On Neural Associative Memory Structures: Storage and Retrieval of Sequences in a Chain of Tournaments

Abstract

Associative memories enjoy many interesting properties in terms of error correction capabilities, robustness to noise, storage capacity and retrieval performance and their usage spans over a large set of applications. In this article, we investigate and extend Tournament-Based Neural Networks, originally proposed by Jiang et al. (2016), which is a novel sequence storage associative memory architecture with high memory efficiency and accurate sequence retrieval. We propose a more general method for learning the sequences which we call Feedback Tournament-Based Neural Networks. The retrieval process is also extended to both directions: forward and backward, i.e. any large-enough segment of a sequence can produce the whole sequence. Furthermore, two retrieval algorithms, Cache-Winner and Explore-Winner are introduced to increase the retrieval performance. Through simulation results, we shed light on the strengths and weaknesses of each algorithm.