## Simple Neuron Models for Independent Component Analysis (1997)

Venue: | Int. Journal of Neural Systems |

Citations: | 24 - 3 self |

### BibTeX

@ARTICLE{Hyvärinen97simpleneuron,

author = {Aapo Hyvärinen and Erkki Oja},

title = {Simple Neuron Models for Independent Component Analysis},

journal = {Int. Journal of Neural Systems},

year = {1997},

volume = {7},

pages = {671--687}

}

### Years of Citing Articles

### OpenURL

### 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 component 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 distributions of the independent components.