The Theory of Neural Network.s: the Hebb Rule and Beyond
نویسنده
چکیده
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 ru1e (e.g., clipped networks). The properties of networks that ernp loy the pro jection method are reviewed. I: Introduction A. The HODfield i'1odel !'-1odelsof neural networks which exhibit features of associative memory have been the subject of intense theoreticalactivity.l_B Following Hopfield's work, 1 attention focused recently on networks that possess a global energy function. Assuming for simplicity a syslem of N two-state neurons, their energy function is given by
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