Simplified neuron model as a principal component analyzer

Erkki Oja

doi:10.1007/bf00275687

journal article Nov 01, 1982

Simplified neuron model as a principal component analyzer

Erkki Oja

Journal of Mathematical Biology Vol. 15 No. 3 pp. 267-273 · Springer Science and Business Media LLC

View at Publisher Save 10.1007/bf00275687

Topics

No keywords indexed for this article. Browse by subject →

References

14

[1]

Anderson, T. W.: An introduction to multivariate statistical analysis. New York: Wiley 1958

[2]

Cooper, L. N., Liberman, F., Oja, E.: A theory for the acquisition and loss of neuron specificity in visual cortex. Biol. Cyb. 33, 9?28 (1979) 10.1007/bf00337414

[3]

Hale, J. K.: Ordinary differential equations. New York: Wiley 1969

[4]

Self-organized formation of topologically correct feature maps

Teuvo Kohonen

Biological Cybernetics 1982 10.1007/bf00337288

[5]

Kohonen, T., Lehtiö, P., Oja, E.: Storage and processing of information in distributed associative memory systems. In: Hinton, G., Anderson, J. A. (eds.). Parallel models of associative memory, pp. 105?143. Hillsdale: Erlbaum 1981

[6]

Kushner, H., Clark, D.: Stochastic approximation methods for constrained and unconstrained systems. New York: Springer 1978 10.1007/978-1-4684-9352-8

[7]

Ljung, L.: Analysis of recursive stochastic algorithms. IEEE Trans. Automatic Control AC-22, 551?575 (1977) 10.1109/tac.1977.1101561

[8]

von der Malsburg, C.: Self-organization of orientation sensitive cells in the striate cortex. Kybernetik 14, 85?100 (1973) 10.1007/bf00288907

[9]

A logical calculus of the ideas immanent in nervous activity

Warren S. McCulloch, Walter Pitts

The Bulletin of Mathematical Biophysics 1943 10.1007/bf02478259

[10]

Nass, M., Cooper, L. N.: A theory for the development of feature detecting cells in visual cortex. Biol. Cyb. 19, 1?18 (1975) 10.1007/bf00319777

[11]

Oja, E., Karhunen, J.: On stochastic approximation of eigenvectors and eigenvalues of the expectation of a random matrix. Helsinki University of Technology, Report TKK-F-A458 (1981)

[12]

Perez, R., Glass, L., Shlaer, R. J.: Development of specificity in the cat visual cortex. J. Math. Biol. 1, 275?288 (1975) 10.1007/bf03380115

[13]

Stent, G. S.: A psychological mechanism for Hebb's postulate of learning. Proc. Natl. Acad. Sciences 70, 997?1001 (1973) 10.1073/pnas.70.4.997

[14]

Takeuchi, A., Amari, S.: Formation of topographic maps and columnar microstructures in nerve fields. Biol. Cyb. 35, 63?72 (1979) 10.1007/bf00337432

Cited By

1,821

A linear self-normalizing spike-timing-dependent plasticity rule for online learning in spiking neural networks

Genevieve C. Fahey, Samuel J. Ippolito · 2026

Neurocomputing

Randomized forward mode gradient for spiking neural networks in scientific machine learning

Ruyin Wan, Qian Zhang · 2026

Neural Networks

Compact representation of transonic airfoil buffet flows with observable-augmented machine learning

Kai Fukami, Yuta Iwatani · 2025

Journal of Fluid Mechanics

Passive exposure to task-relevant stimuli enhances categorization learning

Christian Schmid, Muhammad Haziq · 2024

eLife

Modelling continual learning in humans with Hebbian context gating and exponentially decaying task signals

Timo Flesch, David G. Nagy · 2023

PLOS Computational Biology

Auto-Encoders in Deep Learning—A Review with New Perspectives

Shuangshuang Chen, Wei Guo · 2023

Mathematics

A neuro-inspired computational model of life-long learning and catastrophic interference, mimicking hippocampus novelty-based dopamine modulation and lateral inhibitory plasticity

Pierangelo Afferni, Federico Cascino-Milani · 2022

Frontiers in Computational Neurosci...

A Unifying Review of Deep and Shallow Anomaly Detection

Lukas Ruff, Jacob R. Kauffmann · 2021

Proceedings of the IEEE

Randomized numerical linear algebra: Foundations and algorithms

Per-Gunnar Martinsson, Joel A. Tropp · 2020

Acta Numerica

A Riemannian derivative-free Polak–Ribiére–Polyak method for tangent vector field

Teng-Teng Yao, Zhi Zhao · 2020

Numerical Algorithms

The covariance perceptron: A new paradigm for classification and processing of time series in recurrent neuronal networks

Matthieu Gilson, David Dahmen · 2020

PLOS Computational Biology

Neural correlates of sparse coding and dimensionality reduction

Michael Beyeler, Emily L. Rounds · 2019

PLOS Computational Biology

Advances and opportunities in machine learning for process data analytics

S. Joe Qin, Leo H. Chiang · 2019

Computers & Chemical Engineerin...

A Theoretical Framework to Derive Simple, Firing-Rate-Dependent Mathematical Models of Synaptic Plasticity

Janne Lappalainen, Juliane Herpich · 2019

Frontiers in Computational Neurosci...

SuperSpike: Supervised Learning in Multilayer Spiking Neural Networks

Friedemann Zenke, Surya Ganguli · 2018

Neural Computation

The temporal paradox of Hebbian learning and homeostatic plasticity

Friedemann Zenke, Wulfram Gerstner · 2017

Current Opinion in Neurobiology

Runoff evaluation for ungauged watersheds by SWAP model. 1. Application of artificial neural networks

E. M. Gusev, G. V. Ayzel · 2017

Water Resources

Working memory and response selection: A computational account of interactions among cortico-basalganglio-thalamic loops

Henning Schroll, Julien Vitay · 2012

Neural Networks

Linear and Nonlinear Projective Nonnegative Matrix Factorization

Zhirong Yang, Erkki Oja · 2010

IEEE Transactions on Neural Network...

Unsupervised learning of vowel categories from infant-directed speech

Gautam K. Vallabha, James L. McClelland · 2007

Proceedings of the National Academy...

Metrics

1,821

Citations

14

References

Details

Published: Nov 01, 1982
Vol/Issue: 15(3)
Pages: 267-273
License: View

Authors

E

Erkki Oja

Cite This Article

Erkki Oja (1982). Simplified neuron model as a principal component analyzer. Journal of Mathematical Biology, 15(3), 267-273. https://doi.org/10.1007/bf00275687

Simplified neuron model as a principal component analyzer

You May Also Like