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dc.contributor.authorWang, Wenwenen
dc.contributor.authorSubagdja, Budhitamaen
dc.contributor.authorTan, Ah-Hweeen
dc.contributor.authorStarzyk, Janusz A.en
dc.identifier.citationWang, 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.en
dc.description.abstractThis 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.en
dc.relation.ispartofseriesIEEE transactions on neural networks and learning systemsen
dc.rights© 2012 IEEEen
dc.subjectDRNTU::Engineering::Computer science and engineeringen
dc.titleNeural modeling of episodic memory : encoding, retrieval, and forgettingen
dc.typeJournal Articleen
dc.contributor.schoolSchool of Computer Engineeringen
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