Journal of Mathematical Biology

, Volume 15, Issue 3, pp 267-273

First online:

Simplified neuron model as a principal component analyzer

  • Erkki OjaAffiliated withInstitute of Mathematics, University of Kuopio

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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