Center for Computational Biology, Montana State University
Natural time scale for neural encoding
The nature and information content of neural signals has been discussed extensively in the neuroscience community. However, there is still some arbitrariness in the choice of temporal scale, with which to describe the neural coding. We propose a natural time scale, which emerges through asymptotic limits of the mutual information between stimuli and corresponding spike trains. Using an analytical approach based on this time scale, we characterize the maximal information content of a spike train, independent of its temporal discretization.