Neuromorphic computing and spiking neural networks aim to leverage biological inspiration to achieve greater energy efficiency and computational power beyond traditional von Neumann architectured machines. In particular, spiking neural networks hold the potential to advance artificial intelligence as the basis of third-generation neural networks. Aided by developments in memristive and compute-in-memory technologies, neuromorphic computing hardware is transitioning from laboratory prototype devices to commercial chipsets; ushering in an era of low-power computing. As a nexus of biological, computing, and material sciences, the literature surrounding these concepts is vast, varied, and somewhat distinct from artificial neural network sources. This article uses bibliometric analysis to survey the last 22 years of literature, seeking to establish trends in publication and citation volumes (III-A); analyze impactful authors, journals and institutions (III-B); generate an introductory reading list (III-C); survey collaborations between countries, institutes and authors (III-D), and to analyze changes in research topics over the years (III-E). We analyze literature data from the Clarivate Web of Science using standard bibliometric methods. By briefly introducing the most impactful literature in this field from the last two decades, we encourage AI practitioners and researchers to look beyond contemporary technologies toward a potentially spiking future of computing.

翻译：类脑计算与脉冲神经网络旨在借鉴生物灵感，在超越传统冯·诺依曼架构机器的同时，实现更高的能效与计算能力。尤其作为第三代神经网络的基础，脉冲神经网络具备推动人工智能发展的潜力。借助忆阻器和存内计算技术的进步，类脑计算硬件正从实验室原型设备向商业芯片组过渡，开启了低功耗计算的时代。作为生物学、计算科学和材料科学的交汇点，围绕这些概念的文献庞杂多样，且在某种程度上与人工神经网络文献来源有所区别。本文采用文献计量分析方法，对过去22年的文献进行综述，旨在：确定论文发表与引用量的趋势（III-A）；分析高影响力作者、期刊及机构（III-B）；建立入门阅读清单（III-C）；调查国家、机构及作者间的合作情况（III-D）；以及分析研究主题随时间的演变（III-E）。我们使用标准文献计量方法分析来自科睿唯安科学网（Web of Science）的文献数据。通过简要介绍该领域过去二十年间最具影响力的文献，我们鼓励人工智能从业者和研究人员跳出当前技术的局限，展望一个潜在的脉冲计算未来。