Editorial : Theoretical advances and practical applications of spiking neural networks

Di Caterina, Gaetano and Zhang, Malu and Liu, Jundong (2024) Editorial : Theoretical advances and practical applications of spiking neural networks. Frontiers in Neuroscience, 18. 1406502. ISSN 1662-453X (https://doi.org/10.3389/fnins.2024.1406502)

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Neuromorphic engineering has experienced a significant growth in popularity over the last 10 years, going from being a niche academic research area, often confused with deep learning and mostly unknown to the wider industrial community, to being the main focus of many funding calls, significant industrial endeavours, and national and international initiatives. The advent to market of neuromorphic sensors, with a related widening understanding of the event-based sensing paradigm, combined with the development of the first neuromorphic processors, has steered the wider academic community and industry toward the investigation and use of Spiking Neural Networks (SNN). Very often overlooked in favour of the now extremely popular Deep Neural Networks (DNN), SNNs have become a serious alternative to DNNs, in application domains where size, weight and power are key limiting factors to the deployment of AI systems, such in Space applications, Security and Defence, Automotive, and more generally AI at the Edge. Nonetheless, there are many aspects of SNNs that still require significant investigation, as there are many unexplored avenues in this regard. To this aim, the articles accepted in this special topic present novel research works that focus on methodologies for training of SNNs and on the use of SNN in real life applications.