EDITORIAL article
Front. Artif. Intell.
Sec. Machine Learning and Artificial Intelligence
Volume 8 - 2025 | doi: 10.3389/frai.2025.1676744
This article is part of the Research TopicDeep Neural Network Architectures and Reservoir ComputingView all 6 articles
Editorial: Deep Neural Network Architectures and Reservoir Computing
Provisionally accepted- 1Chiba Institute of Technology, Narashino, Japan
- 2Mahindra University, Hyderabad, India
- 3Tokyo Daigaku, Bunkyo, Japan
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
Over the past decade, deep learning (DL) techniques such as convolutional neural networks (CNNs) and long short-term memory (LSTM) networks have played a pivotal role in advancing the field of computational intelligence (Bengio et al. (2021)). Recent developments in deep neural network (DNN) architectures and computational infrastructure (particularly parallel computing), have further accelerated the progress by supporting the computational demands of optimizing large numbers of network parameters. These advancements have expanded the applicability of DL to a broad range of tasks in computational intelligence (Sharifani and Amini (2023)).Simultaneously, reservoir computing (RC) has attracted increasing attention (Tanaka et al. (2019)).Typically, RC consists of a fixed recurrent neural network (the reservoir) and a trainable readout layer. It exploits the nonlinear spatiotemporal dynamics of the reservoir to transform inputs, while learning is applied only to the output layer. This structure dramatically reduces the number of trainable parameters, resulting in high learning efficiency. However, conventional RC, which typically involves a single reservoir layer, has generally not matched the performance of deeper neural architectures used in mainstream DL.
Keywords: Deep echo state network, deep learning, dynamics, Echo state network, reservoir computing
Received: 31 Jul 2025; Accepted: 04 Aug 2025.
Copyright: © 2025 Nobukawa, Bhattacharya and Hirose. 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: Sou Nobukawa, Chiba Institute of Technology, Narashino, Japan
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.