标题： 脉冲神经网络在普适计算中的前景：综述与新视角 显示英文标题

标题： The Promise of Spiking Neural Networks for Ubiquitous Computing: A Survey and New Perspectives

摘要： 尖峰神经网络（SNNs）作为一种受生物启发的网络，利用稀疏的事件驱动信号实现了低功耗计算，并且天然能够建模时序动态。这些特性与泛在计算系统的需要紧密契合，这类系统通常运行在资源受限的设备上，同时持续监控和处理时间序列传感器数据。 尽管SNNs具有独特且有前景的特点，但它们在泛在计算社区中仍未得到充分关注，或者至少没有被广泛采用。 为了解决这一问题，本文首先介绍了SNNs的核心组成部分，包括模型和训练机制。 随后，本文对76项基于SNN的时间序列数据分析研究进行了系统的综述，将其分类到六个关键应用领域。 对于每个领域，我们总结了相关工作及其后续进展，提炼了核心见解，并强调了对研究人员和从业者的关键启示。 为了促进动手实验，我们还提供了当前软件框架和神经形态硬件平台的全面综述，详细描述了它们的功能和规格，并根据特定的应用需求提供了选择开发工具的定制化建议。 最后，我们在每个应用领域识别了主要挑战，并提出了泛在计算社区需要探索的未来研究方向。 我们的调查突显了SNNs在实现多样化应用领域中的节能泛在感知方面的变革潜力，同时也为希望进入这一新兴领域的研究人员提供了一本必不可少的入门指南。 显示英文摘要

摘要： Spiking neural networks (SNNs) have emerged as a class of bio -inspired networks that leverage sparse, event- driven signaling to achieve low-power computation while inherently modeling temporal dynamics. Such characteristics align closely with the demands of ubiquitous computing systems, which often operate on resource- constrained devices while continuously monitoring and processing time - series sensor data. Despite their unique and promising features, SNNs have received limited attention and remain underexplored (or at least, under-adopted) within the ubiquitous computing community. To address this gap, this paper first introduces the core components of SNNs, both in terms of models and training mechanisms. It then presents a systematic survey of 76 SNN-based studies focused on time-series data analysis, categorizing them into six key application domains. For each domain, we summarize relevant works and subsequent advancements, distill core insights, and highlight key takeaways for researchers and practitioners. To facilitate hands-on experimentation, we also provide a comprehensive review of current software frameworks and neuromorphic hardware platforms, detailing their capabilities and specifications, and then offering tailored recommendations for selecting development tools based on specific application needs. Finally, we identify prevailing challenges within each application domain and propose future research directions that need be explored in ubiquitous community. Our survey highlights the transformative potential of SNNs in enabling energy-efficient ubiquitous sensing across diverse application domains, while also serving as an essential introduction for researchers looking to enter this emerging field.