Spiking neural networks offer low energy consumption due to their event-driven nature. Beyond binary spike outputs, their intrinsic floating-point dynamics merit greater attention. Neuronal threshold levels and reset modes critically determine spike count and timing. Hard reset cause information loss, while soft reset apply uniform treatment to neurons. To address these issues, we design an adaptive reset neuron that establishes relationships between inputs, outputs, and reset, while integrating a simple yet effective threshold adjustment strategy. Experimental results demonstrate that our method achieves excellent performance while maintaining lower energy consumption. In particular, it attains state-of-the-art accuracy on Tiny-ImageNet and CIFAR10-DVS. Codes are available at https://github.com/2ephyrus/AR-LIF.

翻译：脉冲神经网络因其事件驱动的特性而具备低能耗优势。除了二值脉冲输出外，其内在的浮点动力学特性值得更多关注。神经元阈值水平与复位模式对脉冲计数和发放时刻具有关键影响。硬复位会导致信息损失，而软复位则对神经元采用统一处理方式。为解决这些问题，我们设计了一种适应性复位神经元，该神经元建立了输入、输出与复位之间的关联，同时集成了一种简单而有效的阈值调节策略。实验结果表明，我们的方法在保持较低能耗的同时实现了优异的性能。特别是在Tiny-ImageNet和CIFAR10-DVS数据集上达到了最先进的准确率。代码公开于https://github.com/2ephyrus/AR-LIF。