Article

Journal of Mathematical Biology

, Volume 15, Issue 3, pp 267-273

Simplified neuron model as a principal component analyzer

  • Erkki OjaAffiliated withInstitute of Mathematics, University of Kuopio

Rent the article at a discount

Rent now

* Final gross prices may vary according to local VAT.

Get Access

Abstract

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.

Key words

Neuron models Synaptic plasticity Stochastic approximation