Abstract
We investigate the extent to which Spiking Neural Networks (SNNs) trained with Surrogate Gradient Descent (Surrogate GD), with and without delay learning, can learn from precise spike timing beyond firing rates. We first design synthetic tasks isolating intra-neuron inter-spike intervals and cross-neuron synchrony under matched spike counts. On more complex spike-based speech recognition datasets (Spiking Heidelberg Digits (SHD) and Spiking Speech Commands (SSC), we construct variants where spike count information is eliminated and only timing information remains, and show that Surrogate GD-trained SNNs are able to perform significantly above chance whereas purely rate-based models perform at chance level. We further evaluate robustness under biologically inspired perturbations -- including Gaussian jitter per spike or per-neuron, and spike deletion -- revealing consistent but perturbation-specific degradation. Networks show a sharp performance drop when spike sequences are reversed in time, with a larger drop in performance from SNNs trained with delays, indicating that these networks are more human-like in terms of behaviour. To facilitate further studies of temporal coding, we have released our modified SHD and SSC datasets.
Abstract (translated)
我们研究了使用替代梯度下降（Surrogate Gradient Descent，Surrogate GD）训练的脉冲神经网络（Spiking Neural Networks，SNNs），在有或没有延迟学习的情况下，能否从精确的脉冲时间而非仅基于放电率来学习。首先，我们设计了一系列合成任务，这些任务孤立地考察了在匹配放电数量的前提下，单个神经元内的脉冲间隔和跨神经元同步性。对于更复杂的基于脉冲的声音识别数据集（Spiking Heidelberg Digits (SHD) 和 Spiking Speech Commands (SSC)，我们构建了一些变体，在其中消除了放电计数信息，只剩下时间信息，并展示了用替代梯度下降法训练的SNNs能够显著地超越随机水平，而单纯基于速率模型的表现则在随机水平。进一步地，我们在生物启发的扰动下评估了它们的鲁棒性——包括每个脉冲或每种神经元上的高斯抖动（Gaussian jitter）和脉冲删除，揭示了与特定干扰相关的但一致性的性能下降。当脉冲序列的时间顺序被逆转时，网络表现出现了急剧下降，而那些使用延迟训练的SNNs的表现下降幅度更大，这表明这些网络在行为上更接近于人类。 为了促进对时间编码进一步的研究，我们已经发布了修改后的SHD和SSC数据集。
URL
https://arxiv.org/abs/2507.16043
