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Retrieval properties of a neural network with an asymmetric learning rule

Elizabeth Gardner, Stephan Mertens and Annette Zippelius

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Abstract

We consider a Hebbian learning mechanism, which gives rise to a change in synaptic efficacies only if the postsynaptic neuron is active. The model is solved analytically in the limit of strong dilution. The network is shown to classify initial configurations according to their mean activity and their overlap with one of the learnt patterns. The capacity of the network is calculated as a function of threshold.

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BiBTeX Entry

@article{,
   author    = {Elizabeth Gardner and Stephan Mertens and Annette Zippelius},
   title     = {Retrieval properties of a neural network with an asymmetric learning rule},
   journal   = {J.~Phys.~A},
   year      = {1989},
   volume    = {22},
   pages     = {2009-2018}
}

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