Future research direction based on this project is threefold. First, the algorithm could be generalized to any order Markov process, at the expense of requiring larger amount of data. Second, we would like to derive the theoretical error bound for the estimate in terms of basic information-theoretic quantities such as mutual information and entropy. Third, it may not be necessary to decode from spike trains, as there is some evidence, at least for certain neural systems, that there is a significant amount of information in local field potential (LFP). Throwing away the LFP might be a tremendous wastage of useful data. An important question is the level of redundancy between the LFP and the spikes, and the role of LFP in neural systems, which some researchers hypothesize lie in spike synchronization.

Here is a list of people involved in this project: A useful introductory material on the theory of point processes can be found here

To read about the basics of the new decoding algorithm click here