Spiking Neural Networks (SNNs) are promising energy-efficient models and powerful framworks of modeling neuron dynamics. However, existing binary spiking neurons exhibit limited biological plausibilities and low information capacity. Recently developed ternary spiking neuron possesses higher consistency with biological principles (i.e. excitation-inhibition balance mechanism). Despite of this, the ternary spiking neuron suffers from defects including iterative information loss, temporal gradient vanishing and irregular distributions of membrane potentials. To address these issues, we propose Complemented Ternary Spiking Neuron (CTSN), a novel ternary spiking neuron model that incorporates an learnable complemental term to store information from historical inputs. CTSN effectively improves the deficiencies of ternary spiking neuron, while the embedded learnable factors enable CTSN to adaptively adjust neuron dynamics, providing strong neural heterogeneity. Furthermore, based on the temporal evolution features of ternary spiking neurons' membrane potential distributions, we propose the Temporal Membrane Potential Regularization (TMPR) training method. TMPR introduces time-varying regularization strategy utilizing membrane potentials, furhter enhancing the training process by creating extra backpropagation paths. We validate our methods through extensive experiments on various datasets, demonstrating remarkable performance advances.

翻译：脉冲神经网络（SNNs）是极具前景的高效能效模型，也是模拟神经元动力学的强大框架。然而，现有的二值脉冲神经元存在生物合理性有限与信息容量低的问题。近期发展的三值脉冲神经元展现出更高的生物学原理一致性（即兴奋-抑制平衡机制）。尽管如此，三值脉冲神经元仍存在迭代信息丢失、时序梯度消失以及膜电位分布不规则等缺陷。为解决这些问题，我们提出互补三值脉冲神经元（CTSN），这是一种新型三值脉冲神经元模型，通过引入可学习的互补项来存储历史输入信息。CTSN有效改善了传统三值脉冲神经元的不足，同时其嵌入的可学习因子使神经元能够自适应调整动力学特性，提供显著的神经异质性。此外，基于三值脉冲神经元膜电位分布的时序演化特征，我们提出时序膜电位正则化（TMPR）训练方法。TMPR通过利用膜电位引入时变正则化策略，通过创建额外的反向传播路径进一步增强训练过程。我们在多个数据集上进行了广泛实验验证所提方法，结果表明其性能显著提升。