Estimating conditional intensity function of a neural spike train by particle Markov Chain Monte Carlo and smoothing
Understanding neural activities is fundamental and challenging in decoding how the brain processes information. An essential part of the problem is to define a meaningful and quantitative characterization of neural activities when they are represented by a sequence of action potentials or a neural spike train. The thesis approaches to use a point process to represent a neural spike train, and such representation provides a conditional intensity function (CIF) to describe neural activities. The estimation procedure for CIF, including particle Markov Chain Monte Carlo (PMCMC) and smoothing, is introduced and applied to a real data set. From the CIF and its derivative of a neural spike train, we can successfully observe adaption behavior. Simulation study verifies that the estimation procedure provides reliable estimate of CIF. This framework provides a definite quantification of neural activities and facilitates further investigation of understanding the brain from neurological perspective.