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.

近年来，神经科学和人工智能的进展为理解大脑的复杂性以及计算系统对其的模拟提供了前所未有的机会。本文中，我们提出数字孪生大脑（DTB）作为一个桥梁，将生物学和人工智能连接起来。DTB 由大脑结构、底层模型和广泛应用构成。关键是，脑图谱为 DTB 内的大脑网络组织提供了重要约束。此外，我们强调了跨学科领域的联合努力所带来的开放问题，并强调了 DTB 的深远影响。DTB 能够提供对智能和神经系统疾病形成的前所未有的洞察力，这对于推进我们对生物学和人工智能的理解，进一步推动人工通用智能的发展，以及促进精确的精神卫生保健具有重大的潜力。