Self-organizing neural networks for universal learning and multimodal memory encoding

Self-organizing neural networks for universal learning and multimodal memory encoding

Learning and memory are two intertwined cognitive functions of the human brain. This paper shows how a family of biologically-inspired self-organizing neural networks, known as fusion Adaptive Resonance Theory (fusion ART), may provide a viable approach to realizing the learning and memory functions...

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Bibliographic Details
Main Authors: TAN, Ah-hwee, SUBAGDJA, Budhitama, WANG, Di, MENG, Lei
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2019
Subjects:
Adaptive resonance theory
Universal learning
Memory encoding
Databases and Information Systems
OS and Networks
Online Access:https://ink.library.smu.edu.sg/sis_research/5203
https://ink.library.smu.edu.sg/context/sis_research/article/6206/viewcontent/Self_organizing_neural_networks_for_universal_learning_and_multim.pdf
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Institution: Singapore Management University
Language: English

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https://ink.library.smu.edu.sg/sis_research/5203
https://ink.library.smu.edu.sg/context/sis_research/article/6206/viewcontent/Self_organizing_neural_networks_for_universal_learning_and_multim.pdf

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