e/Oja's rule

Information
has gloss	eng: Ojas learning rule, or simply Ojas rule, named after a 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.
lexicalization	eng: Oja's rule
instance of	(noun) any network of neurons or nuclei that function together to perform some function in the body neural network, neural net