Point process space and spike train space

In ITL (information theoretic learning; @CNEL), instead of using a kernel between two data samples, an expectation of product of probability estimate is used as a kernel. It's called the cross information potential (CIP). Therefore, the kernel is really between statistics of two datasets, not just two sample points. Of course, when a single data point is used to estiamte the PDF, it becomes just like the normal kernel method. The inner product defined by CIP uses the statistics of the data (which can be equivalently represented by the mean of samples in the RKHS). [See Xu, Paiva, Park, Principe 2009]
Same thing is growing in my research on spike train lately. Instead of defining a kernel between two spike trains, I would first estimate point processes from a set of spike train and then create an inner product based on CIP or other cpd kernels.
The question is where this would be useful. The single trial approximation is not so useful because the structure over the trials is lost. It would be same as the mCI RKHS we proposed. [See Paiva, Park, Principe 2009] The biggest problem is how to get a nonparametric estimator for point processes. They are very high dimension!!