ANALYZING NEURAL CODING: JOINTLY TYPICAL SEQUENCES AND QUANTIZATION

Alexander G. Dimitrov and John P. Miller

Center for Computational Biology

Montana State University

The nature and information content of neural signals have been discussed extensively in the neuroscience community. They are an important ingredient of 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 shall describe our progress in modeling it through jointly typical sequences. The search for pattern codes requires exponentially more data than the search for mean rate or correlation codes. We will also describe a method that enables optimal use of limited quantities of data, through quantization to a reproduction set of small finite size. To asses the quality of the quantization we use an information-based distortion measure. The quantization is optimized to have minimal distortion for a fixed size of the reproduction. This method allows us to study coarse models of coding schemes which can be refined as more data becomes available.