期刊
FRONTIERS IN NEUROSCIENCE
卷 16, 期 -, 页码 -出版社
FRONTIERS MEDIA SA
DOI: 10.3389/fnins.2022.1096737
关键词
artificial intelligence; brain; brain-inspired intelligence; neurobiological explainability; AI evaluation; artificial neural network
资金
- Chinese National Programs for Brain Science and Brain-like Intelligence Technology
- National Natural Science Foundation of China [2021ZD0202101]
- Major Project of Philosophy and Social Science Research, Ministry of Education of China [71942003, 32161143022, 32171080, 31900766]
- CAS-VPST Silk Road Science Fund [19JZD010]
- Collaborative Innovation Program of Hefei Science Center, CAS [GLHZ202128]
- Anhui Provincial Key Research and Development Project [2020HSC-CIP001]
- [202004b11020013]
Recent advances in brain-inspired AI have shown a tipping point in the collaboration between neuroscience and AI, with AI models achieving remarkable performance without relying heavily on neuroscience. However, recent research on the neurobiological explainability of AI models suggests that these accurate models may align with the computational processes in the brain, despite being developed without explicit references to neuroscience. This perspective highlights the need for a practical framework to evaluate the brain-likeness of AI models and pave the way for further improvements.
Fueled by the development of neuroscience and artificial intelligence (AI), recent advances in the brain-inspired AI have manifested a tipping-point in the collaboration of the two fields. AI began with the inspiration of neuroscience, but has evolved to achieve a remarkable performance with little dependence upon neuroscience. However, in a recent collaboration, research into neurobiological explainability of AI models found that these highly accurate models may resemble the neurobiological representation of the same computational processes in the brain, although these models have been developed in the absence of such neuroscientific references. In this perspective, we review the cooperation and separation between neuroscience and AI, and emphasize on the current advance, that is, a new cooperation, the neurobiological explainability of AI. Under the intertwined development of the two fields, we propose a practical framework to evaluate the brain-likeness of AI models, paving the way for their further improvements.
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