In this talk, I will outline the basic theory of random point process and
illustrate how to apply point process theory for statistical modeling and
inference in the fields of computational neuroscience and bioengineering. An
overview of statistical inference tools is given.

Research applications include neural coding and decoding, human
cardiovascular modeling and signal processing, Calcium imaging, and many
others.

A broad overview of relevant research projects is presented. I will also use
examples to show how the interactions of continuous and discrete-valued
signal processing open many interesting research questions and topics.