title: A nonparametric quantification of neural response field structures.
abstract: The response fields of higher cortical neurons are usually approximated with smooth mathematical functions for the purpose of population parameterization or theoretical modeling. We used instead two nonparametric methods (principal component analysis and independent component analysis), which provided a basis for the response field clustering. Although both methods performed satisfactorily, the principal component analysis space is more straightforward to calculate. It also gave a clear preference toward the smallest number of functional response field classes. Clustering was performed with both K-means and superparamagnetic clustering algorithms with similar results. We also show that the shapes of the eigenvectors remain consistent regardless of the response field data sets size. This finding reflects the fact that the response fields were generated by the same neural network and encode the same underlying process.
authors.affiliation: Division of Biology, California Institute of Technology, Pasadena, California 91125, USA. brozovic@vis.caltech.edu
authors.forename: Marina
authors.initials: M
authors.lastname: Brozović
authors.forename: Richard A
authors.initials: RA
authors.lastname: Andersen
mesh_terms: D001931:Brain Mapping; D002540:Cerebral Cortex / Q000166:cytology; D016000:Cluster Analysis; D006801:Humans; D008959:Models, Neurological; D009474:Neurons / Q000502:physiology*; D025341:Principal Component Analysis / Q000379:methods; D012677:Sensation / Q000502:physiology*; D018709:Statistics, Nonparametric