Analysis of spike train data: Classification and Bayesian alignment

作者：Cheng Wen; Dryden Ian L; Hitchcock David B^*; Le Huiling

来源：Electronic Journal of Statistics, 2014, 8(2): 1786-1792.

DOI：10.1214/14-EJS865C

• Markov chain Monte Carlo
• Poisson process
• registration
• time warping

We analyze a data set of spike trains obtained under four different experimental conditions. We model the data curves via mixtures of normal densities. The peak locations in the fitted curves are modeled via a non-homogeneous Poisson process and classification of the spike trains into groups may be done based on the estimated spacings between peaks. We employ a Bayesian, MCMC-based registration method to align the fitted curves and summarize the data using meaningful functional statistics and posterior intervals.

• 出版日期2014