high dimensional stimulus space, we use model and minimize an upper bound of the information distortion. We compare the results to the linear stimulus reconstruction approach. For a single neuron, a reproduction with two classes completely recovers the stimulus reconstruction results. A 3-class reproduction uncovered additional structure not present in the stimulus reconstruction results. Further structure was found in the class-conditioned covariance matrix.