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 hwich are highly correlated with the retrieved pattern, for a number of stored memories that does not exceed ${\mathrm{\alpha }}_{\mathit{a}\mathit{s}\mathit{t}}$N, where ${\mathrm{\alpha }}_{\mathit{a}\mathit{s}\mathit{t}}$∈[0,0.41] depends on the global activity level in the network and N is the number of neurons.