Spiking neurons, the fundamental information processing units of Spiking Neural Networks (SNNs), have the all-or-zero information output form that allows SNNs to be more energy-efficient compared to Artificial Neural Networks (ANNs). However, the hard reset mechanism employed in spiking neurons leads to information degradation due to its uniform handling of diverse membrane potentials. Furthermore, the utilization of overly simplified neuron models that disregard the intricate biological structures inherently impedes the network's capacity to accurately simulate the actual potential transmission process. To address these issues, we propose a dendrite-soma-axon (DSA) neuron employing the soft reset strategy, in conjunction with a potential change-based perception mechanism, culminating in the change-perceptive dendrite-soma-axon (CP-DSA) neuron. Our model contains multiple learnable parameters that expand the representation space of neurons. The change-perceptive (CP) mechanism enables our model to achieve competitive performance in short time steps utilizing the difference information of adjacent time steps. Rigorous theoretical analysis is provided to demonstrate the efficacy of the CP-DSA model and the functional characteristics of its internal parameters. Furthermore, extensive experiments conducted on various datasets substantiate the significant advantages of the CP-DSA model over state-of-the-art approaches.

翻译：脉冲神经元作为脉冲神经网络的基本信息处理单元，其全有或全无的信息输出形式使得脉冲神经网络相比人工神经网络具有更高的能效。然而，脉冲神经元采用的硬重置机制因其对多样膜电位的统一处理方式，导致信息退化。此外，使用过度简化且忽略复杂生物结构的神经元模型，本质上限制了网络准确模拟实际电位传输过程的能力。为解决这些问题，我们提出了一种采用软重置策略的树突-胞体-轴突神经元，并结合基于电位变化的感知机制，最终构建了变化感知树突-胞体-轴突神经元。该模型包含多个可学习参数，扩展了神经元的表示空间。变化感知机制使模型能够利用相邻时间步的差异信息，在较短时间步内实现具有竞争力的性能。我们提供了严格的理论分析，以证明CP-DSA模型的有效性及其内部参数的功能特性。此外，在多个数据集上进行的大量实验证实了CP-DSA模型相较于现有先进方法的显著优势。