Storing patterns in a spin-glass model of neural networks nears saturation
D. Grensing, Reiner Kühn, J. Leo van Hemmen · Journal of Physics A Mathematical and General · 1987
A Gaussian approximation for the synaptic noise and the n to 0 replica method are used to study spin-glass models of neural networks near saturation, i.e. when the number p of stored patterns increases with the size of the network N and p= alpha N. Qualitative features are predicted surprisingly well. For instance, at T=0 the linear Hopfield network provides effective associative memory with errors not exceeding 0.05% for alpha