Neural Coding and Decoding

Methods based on Rate Distortion theory have been successfully used to cluster stimuli and neural responses in order to study neural codes at a level of detail supported by the amount of available data. They approximate the joint stimulus-response distribution by quantizing paired stimulus-response observations into smaller reproductions of the stimulus and response spaces. An optimal quantization is found by maximizing an information-theoretic cost function subject to both equality and inequality constraints, in hundreds to thousands of dimensions. This analytical approach has several advantages over other current approaches: it yields the most informative approximation of the encoding scheme given the available data (i.e., it gives the lowest distortion, by preserving the most mutual information between stimulus and response classes), the cost function, which is intrinsic to the problem, does not introduce implicit assumptions about the nature or linearity of the encoding scheme, the maximum entropy quantizer does not introduce additional implicit constraints to the problem, it incorporates an objective, quantitative scheme for refining the codebook as more stimulus/response data becomes available, it does not need repetitions of the stimulus under mild continuity assumptions, so the stimulus space may be investigated more thoroughly. Here the method is applied to the study of neural sensory representation. The application of this approach to the analysis of biological sensory coding involved a further restriction of the space of allowed quantizers to a smaller family of parametric distributions. We show that, for some cells in this system, a significant amount of information is encoded in patterns of spikes that would not be discovered through analyses based on linear stimulus-response measures.https://pdxscholar.library.pdx.edu/systems_science_seminar_series/1022/thumbnail.jp