A self-organizing approach to episodic memory modeling
This paper presents a neural model that learns episodic traces in response to a continual stream of sensory input and feedback received from the environment. The proposed model, based on fusion Adaptive Resonance Theory (fusion ART) network, extracts key events and encodes spatiotemporal relations b...
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| Main Authors: | , , |
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| Format: | text |
| Language: | English |
| Published: |
Institutional Knowledge at Singapore Management University
2010
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| Subjects: | |
| Online Access: | https://ink.library.smu.edu.sg/sis_research/6164 https://ink.library.smu.edu.sg/context/sis_research/article/7167/viewcontent/Episodic20Memory20IJCNN202010.pdf |
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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | This paper presents a neural model that learns episodic traces in response to a continual stream of sensory input and feedback received from the environment. The proposed model, based on fusion Adaptive Resonance Theory (fusion ART) network, extracts key events and encodes spatiotemporal relations between events by creating cognitive nodes dynamically. The model further incorporates a novel memory search procedure, which performs parallel search of stored episodic traces continuously. Comparing with prior systems, the proposed episodic memory model presents a robust approach to encoding key events and episodes and recalling them using partial and erroneous cues. We present experimental studies, wherein the model is used to learn episodic memory of an agent’s experience in a first person game environment called Unreal Tournament. Our experimental results show that the model produces highly robust performance in encoding and recalling events and episodes even with incomplete and noisy cues. |
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