Time-critical networking requires low-latency decisions from sparse and bursty telemetry, where fixed-step neural inference waste computation. We introduce Network-Optimised Spiking (NOS), a two-state neuron whose variables correspond to normalised queue occupancy and a recovery resource. NOS combines a saturating excitability nonlinearity for finite buffers, service and damping leaks, graph-local inputs with per-link gates and delays, and differentiable resets compatible with surrogate gradients and neuromorphic deployment. We establish existence and uniqueness of subthreshold equilibria, derive Jacobian-based local stability tests, and obtain a scalar network stability threshold that separates topology from node physics through a Perron-mode spectral condition. A stochastic arrival model aligned with telemetry smoothing links NOS responses to classical queueing behaviour while explaining increased variability near stability margins. Across chain, star, and scale-free graphs, NOS improves early-warning F1 and detection latency over MLP, RNN, GRU, and temporal-GNN baselines under a common residual-based protocol, while providing practical calibration and stability rules suited to resource-constrained networking deployments. Code and Demos: https://mbilal84.github.io/nos-snn-networking/

翻译：时间关键型网络需要基于稀疏突发遥测数据做出低延迟决策，而固定步长的神经推断会浪费计算资源。本文提出网络优化脉冲（NOS）模型，这是一种双状态神经元，其变量对应于归一化队列占用率和恢复资源。NOS结合了适用于有限缓冲区的饱和兴奋性非线性特性、服务与阻尼泄漏、具有逐链路门控和延迟的图局部输入，以及与替代梯度及神经形态部署兼容的可微分重置机制。我们证明了亚阈值平衡点的存在性和唯一性，推导了基于雅可比矩阵的局部稳定性检验方法，并通过Perron模态谱条件获得了一个标量网络稳定性阈值，从而将拓扑结构与节点物理特性分离。与遥测平滑对齐的随机到达模型将NOS响应与经典排队行为联系起来，同时解释了稳定性边界附近变异性增加的现象。在链状、星型和无标度图结构上，基于共同的残差协议，NOS在早期预警F1分数和检测延迟方面优于MLP、RNN、GRU及时序图神经网络基线，同时提供了适用于资源受限网络部署场景的实用校准与稳定性规则。代码与演示：https://mbilal84.github.io/nos-snn-networking/