About: Oja's rule

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Oja's learning rule, or simply Oja's rule, named after Finnish computer scientist Erkki Oja, is a model of how neurons in the brain or in artificial neural networks change connection strength, or learn, over time. It is a modification of the standard Hebb's Rule (see Hebbian learning) that, through multiplicative normalization, solves all stability problems and generates an algorithm for principal components analysis. This is a computational form of an effect which is believed to happen in biological neurons.

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rdfs:label	Oja's rule (en)
rdfs:comment	Oja's learning rule, or simply Oja's rule, named after Finnish computer scientist Erkki Oja, is a model of how neurons in the brain or in artificial neural networks change connection strength, or learn, over time. It is a modification of the standard Hebb's Rule (see Hebbian learning) that, through multiplicative normalization, solves all stability problems and generates an algorithm for principal components analysis. This is a computational form of an effect which is believed to happen in biological neurons. (en)
dcterms:subject	Biophysics Neural circuits Hebbian theory Computational neuroscience Artificial neural networks
Wikipage page ID	9617564 (xsd:integer)
Wikipage revision ID	1116578612 (xsd:integer)
Link from a Wikipage to another Wikipage	Cartesian coordinates Power series Big-O notation Variance Convolution Biophysics Eigenvalue Eigenvector Generalized Hebbian Algorithm Generalized Hebbian algorithm Neural circuits Hebbian theory Contrastive Hebbian learning Artificial neural networks Synaptic plasticity Computational neuroscience Activation function Learning rule Long-term depression Long-term potentiation Alan Turing Erkki Oja Euclidean vector Stochastic gradient descent Artificial neural networks Binocular vision Biophysics BCM theory Lyapunov function Self-organizing map Without loss of generality Neural backpropagation Principal components analysis Hebbian learning Independent components analysis
Link from a Wikipage to an external page	http://www.scholarpedia.org/article/Oja_learning_rule http://users.ics.aalto.fi/oja
sameAs	Oja's rule Oja's rule Oja's rule Oja's rule Oja's rule Oja's rule
dbp:wikiPageUsesTemplate	dbt:= dbt:Math dbt:Reflist dbt:Short_description dbt:Hebbian_learning
has abstract	Oja's learning rule, or simply Oja's rule, named after Finnish computer scientist Erkki Oja, is a model of how neurons in the brain or in artificial neural networks change connection strength, or learn, over time. It is a modification of the standard Hebb's Rule (see Hebbian learning) that, through multiplicative normalization, solves all stability problems and generates an algorithm for principal components analysis. This is a computational form of an effect which is believed to happen in biological neurons. (en)
gold:hypernym	Model
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foaf:isPrimaryTopicOf	wikipedia-en:Oja's_rule
is Link from a Wikipage to another Wikipage of	Oja Frederick Sanger Generalized Hebbian algorithm Contrastive Hebbian learning Hebbian theory Learning rule Erkki Oja Principal component analysis Synaptic weight
is Wikipage disambiguates of	Oja
is known for of	Erkki Oja
is known for of	Erkki Oja
is foaf:primaryTopic of	wikipedia-en:Oja's_rule

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