Encoding and recall of spatio-temporal episodic memory in real time

• Main Authors: CHANG, Poo-Hee, TAN, Ah-hwee
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2017
• Subjects: Machine Learning; Neural Networks; Multidisciplinary Topics and Applications; Cognitive Modeling; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/6082; https://ink.library.smu.edu.sg/context/sis_research/article/7085/viewcontent/Encoding_and_Recall_of_Spatio_Temporal_Episodic_Memory_in_Real_Time.pdf
• Institution: Singapore Management University

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.