Risk-Aware Gradient Allocation for Spiking Neural Network

Surrogate-gradient learning enables the direct training of spiking neural networks (SNNs), but conventional threshold-centered surrogates concentrate backward gradient energy on the membrane states most susceptible to spike flipping. We recast surrogate design as a risk-aware gradient allocation problem and introduce Gradient Risk Exposure (GRE) to quantify this mismatch between gradient concentration and state instability. By minimizing GRE subject to a Kullback–Leibler penalty on deviations from the baseline allocation, we derive the Risk-Calibrated Surrogate Gradient (RCSG), a closed-form exponential calibration applicable to arbitrary baseline surrogates. RCSG redistributes gradient energy toward more stable membrane states without altering the forward neuronal dynamics or requiring an auxiliary robustness loss. On three static-image classification benchmarks, RCSG improves adversarial robustness while maintaining or improving clean accuracy, and its gains hold consistently across surrogate families, network architectures, and attack gradient estimators.