AI 中文总结
本综述梳理受体感知全脑模型的数学谱系，明确其约化假设，将其置于约化群体模型格局中，引入模拟成本与内存流量作为基准维度，界定该建模为多属性间的权衡。
AI 中文摘要
许多药理和病理机制作用于分子、突触或细胞尺度，而所关注的现象往往在皮层群体和全脑记录层面被测量。这种尺度差距催生了约化模型，这类模型既保持生物学可解释性，又足够易处理，可用于全脑模拟、参数探索及与经验信号的比较。本综述以Sacha等人（2025）提出的受体感知全脑框架为这类机制约化策略的代表性实例展开研究。我们梳理了通向该模型的数学谱系：从El Boustani和Destexhe（2009）的主方程形式，经Zerlaut等人（2016）的半分析传递函数框架、Zerlaut等人（2018）的基于电导的皮层平均场模型，以及Di Volo等人（2019）的自适应扩展，最终到Sacha等人的连接组耦合全脑实现。我们明确了该约化所依赖的假设，并为方程的推导与解释留出充足空间，使全脑模型被理解为显式约化与嵌入链的终点，而非黑箱。除单一谱系外，我们将该框架置于更广泛的约化群体模型格局中，调研其当前扩展方向，包括异质性、科学机器学习及数据驱动代理。最后，我们将算法模拟成本与内存流量作为独立于硬件的显式基准维度引入。因此，本综述将受体感知全脑建模界定为机制透明度、生物学细节、预测灵活性与计算负担之间的权衡。
英文摘要
Many pharmacological and pathological perturbations arise at molecular, synaptic, or cellular scales, but are observed through population and whole-brain signals. Cross-scale reductions must preserve relevant mechanisms while remaining tractable. This review asks which microscopic mechanisms remain explicit, interpretable, and testable after reduction, and what claims these models support. Using receptor-aware adaptive mean fields from the master-equation lineage as a worked case, we trace finite-size population statistics and semi-analytical transfer functions into conductance-based adaptive nodes coupled through the connectome. We compare this strategy with phenomenological neural masses, low-dimensional and population-density reductions, large-scale spiking models, and learned or hybrid surrogates, including computational work and memory traffic. Receptor-dependent synaptic kinetics, conductance state, and spike-frequency adaptation can remain manipulable across scales, enabling interpretable interventions and testable mesoscopic and macroscopic consequences. However, this relies on coarse-grained Markovianity, population homogeneity, quasi-stationary transfer functions, moment closure, regional uniformity, and measurement-specific observation models. First-order implementations discard covariance dynamics, while macroscopic agreement cannot identify a unique molecular cause. Node-local biological detail mainly changes prefactors, whereas dense global covariances change the scaling class. Cross-scale models should therefore be judged by the interventions and observables they preserve, validity domain, identifiability, empirical adequacy, and computational burden. Receptor-aware mean fields are not universal, but offer a transparent, tractable strategy for selected mechanistic questions when each reduction step is independently validated.
CommentsMinor typographical corrections and consistency fixes; conclusions unchanged
