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.
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Oja, E. Simplified neuron model as a principal component analyzer. J. Math. Biology 15, 267–273 (1982). https://doi.org/10.1007/BF00275687
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DOI: https://doi.org/10.1007/BF00275687