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

• 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
• Institution: Singapore Management University

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...