Improved retrieval for challenging scenarios in clique-based neural networks

Abstract

This paper describes new retrieval algorithms based on heuristic approach in clique-based neural networks introduced by Gripon and Berrou. This associative memory model resembles the well-known Willshaw model with specificity of clustered structure. Several retrieval algorithms exist, for instance, Winners-Take-All and Losers-Kicked-Out. These methods work generally well when the input message suffers reasonable distortions, but the performance drops dramatically in some challenging scenarios because of severe interference. By means of simulations, we show that the proposed heuristic retrieval algorithms are able to significantly mitigate this issue while maintaining biological plausibility to some extent.