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- W2039281010 abstract "Nonlinear neural networks are studied near saturation, when the number $q$ of stored patterns is proportional to the system size $N$, i.e., $q=ensuremath{alpha}N$. The statistical mechanics is obtained for arbitrary nonlinearity. For a wide class of models, including the original Hopfield model and clipped synapses, it is shown that there exists a critical ${ensuremath{alpha}}_{c}$ above which the system looses its memory completely. Furthermore, ${ensuremath{alpha}}_{c}$ never exceeds ${ensuremath{alpha}}_{c}^{mathrm{hopfield}}$ and is determined by a universal expression. A moderate dilution of the bonds may improve the memory function." @default.
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- W2039281010 date "1987-08-01" @default.
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- W2039281010 title "Nonlinear neural networks near saturation" @default.
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- W2039281010 doi "https://doi.org/10.1103/physreva.36.1959" @default.
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