Using graph-theoretic measures to predict the performance of associative memory models

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.

• Type: Conference
• Year: 2008
• Where: ESANN
• Authors: Lee Calcraft, Rod Adams, Weiliang Chen, Neil Davey
• Keywords: Associative Memory Models, ESANN 2008, Neural Networks, Pattern Completion Performance, Pattern-completion Performance