A simple linear neuron model with constrained Hebbian-type synaptic modification is analyzed and a new class of unconstrained learning rules is derived. It is shown that the model neuron tends to extract the principal component from a stationary input vector sequence.
T. W. Anderson,et al.
An Introduction to Multivariate Statistical Analysis
J. Hale,et al.
Ordinary Differential Equations.
A physiological mechanism for Hebb's postulate of learning.
Proceedings of the National Academy of Sciences of the United States of America.
R. Pérez,et al.
Development of Specificity in the Cat Visual Cortex
Journal of Mathematical Biology.
Harold J. Kushner,et al.
wchastic. approximation methods for constrained and unconstrained systems
Roman Bek,et al.
Discourse on one way in which a quantum-mechanics language on the classical logical base can be built up
Teuvo Kohonen,et al.
Storage and Processing of Information in Distributed Associative Memory Systems