Bayesian estimation of stimulus responses in Poisson spike trains. Export

@article{citeulike:91728, abstract = {A Bayesian method is developed for estimating neural responses to stimuli, using likelihood functions incorporating the assumption that spike trains follow either pure Poisson statistics or Poisson statistics with a refractory period. The Bayesian and standard estimates of the mean and variance of responses are similar and asymptotically converge as the size of the data sample increases. However, the Bayesian estimate of the variance of the variance is much lower. This allows the Bayesian method to provide more precise interval estimates of responses. Sensitivity of the Bayesian method to the Poisson assumption was tested by conducting simulations perturbing the Poisson spike trains with noise. This did not affect Bayesian estimates of mean and variance to a significant degree, indicating that the Bayesian method is robust. The Bayesian estimates were less affected by the presence of noise than estimates provided by the standard method.}, address = {Cognitive Brain Mapping Laboratory, RIKEN Brain Science Institute, Saitama 351-0198, Japan. sidney@brain.riken.jp}, author = {Lehky, S. R.}, citeulike-article-id = {91728}, citeulike-linkout-0 = {http://dx.doi.org/10.1162/089976604323057407}, citeulike-linkout-1 = {http://neco.mitpress.org/cgi/content/abstract/16/7/1325}, citeulike-linkout-2 = {http://view.ncbi.nlm.nih.gov/pubmed/15165392}, citeulike-linkout-3 = {http://www.hubmed.org/display.cgi?uids=15165392}, doi = {10.1162/089976604323057407}, issn = {0899-7667}, journal = {Neural Comput}, keywords = {bayesian, pointprocess, statistics}, month = {July}, number = {7}, pages = {1325--1343}, posted-at = {2005-03-01 04:44:16}, priority = {2}, title = {Bayesian estimation of stimulus responses in Poisson spike trains.}, url = {http://dx.doi.org/10.1162/089976604323057407}, volume = {16}, year = {2004} }

TY - JOUR ID - citeulike:91728 L3 - citeulike-article-id:91728 N2 - A Bayesian method is developed for estimating neural responses to stimuli, using likelihood functions incorporating the assumption that spike trains follow either pure Poisson statistics or Poisson statistics with a refractory period. The Bayesian and standard estimates of the mean and variance of responses are similar and asymptotically converge as the size of the data sample increases. However, the Bayesian estimate of the variance of the variance is much lower. This allows the Bayesian method to provide more precise interval estimates of responses. Sensitivity of the Bayesian method to the Poisson assumption was tested by conducting simulations perturbing the Poisson spike trains with noise. This did not affect Bayesian estimates of mean and variance to a significant degree, indicating that the Bayesian method is robust. The Bayesian estimates were less affected by the presence of noise than estimates provided by the standard method. IS - 7 JF - Neural Comput SN - 0899-7667 AD - Cognitive Brain Mapping Laboratory, RIKEN Brain Science Institute, Saitama 351-0198, Japan. sidney@brain.riken.jp EP - 1343 TI - Bayesian estimation of stimulus responses in Poisson spike trains. VL - 16 SP - 1325 KW - bayesian KW - pointprocess KW - statistics AU - Lehky, SR PY - 2004/07// UR - http://dx.doi.org/10.1162/089976604323057407 ER -