Brain digital twins aim to provide faithful, individualized computational representations of brains as dynamical systems, enabling mechanistic understanding and supporting prediction of clinical interventions. Yet current approaches remain fragmented across data pipelines, model classes, temporal scales, and computing platforms, which prevents the preservation of execution semantics across the end-toend workflow. This survey introduces physically constrained executability as a unifying perspective for comparing approaches at the level of execution: whether an execution state is persistent, which events are permitted to update it (simulation, measurement, actuation), and how strongly execution is temporally and causally coupled to neurobiological dynamics. Building on modeling and simulation theory, I propose a taxonomy of execution regimes ranging from isolated offline models to coordinated co-simulation, to continuously executing digital twins sustained by online data assimilation, and ultimately to neuro-neuromorphic physical systems in which biological and computational dynamics are co-executed under shared physical constraints. The executability concept clarifies why accuracy alone is insufficient, and motivates an agenda centered on semantic interoperability, hybrid-time correctness, evaluation protocols, scalable reproducible workflows, and safe closed-loop validation. This survey adopts a systems and runtime-oriented perspective, enabling comparison of heterogeneous approaches based on their execution semantics rather than on model form or application domain alone.

翻译：大脑数字孪生旨在提供忠实、个体化的脑部计算表征，将其视为动态系统，从而实现对脑功能的机制性理解并支持临床干预的预测。然而，当前的研究方法在数据管道、模型类别、时间尺度和计算平台方面仍呈现碎片化状态，这阻碍了端到端工作流中执行语义的保持。本综述引入物理约束下的可执行性作为统一视角，用于在执行层面比较不同方法：即执行状态是否持久，哪些事件允许更新该状态（模拟、测量、驱动），以及执行过程与神经生物动力学在时间与因果上的耦合强度。基于建模与仿真理论，本文提出了一套执行机制分类体系，涵盖从孤立离线模型到协同共仿真、再到由在线数据同化维持的持续执行数字孪生，直至最终在共享物理约束下生物动力学与计算动力学共执行的神经-神经形态物理系统。可执行性概念阐明了为何仅有精确性是不够的，并推动了以语义互操作性、混合时间正确性、评估协议、可扩展可复现工作流以及安全闭环验证为核心的议程。本综述采用面向系统与运行时的视角，使得基于执行语义而非模型形式或应用领域来比较异构方法成为可能。