Analyzing sensory systems with the information distortion function

The nature and information content of neural signals have been discussed extensively in the neuroscience community. They are important ingredients in many theories on neural function, yet there is still no agreement on the details of neural coding. There have been various suggestions about how information is encoded in neural spike trains: by the number of spikes, by temporal correlations, through single spikes, or by spike patterns in one, or across many neurons. The latter scheme is most general and encompasses many others. We present an algorithm which can recover a coarse representation of a pattern coding scheme, through quantization to a reproduction set of smaller size. Among many possible quantizations, we choose one which preserves as much of the informativeness of the original stimulus/response relation as possible, through the use of 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 when more data becomes available. We shall describe a model in which full recovery is possible and present example for cases with partial recovery.