A rate and history-preserving resampling algorithm for neural spike trains. Export

@article{citeulike:5198384, 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.}, author = {Harrison, M. T. and Geman, S.}, citeulike-article-id = {5198384}, citeulike-linkout-0 = {http://dx.doi.org/10.1162/neco.2008.03-08-730}, citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/19018703}, citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=19018703}, doi = {10.1162/neco.2008.03-08-730}, issn = {0899-7667}, journal = {Neural computation}, keywords = {amyproject}, month = {May}, number = {5}, pages = {1244--1258}, posted-at = {2009-07-18 19:51:21}, priority = {2}, title = {A rate and history-preserving resampling algorithm for neural spike trains.}, url = {http://dx.doi.org/10.1162/neco.2008.03-08-730}, volume = {21}, year = {2009} }

TY - JOUR ID - citeulike:5198384 L3 - citeulike-article-id:5198384 N2 - 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. IS - 5 JF - Neural computation SN - 0899-7667 EP - 1258 TI - A rate and history-preserving resampling algorithm for neural spike trains. VL - 21 SP - 1244 KW - amyproject AU - Harrison, MT AU - Geman, S PY - 2009/05// UR - http://dx.doi.org/10.1162/neco.2008.03-08-730 ER -