CoChat
Sign Up
Cited by 40Neural Networks and ApplicationsCognitive Computing and NetworksMemory and Neural Mechanisms

Self-organizing neural networks for universal learning and multimodal memory encoding

Tan, Ah-Hwee, Subagdja, Budhitama, Wang, Di, Meng, Lei·2019·Neural Networks
DOI 10.1016/j.neunet.2019.08.020
Open this paper in CoChat — free account

Abstract

Learning and memory are two intertwined cognitive functions of the human brain. This paper shows how a family of biologically-inspired self-organizing neural networks, known as fusion Adaptive Resonance Theory (fusion ART), may provide a viable approach to realizing the learning and memory functions. Fusion ART extends the single-channel Adaptive Resonance Theory (ART) model to learn multimodal pattern associative mappings. As a natural extension of ART, various forms of fusion ART have been developed for a myriad of learning paradigms, ranging from unsupervised learning to supervised learning, semi-supervised learning, multimodal learning, reinforcement learning, and sequence learning. In addition, fusion ART models may be used for representing various types of memories, notably episodic memory, semantic memory and procedural memory. In accordance with the notion of embodied intelligence, such neural models thus provide a computational account of how an autonomous agent may learn and adapt in a real-world environment. The efficacy of fusion ART in learning and memory shall be discussed through various examples and illustrative case studies.

Integrity signals

Registry

Crossref · 10.1016/j.neunet.2019.08.020

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Fusion ART extends Adaptive Resonance Theory by integrating multiple information channels through complementary learning, allowing simultaneous processing of heterogeneous data types within a unified resonance-based system.

    “Fusion ART (Tan, Carpenter, and Grossberg 2007; Tan et al. 2019) extends this framework by integrating multiple information channels through complementary learning, allowing simultaneous processing of heterogeneous data types within a unified resonance-based system.”

    From ARTEM: Enhancing Large Language Model Agents with Spatial-Temporal Episodic Memory (2026) · cites this paper as Tan et al. 2019

Cites

Flagged first, then by citations

Research works printed but not linked: 35

  1. Berntsen, 2002, Emotionally charged autobiographical memories across the life span: The recall of happy, sad, traumatic, and involuntary memories, Psychology and Aging, 17, 636, 10.1037/0882-7974.17.4.636 · DOI · Paper page
  2. Bluck, 1998, Reminiscence as autobiographical memory: A catalyst for reminiscence theory development, Ageing & Society, 18, 185, 10.1017/S0144686X98006862 · DOI
  3. Carpenter, 1987, ART 2: Self-organization of stable category recognition codes for analog input patterns, Applied Optics, 26, 4919, 10.1364/AO.26.004919 · DOI
  4. Carpenter, 1992, A self-organizing neural network for supervised learning, recognition and prediction, IEEE Communications Magazine, 30, 38, 10.1109/35.156802 · DOI
  5. Carpenter, 1992, Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps, IEEE Transactions on Neural Networks, 3, 698, 10.1109/72.159059 · DOI
  6. Carpenter, 1991, ARTMAP: Supervised real time learning and classification by a self-organizing neural network, Neural Networks, 4, 565, 10.1016/0893-6080(91)90012-T · DOI
  7. Carpenter, 1991, ART 2-a: Fast stable learning and categorization of analog patterns by an adaptive resonance system, Neural Networks, 4, 493, 10.1016/0893-6080(91)90045-7 · DOI
  8. Carpenter, 1991, Fuzzy ART: Fast stable learning and categorization of analog patterns by an adaptive resonance system, Neural Networks, 4, 759, 10.1016/0893-6080(91)90056-B · DOI
  9. Conway, 2000, The construction of autobiographical memories in the self-memory system, Psychological Review, 107, 261, 10.1037/0033-295X.107.2.261 · DOI · Paper page
  10. Feng, 2016, Towards autonomous behavior learning of non-player characters in games, Expert Systems with Applications, 56, 89, 10.1016/j.eswa.2016.02.043 · DOI
  11. Grossberg, 1976, Adaptive pattern recognition and universal recoding. I: Parallel development and coding of neural feature detectors, Biological Cybernetics, 23, 121, 10.1007/BF00344744 · DOI
  12. Grossberg, 1976, Adaptive pattern recognition and universal recoding. II: Feedback, expectation, olfaction, and illusion, Biological Cybernetics, 23, 187, 10.1007/BF00340335 · DOI
  13. Grossberg, 1978, Behavioral contrast in short-term memory: Serial binary memory models or parallel continuous memory models?, Journal of Mathematical Psychology, 3, 199, 10.1016/0022-2496(78)90016-0 · DOI
  14. Grossberg, 2003, How does the cerebral cortex work? development, learning, attention, and 3d vision by laminar circuits of visual cortex, Behavioral and Cognitive Neuroscience Reviews, 2, 47, 10.1177/1534582303002001003 · DOI
  15. He, 2003, On machine learning methods for chinese documents classification, Applied Intelligence: Special Issue on Text and Web Mining, 18, 311, 10.1023/A:1023202221875 · DOI
  16. Healy, 1993, A neural architecture for pattern sequence verification through inferencing, IEEE Transactions on Neural Networks, 4, 9, 10.1109/72.182691 · DOI
  17. Meng, 2014, Semi-supervised heterogeneous fusion for multimedia data co-clustering, IEEE Transactions on Knowledge and Data Engineering, 26, 2293, 10.1109/TKDE.2013.47 · DOI
  18. Park, 2018, Deep ART neural model for biologically inspired episodic memory and its application to task performance of robots, IEEE Transactions on Cybernetics, 48, 1786, 10.1109/TCYB.2017.2715338 · DOI
  19. Raizada, 2003, Towards a theory of the laminar architecture of cerebral cortex: Computational clues from the visual system, Cerebral Cortex, 13, 200, 10.1093/cercor/13.1.100 · DOI
  20. Samsonovich, 2005, A simple neural network model of the hippocampus suggesting its pathfinding role in episodic memory retrieval, Learning and Memory, 12, 193, 10.1101/lm.85205 · DOI
  21. Starzyk, 2009, Spatio-temporal memories for machine learning: A long-term memory organization, IEEE Transactions on Neural Networks, 20, 768, 10.1109/TNN.2009.2012854 · DOI
  22. Subagdja, 2012, IFALCON: A neural architecture for hierarchical planning, Neurocomputing, 86, 124, 10.1016/j.neucom.2012.01.008 · DOI
  23. Subagdja, 2015, Neural modeling of sequential inferences and learning over episodic memory, Neurocomputing, 161, 229, 10.1016/j.neucom.2015.02.038 · DOI
  24. Tan, 1995, Adaptive resonance associative map, Neural Networks, 8, 437, 10.1016/0893-6080(94)00092-Z · DOI
  25. Tan, 1997, CaScade ARTMAP:integrating neural computation and symbolic knowledge processing, IEEE Transactions on Neural Networks, 8, 237, 10.1109/72.557661 · DOI
  26. Tan, 2008, Integrating temporal difference methods and self-organizing neural networks for reinforcement learning with delayed evaluative feedback, IEEE Transactions on Neural Networks, 19, 230, 10.1109/TNN.2007.905839 · DOI
  27. Tan, 2004, Towards personalized web intelligence, Knowledge and Information Systems, 6, 595, 10.1007/s10115-003-0130-9 · DOI
  28. Tan, 2005, Predictive neural networks for gene expression data analysis, Neural Networks, 18, 297, 10.1016/j.neunet.2005.01.003 · DOI
  29. Teng, 2013, Adaptive computer-generated forces for simulator-based training, Expert Systems with Applications, 40, 7341, 10.1016/j.eswa.2013.07.004 · DOI
  30. Teng, 2015, Self-organizing neural networks integrating domain knowledge and reinforcement learning, IEEE Transactions on Neural Networks and Learning Systems, 26, 889, 10.1109/TNNLS.2014.2327636 · DOI
  31. Teyler, 1986, The hippocampal memory indexing theory, Behavioral Neuroscience, 100, 147, 10.1037/0735-7044.100.2.147 · DOI · Paper page
  32. Tulving, 1985, How many memory systems are there?, American Psychologist, 40, 385, 10.1037/0003-066X.40.4.385 · DOI · Paper page
  33. Wang, 2012, Neural modeling of episodic memory: Encoding, retrieval, and forgetting, IEEE Transactions on Neural Networks and Learning Systems, 23, 1574, 10.1109/TNNLS.2012.2208477 · DOI
  34. Wang, 2015, Creating autonomous adaptive agents in a real-time first-person shooter computer game, IEEE Transactions on Computational Intelligence and AI in Games, 7, 123, 10.1109/TCIAIG.2014.2336702 · DOI
  35. Wang, 2017, Semantic memory modeling and memory interaction in learning agents, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 47, 2882, 10.1109/TSMC.2016.2531683 · DOI · Paper page

Cited by

Sorted by citations

Authors
Tan, A.-H., Subagdja, B., Wang, D., & Meng, L.
Affiliations
  • Nanyang Technological University (SG)
  • National University of Singapore (SG)
Corresponding author
Tan, Ah-Hwee
ORCID
Venue
Neural Networks
Publisher
Elsevier BV
Published
2019-12
Volume
120
Pages
58-73
ISSN
0893-6080 (ISSN-L)
DOI
10.1016/j.neunet.2019.08.020
OpenAlex
W2971898177
Cited by
40
References
1
Citations by year
2026: 4 · 2025: 7 · 2024: 4 · 2023: 5 · 2022: 6 · 2021: 6 · 2020: 6 · 2019: 2
FWCI
2.28
Type
journal article
Language
en
Open access
closed
Licence
Locations
  • Neural Networks (publishedVersion)
  • PubMed (publishedVersion)
  • Singapore Management University Institutional Knowledge (InK) (Singapore Management University) (submittedVersion)
Funders
  • Ministry of Health -Singapore (MOH/NIC/COG04/2017)
  • National Research Foundation Singapore
  • DSO National Laboratories - Singapore (DSOCL11258, DSOCL16006)
  • Ministry of Education - Singapore (RG130/18)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
  • Physical Sciences › Computer Science › Artificial Intelligence › Cognitive Computing and Networks
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
Keywords
self-organizing neural networks, associative memory, episodic memory, semantic memory, procedural memory, embodied AI
MeSH terms
Machine Learning, Mathematical Concepts

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review