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

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