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

Abolpour Mofrad, Asieh; Abolpour Mofrad, Samaneh; Yazidi, Anis; Parker, Matthew Geoffrey
Journal article, Peer reviewed
Published version
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URI
https://hdl.handle.net/11250/2992618
Date
2021
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  • Department of Informatics [881]
  • Registrations from Cristin [8666]
Original version
Neural Computation. 2021, 33 (9), 2550-2577.   10.1162/neco_a_01417
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 letter, we investigate and extend tournament-based neural networks, originally proposed by Jiang, Gripon, Berrou, and Rabbat (2016), 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—in other words, 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.
Publisher
MIT Press
Journal
Neural Computation
Copyright
Copyright 2021 Massachusetts Institute of Technology

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