Spiking Neural Networks (SNNs) offer compelling energy efficiency on neuromorphic hardware, yet their training remains challenging because the discrete spike threshold is non-differentiable. Surrogate-gradient methods sidestep this by approximating the derivative, but they impose backpropagation infrastructure that is incompatible with on-chip learning. Evolution Strategies (\es) are a natural gradient-free alternative, yet their computational cost scales with the number of parameters, making them impractical for large weight matrices. We present a method for training SNNs using EGGROLL, a low-rank factorisation of ES perturbations that reduces per-generation memory from $\mathcal{O}(mn)$ to $\mathcal{O}(r(m{+}n))$. Combining EGGROLL with a Leaky Integrate-and-Fire SNN on N-MNIST, we demonstrate that gradient-free training achieves 79.21% test accuracy while reducing per-generation wall-clock time by 2.23$\times$ relative to full-rank ES. Our results demonstrate EGGROLL is viable for SNN training, with a clear accuracy-speed tradeoff, compatible with training on neuromorphic hardware without surrogate gradients.

翻译：脉冲神经网络在神经形态硬件上具有显著的能效优势，但其训练因离散脉冲阈值不可微分而颇具挑战。替代梯度方法通过近似导数规避了这一问题，但这类方法需要反向传播基础设施，与片上学习不兼容。进化策略作为天然的无梯度替代方案，其计算成本却随参数数量增长，导致无法应用于大规模权重矩阵。我们提出一种利用EGGROLL训练脉冲神经网络的方法，该方法通过ES扰动的低秩分解将每代内存需求从$\mathcal{O}(mn)$降至$\mathcal{O}(r(m{+}n))$。将EGGROLL与基于N-MNIST数据集的泄露整合-发放脉冲神经网络结合，我们证明无梯度训练可达到79.21%的测试准确率，同时将每代运行时间相比全秩ES减少2.23倍。实验结果表明，EGGROLL在脉冲神经网络训练中具有可行性，存在清晰的准确率-速度权衡，且无需替代梯度即可兼容神经形态硬件上的训练。