Runhe HUANG

Name
Runhe HUANG
Major
Computer and Information Sciences
Lab.
AI Lab.
Research Fields
Cognitive Computing, Ubiquitous Intelligence, Machine Learning, Human Brain and Memory Modeling
Keywords
Cognitive learning, Machine Intelligence, Associative Memory Modeling, Smart Home
URL
https://rhuang.cis.k.hosei.ac.jp/
 It is observed that some people are good at answering questions in exams or finding solutions to problems at work, however, some are not. The differences are individuals’ learnt knowledge and experience, how effectively they are associated and how efficiently they are recalled. The process by which we remember facts and events has fascinated philosophers, neuroscientists, psychologists, and biologists for millennia. Some evidences and experimental observations from neuroscience have proven our human brain can perform memory consolidation at system level (explicit memory storage in the hippocampus where learning-related changes occur) and cellular level (distributed memory trace in the neocortex where learning induces plasticity-related molecular changes gradually). The scientific questions are ① how learning and memorizing functionality can be unified in a computational model? ② how learning and memorizing functions can be intertwisted and controlled in a time-course for memory’s effectiveness and efficiency?③ how learned knowledge is recalled for a better efficiency? To these questions, the goal of this research is to seek for a simple and unified computational model for memory in which both learning and memorizing can be conducted and knowledge recall can be done effectively and efficiently.
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