On the Analysis of Multi-Channel Neural Spike Data
Bo Chen, David Carlson, Lawrence Carin · 2011
Nonparametric Bayesian methods are developed for analysis of multi-channel spike-train data, with the feature learning and spike sorting performed jointly. The feature learning and sorting are performed simultaneously across all chan-nels. Dictionary learning is implemented via the beta-Bernoulli process, with spike sorting performed via the dynamic hierarchical Dirichlet process (dHDP), with these two models coupled. The dHDP is augmented to eliminate refractory-period violations, it allows the “appearance ” and “disappearance ” of neurons over time, and it models smooth variation in the spike statistics. 1