Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
8-2017
Abstract
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 stateof-the-art associative memory model.
Keywords
Machine Learning, Neural Networks, Multidisciplinary Topics and Applications, Cognitive Modeling
Discipline
Databases and Information Systems
Research Areas
Data Science and Engineering
Publication
Proceedings of the 26th International Joint Conference on Artificial Intelligence: IJCAI'17, Melbourne, Australia, 2017 August 19-25
First Page
1490
Last Page
1496
Identifier
10.24963/ijcai.2017/206
Publisher
ACM
City or Country
Melbourne, Australia
Citation
1
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.