Spiking Neural Networks (SNN) are characterised by their unique temporal dynamics, but the properties and advantages of such computations are still not well understood. In order to provide answers, in this work we demonstrate how Spiking neurons can enable temporal feature extraction in feed-forward neural networks without the need for recurrent synapses, showing how their bio-inspired computing principles can be successfully exploited beyond energy efficiency gains and evidencing their differences with respect to conventional neurons. This is demonstrated by proposing a new task, DVS-Gesture-Chain (DVS-GC), which allows, for the first time, to evaluate the perception of temporal dependencies in a real event-based action recognition dataset. Our study proves how the widely used DVS Gesture benchmark could be solved by networks without temporal feature extraction, unlike the new DVS-GC which demands an understanding of the ordering of the events. Furthermore, this setup allowed us to unveil the role of the leakage rate in spiking neurons for temporal processing tasks and demonstrated the benefits of "hard reset" mechanisms. Additionally, we also show how time-dependent weights and normalization can lead to understanding order by means of temporal attention.

翻译：脉冲神经网络（SNN）以其独特的时序动力学为特征，但其计算特性与优势尚未得到充分理解。为探究这一问题，本研究展示了脉冲神经元如何在不依赖循环突触的前馈神经网络中实现时序特征提取，揭示了其类脑计算原理在能效增益之外的广泛应用潜力，并阐明了其与传统神经元的本质差异。我们通过提出新任务DVS-Gesture-Chain（DVS-GC）验证了这一发现——该任务首次实现了在真实基于事件的动作识别数据集中对时序依赖感知的评估。研究表明，广泛应用的DVS Gesture基准可通过无时序特征提取的网络解决，而新提出的DVS-GC则要求理解事件的顺序关系。此外，该实验设置揭示了脉冲神经元漏电率在时序处理任务中的作用，并验证了"硬重置"机制的优越性。同时，我们还展示了时间依赖的权重与归一化如何通过时序注意力机制实现顺序理解。