Spiking Neural Networks (SNNs) have emerged with promising energy-efficient property, yet a substantial performance gap persists compared to Artificial Neural Networks (ANNs). This gap stems from at least two key limitations: first, conventional spiking neurons offer limited information representation capacity, underutilizing the rich dynamics of membrane potentials; second, fixed surrogate gradient (SG) functions across time steps leads to imprecise gradient propagation, impeding effective direct training. To address these two challenges, we propose a new direct training algorithm with three core innovations: first, a circulate-firing spiking neuron model that enhances information representation capacity by leveraging membrane potentials more effectively; second, a time-step-wise learnable surrogate gradient function, enabling accurate gradient estimation during backpropagation; third, a positive-negative balanced loss function to achieve equilibrium between positive and negative membrane potentials and further boost SNN performance. Extensive experiments demonstrate that our methods achieve competitive performance across multiple datasets. Our methods can generalize seamlessly to advanced architectures of Transformer, consistently outperforming existing methods. Our work highlights the effectiveness of further harnessing intrinsic membrane dynamics of SNNs for performance improvement, and thus open a new avenue for advancing high-performance spiking neural architectures.

翻译：尖峰神经网络（SNNs）凭借其低能耗特性而备受关注，但与人工神经网络（ANNs）之间仍存在显著性能差距。这一差距源于至少两个关键限制：第一，传统尖峰神经元的信息表示能力有限，未能充分利用膜电位的丰富动态特性；第二，跨时间步长使用固定替代梯度（SG）函数会导致梯度传播不精确，阻碍有效直接训练。为解决这两个挑战，我们提出一种新型直接训练算法，包含三项核心创新：第一，提出循环发放尖峰神经元模型，通过更有效利用膜电位增强信息表示能力；第二，设计时间步级可学习替代梯度函数，实现反向传播中精确梯度估计；第三，构建正负平衡损失函数，实现正负膜电位之间的均衡，进一步提升SNN性能。大量实验表明，我们的方法在多个数据集上均取得具有竞争力的性能。该方法可无缝泛化至Transformer等先进架构，持续优于现有方法。本研究凸显了进一步利用SNN内在膜动力学特性提升性能的有效性，为推进高性能尖峰神经架构开辟了新途径。