Vanilla spiking neurons in Spiking Neural Networks (SNNs) use charge-fire-reset neuronal dynamics, which can only be simulated in serial and can hardly learn long-time dependencies. We find that when removing reset, the neuronal dynamics are reformulated in a non-iterative form and can be parallelized. By rewriting neuronal dynamics without resetting to a general formulation, we propose the Parallel Spiking Neuron (PSN), which uses dense connections between time-steps to maximize the utilization of temporal information. To avoid the use of future inputs for low-latency inference, we add masks on the weights and obtain the masked PSN. By sharing weights across time-steps, the sliding PSN is proposed with the ability to deal with sequences with variant lengths. We evaluate the PSN family on simulation speed and temporal/static data classification, and the results show the overwhelming advantage of the PSN family in efficiency and accuracy. To our best knowledge, this is the first research about parallelizing spiking neurons and can be a cornerstone for the spiking deep learning community. Our codes are available at \url{https://github.com/fangwei123456/Parallel-Spiking-Neuron}.

翻译：在脉冲神经网络（SNNs）中，传统尖峰神经元采用充电-放电-重置的神经动力学，该过程仅能串行模拟且难以学习长期依赖关系。我们发现，移除重置操作后，神经动力学可重构为无迭代形式并实现并行化。通过将无重置的神经动力学重写为通用公式，我们提出了并行尖峰神经元（PSN），其利用时间步之间的密集连接最大化时间信息的利用效率。为避免低延迟推理时使用未来输入，我们为权重添加掩码得到掩码PSN。通过跨时间步共享权重，我们提出滑动PSN以处理变长序列。我们在仿真速度以及时序/静态数据分类任务上评估了PSN系列模型，结果表明该系列在效率和准确率方面具有压倒性优势。据我们所知，这是首个关于尖峰神经元并行化的研究，可成为脉冲深度学习领域的基石。代码已开源至 \url{https://github.com/fangwei123456/Parallel-Spiking-Neuron}。