title: Two statistical methods for analyzing multiple neuronal data.
abstract: We applied two methods based on statistical analysis to neuronal data of multiple neurons. One is based on ANOVA (analysis of variance) and the other PCA (principal component analysis). These methods describe neuronal responses of multiple neurons from two different view-points; the former is that neuronal encoding is performed by cell by cell basis whereas the latter is that neuronal encoding is performed by a population of neurons. Applying these methods to data from single-unit recording experiments, we demonstrate that the former method enables us to determine whether a population of neurons significantly responds to a set of stimuli whereas the latter method can be used to determine whether collective neuronal responses have significant structure along the time axis.
authors.affiliation: Justsystem Scientific Institute, Tokyo, Japan.
authors.forename: T
authors.initials: T
authors.lastname: Takahashi
mesh_terms: D000704:Analysis of Variance; D000818:Animals; D019277:Entropy; D008251:Macaca; D009474:Neurons; D018709:Statistics, Nonparametric