Artificial intelligence meets brain theory (again).

IF 1.6 4区 工程技术 Q3 COMPUTER SCIENCE, CYBERNETICS
Michael A Arbib
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引用次数: 0

Abstract

After noting the cybernetic origins of Kybernetik/ Biological Cybernetics, we respond to the Editorial by Fellous et al. (2025) and then analyze talks from the NIH BRAIN NeuroAI 2024 Workshop to get one "snapshot" of the state of the conversation between Artificial intelligence (AI) and brain theory (BT). Key recommendations going beyond the earlier Editorial are that: (i) Successes in fitting ANNs to increasingly large neuroscience datasets must not distract us from the quixotic but demanding quest to understand "how the brain works" and discover underlying brain (and AI) operating principles. (ii) We must integrate functional and structural analyses in exploring systems of systems, integrating structures (e.g., brain regions, cortical modules) and functions (e.g., schemas for perception, action and cognition) that bridge between neural circuitry and patterns of behavior. (iii) We must study the diversity of intelligences exhibited by animals in their strategies for survival and not only the disembodied employment of language and reasoning. Finally and briefly, we note the urgency of assessing the societal implications of an age of increasingly pervasive human-machine symbiosis.

人工智能与大脑理论(再次)相遇。
在注意到Kybernetik/生物控制论的控制论起源之后,我们回应了fellow等人(2025)的社论,然后分析了NIH BRAIN NeuroAI 2024研讨会的谈话,以获得人工智能(AI)和大脑理论(BT)之间对话状态的“快照”。超越之前社论的主要建议是:(i)成功地将人工神经网络与日益庞大的神经科学数据集相结合,绝不能分散我们对理解“大脑如何工作”和发现潜在的大脑(和人工智能)工作原理的堂吉诃德式但艰巨的探索的注意力。(ii)在探索系统的系统时,我们必须整合功能和结构分析,整合结构(例如,大脑区域,皮质模块)和功能(例如,感知,行动和认知的图式),它们是神经回路和行为模式之间的桥梁。(iii)我们必须研究动物在生存策略中所表现出的智力多样性,而不仅仅是语言和推理的非实体运用。最后简要地说,我们注意到评估日益普遍的人机共生时代的社会影响的紧迫性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Biological Cybernetics
Biological Cybernetics 工程技术-计算机:控制论
CiteScore
3.50
自引率
5.30%
发文量
38
审稿时长
6-12 weeks
期刊介绍: Biological Cybernetics is an interdisciplinary medium for theoretical and application-oriented aspects of information processing in organisms, including sensory, motor, cognitive, and ecological phenomena. Topics covered include: mathematical modeling of biological systems; computational, theoretical or engineering studies with relevance for understanding biological information processing; and artificial implementation of biological information processing and self-organizing principles. Under the main aspects of performance and function of systems, emphasis is laid on communication between life sciences and technical/theoretical disciplines.
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