Digital twin brain: a bridge between biological intelligence and artificial intelligence

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. 1 Introduction Demystifying the principles that account for human intelligent behaviors, such as recognizing faces and making decisions, has been attracting a tremendous amount of interdisciplinary effort and is also the driving force behind the boom in artificial intelligence. The closer we can approach the intrinsicality of intelligence, the higher the possibility that we could master the emergence of intelligence. As the biological recesses of intelligent behaviors, the multiscale characteristics of the human brain are specifically being identified to explain the remarkable neurobiological basis underlying intelligent abilities.