用神经科学启发AI，解决现实交互、学习脆弱和能耗高的问题
NeuroAI and Beyond: Bridging Between Advances in Neuroscience and ArtificialIntelligence
- 从神经科学提取五项核心原理：体控协同、预测性交互、多尺度学习等
- 提出近中长期结合的研究路线图，推动可感知、高效、鲁棒的AI系统
- 呼吁跨学科人才培养与硬件/伦理支持，促进建立新型科研生态
近年来，神经科学与人工智能（AI）虽取得显著进展，但二者仍关联松散。基于2025年8月美国国家科学基金会组织的研讨会，我们识别出当前AI的三大能力缺口：无法有效与物理世界交互、学习机制导致系统脆弱、能源与数据效率不可持续。针对这些问题，本文提出神经科学中的五大原则：体控协同设计、通过交互进行预测、具有神经调节控制的多尺度学习、分层分布式架构以及稀疏事件驱动计算。据此构建了涵盖短期、中期与长期目标的研究路线图。我们认为，实现该计划需培养一批横跨神经科学与工程领域的新生代研究者，并建立相应的制度条件：跨学科训练体系、硬件资源获取、社区标准规范及伦理框架。最终指出，神经科学赋能的人工智能（NeuroAI）不仅能突破现有AI局限，还可深化对生物神经计算的理解。
原文摘要 · Abstract (English)
Neuroscience and Artificial Intelligence (AI) have made impressive progress in recent years but remain only loosely interconnected. Based on a workshop convened by the National Science Foundation in August 2025, we identify three fundamental capability gaps in current AI: the inability to interact with the physical world, inadequate learning that produces brittle systems, and unsustainable energy and data inefficiency. We describe the neuroscience principles that address each: co-design of body and controller, prediction through interaction, multi-scale learning with neuromodulatory control, hierarchical distributed architectures, and sparse event-driven computation. We present a research roadmap organized around these principles at near, mid, and long-term horizons. We argue that realizing this program requires a new generation of researchers trained across the boundary between neuroscience and engineering, and describe the institutional conditions: interdisciplinary training, hardware access, community standards, and ethics, needed to support them. We conclude that NeuroAI, neuroscience-informed artificial intelligence, has the potential to overcome limitations of current AI while deepening our understanding of biological neural computation.
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