Simplified neuron model as a principal component analyzer

@article{Oja1982SimplifiedNM,
  title={Simplified neuron model as a principal component analyzer},
  author={E. Oja},
  journal={Journal of Mathematical Biology},
  year={1982},
  volume={15},
  pages={267-273}
}
  • E. Oja
  • Published 1982
  • Mathematics, Medicine
  • Journal of Mathematical Biology
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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References

SHOWING 1-8 OF 8 REFERENCES
A physiological mechanism for Hebb's postulate of learning.
  • G. Stent
  • Chemistry, Medicine
  • Proceedings of the National Academy of Sciences of the United States of America
  • 1973
  • 647
  • PDF
wchastic. approximation methods for constrained and unconstrained systems
  • 859
  • Highly Influential
Development of Specificity in the Cat Visual Cortex
  • 59
On stochastic approximation of eigenvectors and eigenvalues of the expectation
  • 1975
Ordinary Differential Equations.
  • 2,714