Abstract
As the third generation of neural networks, spiking neural networks (SNNs) have recently gained widespread attention for their biological plausibility, energy efficiency, and effectiveness in processing neuromorphic datasets. To better emulate biological neurons, various models such as Integrate-and-Fire (IF) and Leaky Integrate-and-Fire (LIF) have been widely adopted in SNNs. However, these neuron models overlook the refractory period, a fundamental characteristic of biological neurons. Research on excitable neurons reveal that after firing, neurons enter a refractory period during which they are temporarily unresponsive to subsequent stimuli. This mechanism is critical for preventing over-excitation and mitigating interference from aberrant signals. Therefore, we propose a simple yet effective method to incorporate the refractory period into spiking LIF neurons through spike-triggered threshold dynamics, termed RPLIF. Our method ensures that each spike accurately encodes neural information, effectively preventing neuron over-excitation under continuous inputs and interference from anomalous inputs. Incorporating the refractory period into LIF neurons is seamless and computationally efficient, enhancing robustness and efficiency while yielding better performance with negligible overhead. To the best of our knowledge, RPLIF achieves state-of-the-art performance on Cifar10-DVS(82.40%) and N-Caltech101(83.35%) with fewer timesteps and demonstrates superior performance on DVS128 Gesture(97.22%) at low latency.
Abstract (translated)
作为第三代神经网络，脉冲神经网络（SNNs）因其生物学合理性、能源效率以及在处理类脑数据集方面的有效性而最近受到了广泛关注。为了更好地模拟生物神经元，各种模型如积分放电（IF）和漏导积分放电（LIF）被广泛应用于SNN中。然而，这些神经元模型忽略了复现期，这是生物神经元的一个基本特征。研究表明，在放电之后，神经元会进入一个暂时对后续刺激无反应的复现期。这一机制对于防止过度兴奋以及减少异常信号干扰至关重要。 因此，我们提出了一种简单而有效的方法，通过脉冲触发阈值动力学将复现期纳入到脉冲LIF神经元中，并将其命名为RPLIF。我们的方法确保每个脉冲能够准确编码神经信息，在连续输入和异常输入干扰下有效地防止神经元过度兴奋。 将复现期融入LIF神经元是无缝且计算效率高的，增强了稳健性和效率，并在几乎不增加额外开销的情况下提高了性能。据我们所知，RPLIF在Cifar10-DVS（82.40%）和N-Caltech101（83.35%）数据集上实现了最先进的性能，在更少的时间步下表现出色，并且在DVS128手势识别任务中以低延迟取得了97.22%的卓越表现。
URL
https://arxiv.org/abs/2509.17769
