title: A rate and history-preserving resampling algorithm for neural spike trains.
abstract: Resampling methods are popular tools for exploring the statistical structure of neural spike trains. In many applications, it is desirable to have resamples that preserve certain non-Poisson properties, like refractory periods and bursting, and that are also robust to trial-to-trial variability. Pattern jitter is a resampling technique that accomplishes this by preserving the recent spiking history of all spikes and constraining resampled spikes to remain close to their original positions. The resampled spike times are maximally random up to these constraints. Dynamic programming is used to create an efficient resampling algorithm.
authors.affiliation: Department of Statistics, Carnegie Mellon University, Pittsburgh, PA 15213, USA. mtharris@cmu.edu
authors.forename: Matthew T
authors.initials: MT
authors.lastname: Harrison
authors.forename: Stuart
authors.initials: S
authors.lastname: Geman
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000465:Algorithms; D000818:Animals; D008390:Markov Chains; D008959:Models, Neurological; D009474:Neurons / Q000502:physiology*; D017711:Nonlinear Dynamics; D013997:Time Factors