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Encoding and Recall of Spatio-Temporal Episodic Memory in Real Time
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Episodic memory enables a cognitive system to improve its performance by reflecting upon past events. In this paper, we propose a computational model called STEM for encoding and recall of episodic events together with the associated contextual information in real time. Based on a class of self-organizing neural networks, STEM is designed to learn memory chunks or cognitive nodes, each encoding a set of co-occurring multi-modal activity patterns across multiple pattern channels. We present algorithms for recall of events based on partial and inexact input patterns. Our empirical results based on a public domain data set show that STEM displays a high level of efficiency and robustness in encoding and retrieval with both partial and noisy search cues when compared with a state-of-the-art associative memory model.
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The Spatial-Temporal Episodic Memory model adapts fusion ART's multi-channel capabilities for episodic memory representation, incorporating temporal, spatial, entity-based, and content-based encoding channels.
“Building upon fusion ART, the Spatial-Temporal Episodic Memory (STEM) model (Chang and Tan 2017) specifically adapts these multi-channel capabilities for episodic memory representation, incorporating temporal, spatial, entity-based, and content-based encoding channels to capture the multifaceted nature of event sequences.”
From ARTEM: Enhancing Large Language Model Agents with Spatial-Temporal Episodic Memory (2026) · cites this paper as Chang and Tan 2017
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Enhancing intelligent agents with episodic memory
Nuxoll, A. M., & Laird, J. E. (2012) · Cognitive Systems Research · doi:10.1016/j.cogsys.2011.10.002 · Cited by 99
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ARTEM: Enhancing Large Language Model Agents with Spatial-Temporal Episodic Memory
Tan, C. H.-M., Subagdja, B., & Tan, A.-H. (2026) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v40i30.39773
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Metadata
- Authors
- Chang, P.-H., & Tan, A.-H.
- Affiliations
- Nanyang Technological University (SG)
- Corresponding author
- Chang, Poo-Hee
- ORCID
- Tan, Ah-Hwee
- Venue
- Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
- Published
- 2017-08
- Pages
- 1490-1496
- OpenAlex
- W2740574988
- Cited by
- 13
- References
- 1
- Citations by year
- 2026: 1 · 2025: 1 · 2023: 3 · 2022: 1 · 2021: 3 · 2020: 1 · 2019: 2 · 2018: 1
- FWCI
- 0.50
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- public-domain
- Locations
- International Joint Conference on Artificial Intelligence (publishedVersion, public-domain)
- Singapore Management University Institutional Knowledge (InK) (Singapore Management University) (submittedVersion, other-oa)
- 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
- episodic memory, spatiotemporal memory, memory encoding, memory retrieval, self-organizing neural networks, contextual information, associative memory
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