In the field of image recognition, spiking neural networks (SNNs) have achieved performance comparable to conventional artificial neural networks (ANNs). In such applications, SNNs essentially function as traditional neural networks with quantized activation values. This article focuses on an another alternative perspective,viewing SNNs as binary-activated recurrent neural networks (RNNs) for sequential modeling tasks.From this viewpoint, current SNN architectures face several fundamental challenges in sequence modeling: (1) Traditional models lack effective memory mechanisms for long-range sequence modeling; (2) The biological-inspired components in SNNs (such as reset mechanisms and refractory period applications) remain theoretically under-explored for sequence tasks; (3) The RNN-like computational paradigm in SNNs prevents parallel training across different timesteps.To address these challenges, this study conducts a systematic analysis of the fundamental mechanisms underlying reset operations and refractory periods in binary-activated RNN-based SNN sequence models. We re-examine whether such biological mechanisms are strictly necessary for generating sparse spiking patterns, provide new theoretical explanations and insights, and ultimately propose the fixed-refractory-period SNN architecture for sequence modeling.

本研究针对当前脉冲神经网络在序列建模中的记忆机制不足、对生物启发成分探讨不够以及限制并行训练的计算范式等问题进行了分析。通过重新审视重置操作和不应期的基本机制，提出了固定不应期脉冲神经网络架构，为生成稀疏脉冲模式提供了新的理论解释和见解，对序列建模具有重要影响。

重新审视用于序列建模的脉冲神经网络中的重置机制：针对二进制激活RNN的专门离散化