Estimating conditional intensity conditional function of a neural spike train by particle Markov chain Monte Carlo and smoothing

Date created: 
Neural data analysis
Neural spike train
Point process
State-space model
Particle Markov Chain Monte Carol

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.

Document type: 
Graduating extended essay / Research project
This thesis may be printed or downloaded for non-commercial research and scholarly purposes. Copyright remains with the author.
Senior supervisor: 
Jiguo Cao
Science: Department of Statistics and Actuarial Science
Thesis type: 
(Project) M.Sc.