Encoding and Recall of Spatio-Temporal Episodic Memory in Real Time
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 state-of-the-art associative memory model.
Cite
Text
Chang and Tan. "Encoding and Recall of Spatio-Temporal Episodic Memory in Real Time." International Joint Conference on Artificial Intelligence, 2017. doi:10.24963/IJCAI.2017/206Markdown
[Chang and Tan. "Encoding and Recall of Spatio-Temporal Episodic Memory in Real Time." International Joint Conference on Artificial Intelligence, 2017.](https://mlanthology.org/ijcai/2017/chang2017ijcai-encoding/) doi:10.24963/IJCAI.2017/206BibTeX
@inproceedings{chang2017ijcai-encoding,
title = {{Encoding and Recall of Spatio-Temporal Episodic Memory in Real Time}},
author = {Chang, Poo-Hee and Tan, Ah-Hwee},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2017},
pages = {1490-1496},
doi = {10.24963/IJCAI.2017/206},
url = {https://mlanthology.org/ijcai/2017/chang2017ijcai-encoding/}
}