Probe and Calibrate: Stochastic Firing-Boundary Adaptation for Spiking Neural Networks

Distribution shifts can perturb the membrane responses of Spiking Neural Networks (SNNs), misaligning source-trained firing boundaries with target-domain activity. Threshold calibration provides a compact adaptation interface, but prediction confidence alone does not reveal how responsive neuronal firing is to boundary changes. We propose ProCal, a probe-and-calibrate framework for online test-time adaptation that uses this local responsiveness to guide threshold updates. During adaptation, each adapted layer receives a stochastic threshold with a learnable location and scale. The induced firing probability yields an analytic responsiveness score: the normalized conditional variance of the binary firing response. ProCal aggregates this score by sample and combines it with prediction confidence to weight entropy minimization. It updates only two scalars per adapted layer—38 on MS-ResNet-20—while freezing synaptic weights and requiring no adaptation-specific source retraining. Each target batch receives one stochastic update followed by prediction with the learned locations as noise-free thresholds. Across two event-corruption suites and two controlled frame-based variants, ProCal achieves the highest mean accuracy among the evaluated methods in three settings. It reaches 53.82% on CIFAR10dvs-C and 64.72% on DVS128Gesture-C; component ablations further show gains of 1.02 and 1.42 percentage points over deterministic threshold adaptation.