dc.contributor.author | Abolpour Mofrad, Asieh | |
dc.contributor.author | Abolpour Mofrad, Samaneh | |
dc.contributor.author | Yazidi, Anis | |
dc.contributor.author | Parker, Matthew Geoffrey | |
dc.date.accessioned | 2022-12-06T10:16:33Z | |
dc.date.available | 2022-12-06T10:16:33Z | |
dc.date.created | 2021-09-10T15:59:18Z | |
dc.date.issued | 2021-08-19 | |
dc.identifier.citation | Neural Computation. 2021, 33 (9), 2550-2577. | en_US |
dc.identifier.issn | 0899-7667 | |
dc.identifier.issn | 1530-888X | |
dc.identifier.uri | https://hdl.handle.net/11250/3036061 | |
dc.description.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. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | MIT Press | en_US |
dc.relation.ispartofseries | Neural Computation;Volume 33, Issue 9 | |
dc.subject | Auto-associative memories | en_US |
dc.subject | Clique-based neural networks | en_US |
dc.subject | Tournament-based neural networks | en_US |
dc.subject | Sequence storage | en_US |
dc.title | On Neural Associative Memory Structures: Storage and Retrieval of Sequences in a Chain of Tournaments | en_US |
dc.type | Peer reviewed | en_US |
dc.type | Journal article | en_US |
dc.description.version | acceptedVersion | en_US |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.fulltext | postprint | |
cristin.qualitycode | 2 | |
dc.identifier.doi | https://doi.org/10.1162/neco_a_01417 | |
dc.identifier.cristin | 1933339 | |
dc.source.journal | Neural Computation | en_US |
dc.source.volume | 33 | en_US |
dc.source.issue | 9 | en_US |
dc.source.pagenumber | 2550-2577 | en_US |