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Estimation and testing problems in auditory neuroscience via clustering
Author(s) -
Hwang Youngdeok,
Wright Samantha,
Hanlon Bret M.
Publication year - 2017
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/biom.12652
Subject(s) - computer science , cluster analysis , estimation , neuroscience , artificial intelligence , psychology , engineering , systems engineering
Summary 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.