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• W2170108436 abstract "Studies Hebbian learning in linear neural networks with emphasis on the self-association information principle. This criterion, in one-layer networks, leads to the space of the principal components and can be generalized to arbitrary architectures. The self-association paradigm appears to be very promising because it accounts for the fundamental features of Hebbian synaptic learning and generalizes the various techniques proposed for adaptive principal component networks. The authors also include a set of simulations that compare various neural architectures and algorithms." @default.
• W2170108436 created "2016-06-24" @default.
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• W2170108436 date "1995-01-01" @default.
• W2170108436 modified "2023-09-26" @default.
• W2170108436 title "Self-association and Hebbian learning in linear neural networks" @default.
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• W2170108436 doi "https://doi.org/10.1109/72.410360" @default.
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