EDITORIAL article
Front. Comput. Neurosci.
This article is part of the Research TopicNeuromorphic and Deep Learning Paradigms for Neural Data Interpretation and Computational NeuroscienceView all 5 articles
Editorial: Neuromorphic and Deep Learning Paradigms for Neural Data Interpretation and Computational Neuroscience
Provisionally accepted
- 1Peking University, Beijing, China
- 2Southwest University, Chongqing, China
- 3Mohamed bin Zayed University of Artificial Intelligence, Masdar City, United Arab Emirates
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Keywords: brain-inspired computing, deep learning, Neural Data Interpretation, neuromorphic computing, Spiking neural networks (SNNs)
Received: 16 Jan 2026; Accepted: 21 Jan 2026.
Copyright: © 2026 Zou, Yuan and Wen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Chenglong Zou
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.