Artificial neural networks achieve strong performance on benchmark tasks but remain fundamentally brittle under perturbations, limiting their deployment in real-world settings. In contrast, biological nervous systems sustain reliable function across decades through homeostatic regulation coordinated across multiple temporal scales. Inspired by this principle, this presents Multi-Scale Temporal Homeostasis (MSTH), a biologically grounded framework that integrates ultra-fast (5-ms), fast (2-s), medium (5-min) and slow (1-hrs) regulation into artificial networks. MSTH implements the cross-scale coordination system for artificial neural networks, providing a unified temporal hierarchy that moves beyond superficial biomimicry. The cross-scale coordination enhances computational efficiency through evolutionary-refined optimization mechanisms. Experiments across molecular, graph and image classification benchmarks show that MSTH consistently improves accuracy, eliminates catastrophic failures and enhances recovery from perturbations. Moreover, MSTH outperforms both single-scale bio-inspired models and established state-of-the-art methods, demonstrating generality across diverse domains. These findings establish cross-scale temporal coordination as a core principle for stabilizing artificial neural systems, positioning MSTH as a foundation for building robust, resilient and biologically faithful intelligence.

翻译：人工神经网络在基准任务上表现出色，但在扰动下仍存在根本性的脆弱问题，限制了其在真实场景中的部署。相比之下，生物神经系统通过跨多个时间尺度协调的稳态调节，能够维持数十年可靠的运作。受此原理启发，本文提出多尺度时间稳态（MSTH），这是一个基于生物学原理的框架，将超快（5毫秒）、快速（2秒）、中速（5分钟）和慢速（1小时）的调节机制整合到人工网络中。MSTH为人工神经网络实现了跨尺度协调系统，提供了一个超越表面仿生学的统一时间层次结构。这种跨尺度协调通过进化优化的机制提高了计算效率。在分子、图结构和图像分类基准上的实验表明，MSTH能持续提升精度、消除灾难性故障并增强从扰动中恢复的能力。此外，MSTH在性能上超越了单尺度仿生模型和已有的先进方法，展现了其在不同领域的普适性。这些发现确立了跨尺度时间协调作为稳定人工神经系统的核心原则，将MSTH定位为构建鲁棒、强韧且符合生物学原理的智能系统的基础。