In recent years, advances in neuroscience and artificial intelligence have paved the way for unprecedented opportunities for understanding the complexity of the brain and its emulation by computational systems. Cutting-edge advancements in neuroscience research have revealed the intricate relationship between brain structure and function, while the success of artificial neural networks highlights the importance of network architecture. Now is the time to bring them together to better unravel how intelligence emerges from the brain's multiscale repositories. In this review, we propose the Digital Twin Brain (DTB) as a transformative platform that bridges the gap between biological and artificial intelligence. It consists of three core elements: the brain structure that is fundamental to the twinning process, bottom-layer models to generate brain functions, and its wide spectrum of applications. Crucially, brain atlases provide a vital constraint, preserving the brain's network organization within the DTB. Furthermore, we highlight open questions that invite joint efforts from interdisciplinary fields and emphasize the far-reaching implications of the DTB. The DTB can offer unprecedented insights into the emergence of intelligence and neurological disorders, which holds tremendous promise for advancing our understanding of both biological and artificial intelligence, and ultimately propelling the development of artificial general intelligence and facilitating precision mental healthcare.

翻译：近年来，神经科学与人工智能的进步为理解大脑复杂性及通过计算系统模拟大脑开辟了前所未有的机遇。神经科学研究的尖端进展揭示了大脑结构与功能之间的复杂关系，而人工神经网络的成功则凸显了网络架构的重要性。当下正是将二者融合，以更深入探索智能如何从大脑的多尺度存储库中涌现的时机。在本综述中，我们提出了数字孪生大脑（DTB）作为连接生物智能与人工智能的变革性平台。它包含三个核心要素：作为孪生过程基础的脑结构、用于生成脑功能的底层模型，以及广泛的应用领域。关键在于，脑图谱提供了至关重要的约束，在DTB中保留了大脑的网络组织。此外，我们强调了需要跨学科领域联合攻关的开放问题，并着重阐述了DTB的深远影响。DTB能够为智能涌现和神经系统疾病提供前所未有的洞见，有望极大促进我们对生物智能与人工智能的理解，并最终推动通用人工智能的发展，助力精准心理健康护理。