title: Simple neuron models for independent component analysis.
abstract: Recently, several neural algorithms have been introduced for Independent component Analysis. Here we approach the problem from the point of view of a single neuron. First, simple Hebbian-like learning rules are introduced for estimating one of the independent components from sphered data. Some of the learning rules can be used to estimate an independent components which has a negative kurtosis, and the others estimate a component of positive kurtosis. Next, a two-unit system is introduced to estimate an independent component of any kurtosis. The results are then generalized to estimate independent components from non-sphered (raw) mixtures. To separate several independent components, a system of several neurons with linear negative feedback is used. The convergence of the learning rules is rigorously proven without any unnecessary hypotheses on the distribution of the independent components.
authors.affiliation: Helsinki University of Technology, Laboratory of Computer and Information Science, Espoo, Finland. aapo.hyvarinen@hut.fihttp://nucleus.hut.fi/-aapo
authors.forename: A
authors.initials: A
authors.lastname: Hyvärinen
authors.forename: E
authors.initials: E
authors.lastname: Oja
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D008959:Models, Neurological; D015233:Models, Statistical; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*