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- W2132511476 abstract "The maximization of the mutual information between the stochastic outputs neurons and the clamped inputs is used as an unsupervised criterion for training a Boltzmann machine. The resulting learning rule contains two terms corresponding to the Hebbian and anti-Hebbian learning. The two terms are weighted by the amount of transmitted information in the learning synapse, giving an information-theoretic interpretation to the proportionality constant given in the biological rule of Hebb. The anti-Hebbian term causes the convergence of weights. Simulation for the encoder problem demonstrates optimal performance of this method." @default.
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- W2132511476 date "2005-08-24" @default.
- W2132511476 modified "2023-09-26" @default.
- W2132511476 title "Self-organization in stochastic neural networks" @default.
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- W2132511476 doi "https://doi.org/10.1109/ijcnn.1993.713958" @default.
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