Network-Optimised Spiking Neural Network for Event-Driven Networking

Spiking neural networks offer event-driven computation suited to time-critical networking tasks such as anomaly detection, local routing control, and congestion management at the edge. Classical units, including Hodgkin-Huxley, Izhikevich, and the Random Neural Network, map poorly to these needs. We introduce Network-Optimised Spiking (NOS), a compact two-variable unit whose state encodes normalised queue occupancy and a recovery resource. The model uses a saturating nonlinearity to enforce finite buffers, a service-rate leak, and graph-local inputs with delays and optional per link gates. It supports two differentiable reset schemes for training and deployment. We give conditions for equilibrium existence and uniqueness, local stability tests from the Jacobian trace and determinant, and a network threshold that scales with the Perron eigenvalue of the coupling matrix. The analysis yields an operational rule g* ~ k* rho(W) linking damping and offered load, shows how saturation enlarges the stable region, and explains finite-size smoothing of synchrony onsets. Stochastic arrivals follow a Poisson shot-noise model aligned with telemetry smoothing. 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/