Recent breakthroughs in artificial intelligence (AI) are reshaping the way we construct computational counterparts of the brain, giving rise to a new class of ``surrogate brains''. In contrast to conventional hypothesis-driven biophysical models, the AI-based surrogate brain encompasses a broad spectrum of data-driven approaches to solve the inverse problem, with the primary objective of accurately predicting future whole-brain dynamics with historical data. Here, we introduce a unified framework of constructing an AI-based surrogate brain that integrates forward modeling, inverse problem solving, and model evaluation. Leveraging the expressive power of AI models and large-scale brain data, surrogate brains open a new window for decoding neural systems and forecasting complex dynamics with high dimensionality, nonlinearity, and adaptability. We highlight that the learned surrogate brain serves as a simulation platform for dynamical systems analysis, virtual perturbation, and model-guided neurostimulation. We envision that the AI-based surrogate brain will provide a functional bridge between theoretical neuroscience and translational neuroengineering.

翻译：近年来人工智能（AI）领域的突破正在重塑我们构建大脑计算对应体的方式，催生出一类新型的“替代大脑”。与传统的假设驱动型生物物理模型不同，基于AI的替代大脑涵盖了一系列数据驱动方法来解决逆问题，其主要目标是通过历史数据准确预测未来全脑动力学。本文提出了构建基于AI的替代大脑的统一框架，该框架整合了前向建模、逆问题求解和模型评估。借助AI模型的表达能力和大规模脑数据，替代大脑为解码神经系统及预测具有高维度、非线性和适应性特征的复杂动力学开启了新窗口。我们强调，学习得到的替代大脑可作为动力学系统分析、虚拟扰动和模型引导神经调控的仿真平台。我们展望基于AI的替代大脑将在理论神经科学与转化神经工程之间架起功能性的桥梁。