%0 Journal Article %1 oja-simplified-neuron-model-1982 %A Oja, Erkki %D 1982 %J Journal of Mathematical Biology %K hebbian learning %N 3 %P 267--273 %R 10.1007/BF00275687 %T Simplified neuron model as a principal component analyzer %U http://dx.doi.org/10.1007/BF00275687 %V 15 %X 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.

@article{oja-simplified-neuron-model-1982, abstract = {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.}, added-at = {2010-07-13T13:38:17.000+0200}, author = {Oja, Erkki}, biburl = {https://www.bibsonomy.org/bibtex/26f8bfecd14d964d63ce0064e73e49e94/mhwombat}, citeulike-article-id = {1222082}, citeulike-linkout-0 = {http://dx.doi.org/10.1007/BF00275687}, citeulike-linkout-1 = {http://linkinghub.elsevier.com/retrieve/pii/BF00275687}, citeulike-linkout-2 = {http://www.springerlink.com/content/u9u6120r003825u1}, day = 1, doi = {10.1007/BF00275687}, file = {:/wombat/taighde/docs/neural_nets/Oja1982.pdf:PDF;:Oja1982.pdf:PDF}, groups = {public}, interhash = {2c3ab1164432ab677fe8dfa708fa6093}, intrahash = {6f8bfecd14d964d63ce0064e73e49e94}, issn = {0303-6812}, journal = {Journal of Mathematical Biology}, keywords = {hebbian learning}, month = nov, number = 3, pages = {267--273}, posted-at = {2010-06-24 05:17:07}, priority = {2}, timestamp = {2016-07-12T19:25:30.000+0200}, title = {Simplified neuron model as a principal component analyzer}, url = {http://dx.doi.org/10.1007/BF00275687}, username = {mhwombat}, volume = 15, year = 1982 }