Using graph theoretic measures to predict the performance of associative memory models
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Author
Calcraft, L.
Adams, R.G.
Chen, W.
Davey, N.
Attention
2299/2415
Abstract
We test a selection of associative memory models built with different connection strategies, exploring the relationship between the structural properties of each network and its pattern-completion performance. It is found that the Local Efficiency of the network can be used to predict pattern completion performance for associative memory models built with a range of different connection strategies. This relationship is maintained as the networks are scaled up in size, but breaks down under conditions of very sparse connectivity.