Statistical issues in the analysis of neuronal data. Export

@article{Kass2005, abstract = {Analysis of data from neurophysiological investigations can be challenging. Particularly when experiments involve dynamics of neuronal response, scientific inference can become subtle and some statistical methods may make much more efficient use of the data than others. This article reviews well-established statistical principles, which provide useful guidance, and argues that good statistical practice can substantially enhance results. Recent work on estimation of firing rate, population coding, and time-varying correlation provides improvements in experimental sensitivity equivalent to large increases in the number of neurons examined. Modern nonparametric methods are applicable to data from repeated trials. Many within-trial analyses based on a Poisson assumption can be extended to non-Poisson data. New methods have made it possible to track changes in receptive fields, and to study trial-to-trial variation, with modest amounts of data.}, author = {Kass, Robert E. and Ventura, Val\'{e}rie and Brown, Emery N.}, citeulike-article-id = {1851076}, citeulike-linkout-0 = {http://dx.doi.org/10.1152/jn.00648.2004}, doi = {10.1152/jn.00648.2004}, journal = {J Neurophysiol}, keywords = {15985692, action, and, animals, bayes, bibtex-import, data, factors, humans, interpretation, models, neurological, neurons, potentials, sensitivity, specificity, statistical, theorem, time}, month = {Jul}, number = {1}, pages = {8--25}, posted-at = {2007-11-01 13:26:49}, priority = {2}, title = {{S}tatistical issues in the analysis of neuronal data.}, url = {10.1152/jn.00648.2004}, volume = {94}, year = {2005} }

TY - JOUR ID - Kass2005 L3 - citeulike-article-id:1851076 N2 - Analysis of data from neurophysiological investigations can be challenging. Particularly when experiments involve dynamics of neuronal response, scientific inference can become subtle and some statistical methods may make much more efficient use of the data than others. This article reviews well-established statistical principles, which provide useful guidance, and argues that good statistical practice can substantially enhance results. Recent work on estimation of firing rate, population coding, and time-varying correlation provides improvements in experimental sensitivity equivalent to large increases in the number of neurons examined. Modern nonparametric methods are applicable to data from repeated trials. Many within-trial analyses based on a Poisson assumption can be extended to non-Poisson data. New methods have made it possible to track changes in receptive fields, and to study trial-to-trial variation, with modest amounts of data. IS - 1 JF - J Neurophysiol EP - 25 TI - {S}tatistical issues in the analysis of neuronal data. VL - 94 SP - 8 KW - 15985692 KW - action KW - and KW - animals KW - bayes KW - bibtex-import KW - data KW - factors KW - humans KW - interpretation KW - models KW - neurological KW - neurons KW - potentials KW - sensitivity KW - specificity KW - statistical KW - theorem KW - time AU - Kass, Robert AU - Ventura, Valérie AU - Brown, Emery PY - 2005/Jul// UR - http://dx.doi.org/10.1152/jn.00648.2004 ER -