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Enhancing neural-network performance via assortativity

Enhancing neural-network performance via assortativity

The performance of attractor neural networks has been shown to depend crucially on the heterogeneity of the underlying topology. We take this analysis a step further by examining the effect of degree-degree correlations---assortativity---on neural-network behavior. We make use of a method recently put forward for studying correlated networks and dynamics …