A. Hyvärinen.  One-Unit Learning Rules for Independent Component Analysis.
In Advances in Neural Information Processing System 9 (NIPS*96), MIT Press, 1997, pp.  480--486.
Postscript  gzipped PostScript.

Abstract: Neural one-unit learning rules for the problem of Independent Component Analysis (ICA) and blind source separation are introduced. In these new algorithms, every ICA neuron develops into a separator that finds one of the independent components. The learning rules use very simple constrained Hebbian/anti-Hebbian learning in which decorrelating feedback may be added. To speed up the convergence of these stochastic gradient descent rules, a novel computationally efficient fixed-point algorithm is introduced.