Neural Comput - Neural associative memory with optimal Bayesian learning.

Tópicos

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Resumo

Neural associative memories are perceptron-like single-layer networks with fast synaptic learning typically storing discrete associations between pairs of neural activity patterns. Previous work optimized the memory capacity for various models of synaptic learning: linear Hopfield-type rules, the Willshaw model employing binary synapses, or the BCPNN rule of Lansner and Ekeberg, for example. Here I show that all of these previous models are limit cases of a general optimal model where synaptic learning is determined by probabilistic Bayesian considerations. Asymptotically, for large networks and very sparse neuron activity, the Bayesian model becomes identical to an inhibitory implementation of the Willshaw and BCPNN-type models. For less sparse patterns, the Bayesian model becomes identical to Hopfield-type networks employing the covariance rule. For intermediate sparseness or finite networks, the optimal Bayesian learning rule differs from the previous models and can significantly improve memory performance. I also provide a unified analytical framework to determine memory capacity at a given output noise level that links approaches based on mutual information, Hamming distance, and signal-to-noise ratio.

Resumo Limpo

neural associ memori perceptronlik singlelay network fast synapt learn typic store discret associ pair neural activ pattern previous work optim memori capac various model synapt learn linear hopfieldtyp rule willshaw model employ binari synaps bcpnn rule lansner ekeberg exampl show previous model limit case general optim model synapt learn determin probabilist bayesian consider asymptot larg network spars neuron activ bayesian model becom ident inhibitori implement willshaw bcpnntype model less spars pattern bayesian model becom ident hopfieldtyp network employ covari rule intermedi spars finit network optim bayesian learn rule differ previous model can signific improv memori perform also provid unifi analyt framework determin memori capac given output nois level link approach base mutual inform ham distanc signaltonois ratio

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