The suprachiasmatic nucleus (SCN) is the mammalian central circadian pacemaker with heterogeneous neurons acting in concert while each neuron harbors a self-sustained molecular clockwork. Nevertheless, how system-level SCN signals encode time of the day remains enigmatic. Here we show that population-level Ca²⁺ signals predict hourly time, via a group decision-making mechanism coupled with a spatially modular time feature representation in the SCN. Specifically, we developed a high-speed dual-view two-photon microscope for volumetric Ca²⁺ imaging of up to 9000 GABAergic neurons in adult SCN slices, and leveraged machine learning methods to capture emergent properties from multiscale Ca²⁺ signals as a whole. We achieved hourly time prediction by polling random cohorts of SCN neurons, reaching 99.0% accuracy at a cohort size of 900. Further, we revealed that functional neuron subtypes identified by contrastive learning tend to aggregate separately in the SCN space, giving rise to bilaterally symmetrical ripple-like modular patterns. Individual modules represent distinctive time features, such that a module-specifically learned time predictor can also accurately decode hourly time from random polling of the same module. These findings open a new paradigm in deciphering the design principle of the biological clock at the system level.

视交叉上核（SCN）是哺乳动物中枢昼夜节律起搏器，具有协同作用的异质神经元，而每个神经元都具有自我维持的分子发条装置。然而，系统级 SCN 信号如何编码一天中的时间仍然是个谜。在这里，我们展示了群体水平的 Ca ²⁺信号通过群体决策机制与 SCN 中的空间模块化时间特征表示相结合来预测每小时的时间。具体来说，我们开发了一种高速双视双光子显微镜，用于对成人 SCN 切片中多达 9000 个 GABA 能神经元进行体积 Ca ²⁺成像，并利用机器学习方法从整体上捕获多尺度 Ca ²⁺信号的涌现特性。我们通过轮询 SCN 神经元的随机队列实现了每小时的时间预测，在队列大小为 900 时达到 99.0% 的准确率。此外，我们发现通过对比学习识别的功能神经元亚型倾向于在 SCN 空间中单独聚集，从而产生双边对称波纹状的模块化图案。各个模块代表独特的时间特征，使得特定于模块的学习时间预测器也可以从同一模块的随机轮询中准确解码每小时时间。这些发现为破译系统级生物钟的设计原理开辟了新的范式。