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

arXiv: https://arxiv.org/abs/2308.01941 · HTML: https://mlsanity.com/paper/2308.01941/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 155 references that are in the map.
- First posted on arXiv 3 Aug 2023.

## Builds on (most cited first, 8 of 8)

- [Adversarial Examples: Attacks and Defenses for Deep Learning](https://mlsanity.com/paper/1712.07107/) (2017)
- [BrainCog: A Spiking Neural Network based Brain-inspired Cognitive Intelligence Engine for Brain-inspired AI and Brain Simulation](https://mlsanity.com/paper/2207.08533/) (2022)
- [The physics of brain network structure, function, and control](https://mlsanity.com/paper/1809.06441/) (2018)
- [Predicting the spatiotemporal diversity of seizure propagation and termination in human focal epilepsy](https://mlsanity.com/paper/1707.00772/) (2017)
- [The scientific case for brain simulations](https://mlsanity.com/paper/1906.06189/) (2019)
- [Personalized brain network models for assessing structure-function relationships](https://mlsanity.com/paper/1802.00473/) (2018)
- [Review on Biophysical Modelling and Simulation Studies for Transcranial Magnetic Stimulation](https://mlsanity.com/paper/2007.00469/) (2020)
- [Applications of optimal nonlinear control to a whole-brain network of FitzHugh-Nagumo oscillators](https://mlsanity.com/paper/2102.08524/) (2021)

## Built on by (most cited first, 1 of 1)

- [Brain Foundation Models: A Survey on Advancements in Neural Signal Processing and Brain Discovery](https://mlsanity.com/paper/2503.00580/) (2025)

## Abstract (arXiv)

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.