  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.
