# Simplified neuron model as a principal component analyzer

@article{Oja1982SimplifiedNM, title={Simplified neuron model as a principal component analyzer}, author={Erkki Oja}, journal={Journal of Mathematical Biology}, year={1982}, volume={15}, pages={267-273} }

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.

## 2,391 Citations

### A new simple /spl infin/OH neuron model as a principal component analyzer

- Computer ScienceCanadian Conference on Electrical and Computer Engineering 2001. Conference Proceedings (Cat. No.01TH8555)
- 2001

An algorithm for unsupervised learning based on the Hebbian learning rule is presented and a simple "almost linear" neuron model is analyzed, showing that the model neuron tends to extract the principal component from a stationary input vector sequence.

### A simple biologically inspired principal component analyzer-ModH neuron model

- Computer Science6th Seminar on Neural Network Applications in Electrical Engineering
- 2002

The solution proposed here is a modified Hebbian rule in which the modification of the synaptic strength is proportional not to pre- and post-synaptic activity, but instead to the pre- synaptic and averaged value of post- Synaptic activity.

### A new simple ∞OH neuron model as a biologically plausible principal component analyzer

- Computer ScienceIEEE Trans. Neural Networks
- 2003

The solution proposed here is a modified Hebbian rule, in which the modification of the synaptic strength is proportional not to pre- and post Synaptic activity, but instead to the presynaptic and averaged value of postsynaptic activity.

### Study of a Self-Learning Artifical Neuron Model

- Computer Science
- 1993

Giving a large autonomy to each cell and using a statistical average of the entries as a self-learning mechanism are not new ideas [1,2]. But linking statistical and linear dependencies through…

### A simple, homogeneous parallel PCA network

- Computer ScienceProceedings of 1993 International Conference on Neural Networks (IJCNN-93-Nagoya, Japan)
- 1993

A form of ANN using interneurons has been shown to be capable of performing a principal component analysis of the input data and a new parallel algorithm is proposed using the innate properties of the network.

### A Symmetrical Lateral Inhibition Network for PCA and Feature Decorrelation

- Computer Science
- 1993

A new network model for data decorrelation and principal component analysis which relays on the biological plausible lateral inhibition and the assignment of the eigenvectors to the neuronal weights are not predefined.

### Neural processing of information

- Computer ScienceProceedings of 1994 Workshop on Information Theory and Statistics
- 1994

An application of the Shannon information measures of entropy and mutual information taken together in the context of the proposed model lead to the Hopfield neuron model with a conditionalized Hebbian learning rule and sigmoidal transfer characteristic.

### Neural processing of information

- Computer ScienceProceedings of 1994 IEEE International Symposium on Information Theory
- 1994

An application of the Shannon information measures of entropy and mutual information taken together in the context of the proposed model lead to the Hopfield neuron model with a conditionalized Hebbian learning rule and sigmoidal transfer characteristic.

### Unsupervised learning algorithm for signal separation

- Computer Science2014 IEEE Symposium on Computational Intelligence for Multimedia, Signal and Vision Processing (CIMSIVP)
- 2014

A neural network capable of separating inputs in an unsupervised manner and can operate as a general clustering algorithm, with neighboring neurons responding to geometrically similar inputs is presented.

### Network Dynamics Governed by Lyapunov Functions: From Memory to Classification

- Computer ScienceTrends in Neurosciences
- 2020

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