A Bibliometric Review of Neuromorphic Computing and Spiking Neural Networks
Nicholas J. Pritchard, Andreas Wicenec, Mohammed Bennamoun, Richard Dodson
14 April 2023
Author Verified?
true
High School Summary
Neuromorphic computing and spiking neural networks are new technologies inspired by the human brain that aim to be more energy efficient and powerful than traditional computers. Spiking neural networks have the potential to advance artificial intelligence by being the basis of third-generation neural networks. With advancements in memristive and compute-in-memory technologies, neuromorphic computing hardware is becoming more commercially available, leading to low-power computing. This article analyzes the literature in this field over the past 22 years to identify trends, influential authors, journals, and institutions, collaborations between countries and authors, and changes in research topics. By sharing this information, the article hopes to inspire researchers and AI practitioners to explore the possibilities of these new technologies in computing.
University Summary
Neuromorphic computing and spiking neural networks are innovative approaches to achieve more efficient and powerful computing inspired by the human brain. This research aims to advance artificial intelligence by using spiking neural networks as the foundation for third-generation neural networks. With the development of memristive and compute-in-memory technologies, neuromorphic computing hardware is transitioning from prototypes to commercial chipsets, enabling low-power computing. The literature on these concepts is extensive and distinct from traditional artificial neural network sources. In this article, the authors conduct a bibliometric analysis of the literature from the past 22 years to identify publication and citation trends, influential authors, journals, and institutions, collaborations, and changes in research topics. By providing an overview of the most impactful literature, this study encourages AI practitioners and researchers to explore the potential of spiking neural networks for the future of computing.