title: Estimation and testing problems in auditory neuroscience via clustering.
abstract: The processing of auditory information in neurons is an important area in neuroscience. We consider statistical analysis for an electrophysiological experiment related to this area. The recorded synaptic current responses from the experiment are observed as clusters, where the number of clusters is related to an important characteristic of the auditory system. This number is difficult to estimate visually because the clusters are blurred by biological variability. Using singular value decomposition and a Gaussian mixture model, we develop an estimator for the number of clusters. Additionally, we provide a method for hypothesis testing and sample size determination in the two-sample problem. We illustrate our approach with both simulated and experimental data.
authors.affiliation: Department of Statistics, Sungkyunkwan University, 03063 Seoul, Korea.
authors.forename: Youngdeok
authors.initials: Y
authors.lastname: Hwang
authors.affiliation: Department of Anesthesiology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin 53706, U.S.A.
authors.forename: Samantha
authors.initials: S
authors.lastname: Wright
authors.affiliation: Department of Statistics, University of Wisconsin-Madison, Madison, Wisconsin 53706, U.S.A.
authors.forename: Bret M
authors.initials: BM
authors.lastname: Hanlon
mesh_terms: D016000:Cluster Analysis; D016011:Normal Distribution; D018401:Sample Size
keywords: Electrophysiology; Gaussian mixture; Singular value decomposition; Synaptic current