CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
A Two-Layer Self-Organizing Map with Vector Symbolic Architecture for Spatiotemporal Sequence Learning and Prediction
Centre for Data Analytics and Cognition, La Trobe University, Melbourne, VIC, 3086, Australia.ORCID iD: 0000-0001-6294-0004
Centre for Data Analytics and Cognition, La Trobe University, Melbourne, VIC, 3086, Australia.ORCID iD: 0000-0003-3291-888X
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science. Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, Luleå, 971 87, Sweden.ORCID iD: 0000-0003-0069-640X
Centre for Data Analytics and Cognition, La Trobe University, Melbourne, VIC, 3086, Australia.
Show others and affiliations
2024 (English)In: Biomimetics, E-ISSN 2313-7673, Vol. 9, no 3, article id 175Article in journal (Refereed) Published
Abstract [en]

We propose a new nature- and neuro-science-inspired algorithm for spatiotemporal learning and prediction based on sequential recall and vector symbolic architecture. A key novelty is the learning of spatial and temporal patterns as decoupled concepts where the temporal pattern sequences are constructed using the learned spatial patterns as an alphabet of elements. The decoupling, motivated by cognitive neuroscience research, provides the flexibility for fast and adaptive learning with dynamic changes to data and concept drift and as such is better suited for real-time learning and prediction. The algorithm further addresses several key computational requirements for predicting the next occurrences based on real-life spatiotemporal data, which have been found to be challenging with current state-of-the-art algorithms. Firstly, spatial and temporal patterns are detected using unsupervised learning from unlabeled data streams in changing environments; secondly, vector symbolic architecture (VSA) is used to manage variable-length sequences; and thirdly, hyper dimensional (HD) computing-based associative memory is used to facilitate the continuous prediction of the next occurrences in sequential patterns. The algorithm has been empirically evaluated using two benchmark and three time-series datasets to demonstrate its advantages compared to the state-of-the-art in spatiotemporal unsupervised sequence learning where the proposed ST-SOM algorithm is able to achieve 45% error reduction compared to HTM algorithm.

Place, publisher, year, edition, pages
Multidisciplinary Digital Publishing Institute (MDPI) , 2024. Vol. 9, no 3, article id 175
Keywords [en]
hierarchical temporal memory, self-organizing maps, spatiotemporal sequence learning, vector symbolic architectures
National Category
Computer Sciences
Research subject
Dependable Communication and Computation Systems
Identifiers
URN: urn:nbn:se:ltu:diva-104938DOI: 10.3390/biomimetics9030175Scopus ID: 2-s2.0-85188743995OAI: oai:DiVA.org:ltu-104938DiVA, id: diva2:1848009
Note

Validerad;2024;NIvå 2;2024-04-02 (marisr);

Full text license: CC BY

Available from: 2024-04-02 Created: 2024-04-02 Last updated: 2024-04-02Bibliographically approved

Open Access in DiVA

fulltext(2530 kB)13 downloads
File information
File name FULLTEXT01.pdfFile size 2530 kBChecksum SHA-512
a8dae152a2ed53cb254358040e10c52d933065920b923411137bd86048627de8d3daf8e1c7acafb8cb20cf7bc448fd081e2caa900a00fef34733a4865146edfd
Type fulltextMimetype application/pdf

Other links

Publisher's full textScopus

Authority records

Osipov, Evgeny

Search in DiVA

By author/editor
Kempitiya, ThimalAlahakoon, DammindaOsipov, EvgenyDe Silva, Daswin
By organisation
Computer Science
Computer Sciences

Search outside of DiVA

GoogleGoogle Scholar
Total: 13 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 194 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf