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On the Analysis of Multi-Channel Neural Spike Data
Bo Chen · David Carlson · Lawrence Carin

Wed Dec 14 08:45 AM -- 02:59 PM (PST) @ None #None

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 channels. 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 refractoryperiod violations, it allows the “appearance” and “disappearance” of neurons over time, and it models smooth variation in the spike statistics.

Author Information

Bo Chen (Duke University)
David Carlson (Duke University)
Lawrence Carin (KAUST)

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