USHE: Variance-Controlled Probabilistic Spiking Encoding for Low-Latency SNNs

Input encoding is a central design choice in spiking neural networks (SNNs), shaping how information and stochasticity enter both learning and inference. Widely used Bernoulli rate encoding relies on independent sampling, introducing input variance that can limit representational precision at low latency. We introduce unbiased spiking hypergeometric encoding (USHE), a tunable family of probabilistic encoders that preserves input intensities in expectation while controlling spike-rate variance and temporal dependence. By decoupling the latent population size from the simulation horizon, USHE controls encoding stochasticity without increasing network latency. Theoretically, we characterize USHE members through constrained variance-minimization problems and establish independent Bernoulli encoding as the infinite-population limit at fixed latency. Experiments on CIFAR-10 and CIFAR-100 show consistent improvements in clean accuracy over Bernoulli encoding and reveal distinct finite-population operating points under adversarial attacks and Gaussian corruption. Controlled encoder substitutions further separate training and inference effects, showing that the inference population governs a systematic clean–robustness trade-off whose magnitude depends on the trained model and architecture. Together, these results establish population-controlled temporal dependence as a principled design dimension for stochastic spike encoding, rather than treating input encoding as a fixed preprocessing step.