Never-Ending Learning for Explainable Brain Computing
Article
Kuai, Hongzhi, Chen, Jianhui, Tao, Xiaohui, Cai, Lingyun, Imamura, Kazuyuki, Matsumoto, Hiroki, Liang, Peipeng and Zhong, Ning. 2024. "Never-Ending Learning for Explainable Brain Computing." Advanced Science. 11 (24). https://doi.org/10.1002/advs.202307647
Article Title | Never-Ending Learning for Explainable Brain Computing |
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ERA Journal ID | 210044 |
Article Category | Article |
Authors | Kuai, Hongzhi, Chen, Jianhui, Tao, Xiaohui, Cai, Lingyun, Imamura, Kazuyuki, Matsumoto, Hiroki, Liang, Peipeng and Zhong, Ning |
Journal Title | Advanced Science |
Journal Citation | 11 (24) |
Article Number | 2307647 |
Number of Pages | 18 |
Year | 2024 |
Publisher | John Wiley & Sons |
ISSN | 2198-3844 |
Digital Object Identifier (DOI) | https://doi.org/10.1002/advs.202307647 |
Web Address (URL) | https://onlinelibrary.wiley.com/doi/10.1002/advs.202307647 |
Abstract | Exploring the nature of human intelligence and behavior is a longstanding pursuit in cognitive neuroscience, driven by the accumulation of knowledge, information, and data across various studies. However, achieving a unified and transparent interpretation of findings presents formidable challenges. In response, an explainable brain computing framework is proposed that employs the never-ending learning paradigm, integrating evidence combination and fusion computing within a Knowledge-Information-Data (KID) architecture. The framework supports continuous brain cognition investigation, utilizing joint knowledge-driven forward inference and data-driven reverse inference, bolstered by the pre-trained language modeling techniques and the human-in-the-loop mechanisms. In particular, it incorporates internal evidence learning through multi-task functional neuroimaging analyses and external evidence learning via topic modeling of published neuroimaging studies, all of which involve human interactions at different stages. Based on two case studies, the intricate uncertainty surrounding brain localization in human reasoning is revealed. The present study also highlights the potential of systematization to advance explainable brain computing, offering a finer-grained understanding of brain activity patterns related to human intelligence. |
Keywords | evidence combination and fusion computing |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 461103. Deep learning |
Byline Affiliations | Maebashi Institute of Technology, Japan |
Capital Normal University, China | |
Beijing University of Technology, China | |
Beijing International Collaboration Base on Brain Informatics and Wisdom Services, China | |
School of Mathematics, Physics and Computing |
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