Simplified neuron model as a principal component analyzer
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 wordsNeuron models Synaptic plasticity Stochastic approximation
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