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Oja's rule - Wikipedia
https://en.wikipedia.org/wiki/Oja%27s_rule
WEBIt 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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Oja learning rule - Scholarpedia
http://www.scholarpedia.org/article/Oja_learning_rule
WEBOct 21, 2011 · The Oja learning rule (Oja, 1982) is a mathematical formalization of this Hebbian learning rule, such that over time the neuron actually learns to compute a principal component of its input stream. Contents. 1The simple neuron model. 1.1From Hebbian learning to the Oja learning rule. 2Oja learning rule and principal component …
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PCA by neurons - MIT
https://www.mit.edu/~9.54/fall14/slides/Class11.pdf
WEBOja rule: Δw = αv(x – vw) Sanger rule: Δw i = αv i (x – Σ k=1 i v k w k) Oja multi-unit rule: Δw i = αv i (x – Σ 1 N v k w k) In Sanger the sum is for k up to j, all previous units, rather than all units. Was shown to converge Oja network converges in simulations
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Oja’s rule: Derivation, Properties - ETH Z
https://cse-lab.ethz.ch/wp-content/uploads/2019/10/tutorial_3_ojas_rule_pdf.pdf
WEBOja's rule: Derivation, Properties. where the index n denotes the iteration number, while D is the dimension of the data vector, the neuron number. In vector notation wn + ynxn wn+1 =. where we used the quotient rule (f=g)0 = (f0g g0f)=g2 and that (f(g(x))0 = f0(g(x))g0(x). By simplifying the expression 3, we get:
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Oja's rule - Eyewire
https://wiki.eyewire.org/Oja%27s_rule
WEBJun 24, 2016 · Oja's rule is simply Hebb's rule with weight normalization, approximated by a Taylor series with terms of ignored for n>1 since η is small. It can be shown that Oja's rule extracts the first principal component of the data set. If there are many Oja's rule neurons, then all will converge to the same principal component, which is not useful.
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What Does Oja’s rule do? - MIT Computer Science and Artificial
http://www.ai.mit.edu/courses/6.892/lecture9-html/sld018.htm
WEBWhat Does Oja’s rule do? - MIT Computer Science and Artificial ...
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Generating chaos by Oja's rule - ScienceDirect
https://www.sciencedirect.com/science/article/pii/S0925231203004132
WEBOct 1, 2003 · Our simulation results have demonstrated the existence of chaotic behavior in a simple adaptive neuron trained by Oja's rule. To the best of our knowledge, this is the first observation of chaotic behavior in an unsupervised learning algorithm.
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Unsupervised Learning: BCM or Oja's Rule
https://cs.stackexchange.com/questions/42611/unsupervised-learning-bcm-or-ojas-rule
WEBIn Oja's rule: dw/dt = k*x*y - w*y^2. Where x is the value at the input neuron, y is the value at the output neuron and w is the connection strength between the two. The idea is that this prevents weights from growing out of proportion. In BCM: dw/dt = k*(y-theta)*x.
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The topology of representation teleportation, regularized Oja's rule
https://cbmm.mit.edu/video/topology-representation-teleportation-regularized-ojas-rule-and-weight-symmetry
WEBThe topology of representation teleportation, regularized Oja's rule, and weight symmetry | The Center for Brains, Minds & Machines. You are here. CBMM, NSF STC » The topology of representation teleportation, regularized Oja's rule, and weight symmetry.
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Global Analysis of Oja’s Flow for Neural Networks - ResearchGate
https://www.researchgate.net/publication/3301865_Global_Analysis_of_Oja's_Flow_for_Neural_Networks
WEBGlobal Analysis of Oja’s Flow for Neural Networks. October 1994. IEEE Transactions on Neural Networks. DOI: IEEE Xplore. Authors: Wei-Yong Yan. Uwe Helmke. University of Wuerzburg. John B....
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