Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/99738
Title: Neural modeling of episodic memory : encoding, retrieval, and forgetting
Authors: Wang, Wenwen
Subagdja, Budhitama
Tan, Ah-Hwee
Starzyk, Janusz A.
Keywords: DRNTU::Engineering::Computer science and engineering
Issue Date: 2012
Source: Wang, W., Subagdja, B., Tan, A. H., & Starzyk, J. A. (2012). Neural modeling of episodic memory : encoding, retrieval, and forgetting. IEEE transactions on neural networks and learning systems, 23(10), 1574-1586.
Series/Report no.: IEEE transactions on neural networks and learning systems
Abstract: This paper presents a neural model that learns episodic traces in response to a continuous stream of sensory input and feedback received from the environment. The proposed model, based on fusion adaptive resonance theory (ART) network, extracts key events and encodes spatio-temporal relations between events by creating cognitive nodes dynamically. The model further incorporates a novel memory search procedure, which performs a continuous parallel search of stored episodic traces. Combined with a mechanism of gradual forgetting, the model is able to achieve a high level of memory performance and robustness, while controlling memory consumption over time. We present experimental studies, where the proposed episodic memory model is evaluated based on the memory consumption for encoding events and episodes as well as recall accuracy using partial and erroneous cues. Our experimental results show that: 1) the model produces highly robust performance in encoding and recalling events and episodes even with incomplete and noisy cues; 2) the model provides enhanced performance in a noisy environment due to the process of forgetting; and 3) compared with prior models of spatio-temporal memory, our model shows a higher tolerance toward noise and errors in the retrieval cues.
URI: https://hdl.handle.net/10356/99738
http://hdl.handle.net/10220/13533
ISSN: 2162-237X
DOI: http://dx.doi.org/10.1109/TNNLS.2012.2208477
Rights: © 2012 IEEE
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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