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Response Functions Improving Performance in Analog Attractor Neural Networks
In the context of attractor neural networks, we study how the equilibrium
analog neural activities, reached by the network dynamics during memory
retrieval, may improve storage performance by reducing the interferences
between the recalled pattern and the other stored ones. We determine a simple
dynamics that stabilizes network states which are highly correlated with the
retrieved pattern, for a number of stored memories that does not exceed
, where depends on the global
activity level in the network and is the number of neurons.Comment: 13 pages (with figures), LaTex (RevTex), to appear on Phys.Rev.E (RC
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