The Theory of Neural Networks: The Hebb Rule and Beyond

Recent studies of the statistical mechanics of neural network models of associative memory are reviewed. The paper discusses models which have an energy function but depart from the simple Hebb rule. This includes networks with static synaptic noise, dilute networks and synapses that are nonlinear functions of the Hebb rule (e.g., clipped networks). The properties of networks that employ the projection method are reviewed.

Authors: H. Sompolinsky
Year of publication: 1987
Journal: Heidelberg Colloquium on Glassy Dynamics pp 485-527

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“Working memory”