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ORIGINAL RESEARCH article

Front. Comput. Neurosci.
Volume 18 - 2024 | doi: 10.3389/fncom.2024.1455530
This article is part of the Research Topic Understanding and Bridging the Gap between Neuromorphic Computing and Machine Learning, volume II View all 12 articles

Editorial: Understanding and Bridging the Gap between Neuromorphic Computing and Machine Learning, Volume II

Provisionally accepted
Lei Deng Lei Deng 1*Huajin Tang Huajin Tang 2Kaushik Roy Kaushik Roy 3
  • 1 Tsinghua University, Beijing, China
  • 2 Zhejiang University, Hangzhou, Zhejiang Province, China
  • 3 Purdue University, West Lafayette, Indiana, United States

The final, formatted version of the article will be published soon.

    Two routes have been paved for pursuing intelligence: neuroscience-inspired neuromorphic computing and computer-science-oriented machine learning. Although machine learning technologies, especially the recent large models, are revolutionizing the human life, neuromorphic computing with endorsement from the powerful and efficient brain represents the future. However, current neuromorphic models usually demonstrate lower accuracy compared to "standard" machine learning models, thus limiting their applications in real-world intelligent tasks. To understand and bridge this gap, we continue to organize the Research Topic in Frontiers in Neuroscience and Frontiers in Computational Neuroscience to collect recent advances in neuromorphic computing. We finally accepted 11 submissions, and the scope of these works covers neuromorphic models and algorithms, hardware implementation, and programming frameworks.

    Keywords: spiking neural networks, neuromorphic computing, neuromorphic hardware, artificial neural networks, machine learning

    Received: 27 Jun 2024; Accepted: 09 Sep 2024.

    Copyright: © 2024 Deng, Tang and Roy. 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: Lei Deng, Tsinghua University, Beijing, China

    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.