Analysis of Spike Train Data: Classification and Bayesian Alignment

• Activity Number: 178
• Type: Contributed
• Date/Time: Monday, August 4, 2014 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistical Learning and Data Mining
• Author(s): David B. Hitchcock*+ and Wen Cheng and Ian L. Dryden and Huiling Le
• Companies: University of South Carolina and University of South Carolina and University of Nottingham and University of Nottingham
• Keywords: Markov chain Monte Carlo ; Poisson process ; Registration ; Time warping

Abstract

Spike train data consist of functions in which the response (the activity of a neuron in the brain) is measured over time. The spike train functions characteristically have numerous sharply peaked spikes at time locations of interest. 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 the experimental groups may be done using a likelihood approach 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. We also obtain posterior intervals that reflect the uncertainty in both the registration process and the mean curve.