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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

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

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

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