Neurocomputing publishes articles describing recent fundamental contributions in the field of neurocomputing. Neurocomputing theory, practice and applications are the essential topics being covered.

NEW! Neurocomputing's Software Track allows you to expose your complete Software work to the community through a novel Publication format: the Original Software Publication

Overview:

Neurocomputing welcomes theoretical contributions aimed at winning further understanding of neural networks and learning systems, including, but not restricted to, architectures, learning methods, analysis of network dynamics, theories of learning, self-organization, biological neural network modelling, sensorimotor transformations and interdisciplinary topics with artificial intelligence, artificial life, cognitive science, computational learning theory, fuzzy logic, genetic algorithms, information theory, machine learning, neurobiology and pattern recognition.

Neurocomputing covers practical aspects with contributions on advances in hardware and software development environments for neurocomputing, including, but not restricted to, simulation software environments, emulation hardware architectures, models of concurrent computation, neurocomputers, and neurochips (digital, analog, optical, and biodevices).

Neurocomputing reports on applications in different fields, including, but not restricted to, signal processing, speech processing, image processing, computer vision, control, robotics, optimization, scheduling, resource allocation and financial forecasting.

Types of publications:

Neurocomputing publishes reviews of literature about neurocomputing and affine fields.

Neurocomputing reports on meetings, including, but not restricted to, conferences, workshops and seminars.

NEW! The Neurocomputing Software Track

Neurocomputing Software Track publishes a new format, the Original Software Publication (OSP) to disseminate exiting and useful software in the areas of neural networks and learning systems, including, but not restricted to, architectures, learning methods, analysis of network dynamics, theories of learning, self-organization, biological neural network modelling, sensorimotor transformations and interdisciplinary topics with artificial intelligence, artificial life, cognitive science, computational learning theory, fuzzy logic, genetic algorithms, information theory, machine learning, neurobiology and pattern recognition. We encourage high-quality original software submissions which contain non-trivial contributions in the above areas related to the implementations of algorithms, toolboxes, and real systems. The software must adhere to a recognized legal license, such as OSI approved licenses.

Importantly, the software will be a full peer reviewed publication that is able to capture your software updates once they are released. To fully acknowledge the author's/developers work your software will be fully citable as an Original Software Publication, archived and indexed and available as a complete online "body of work" for other researchers and practitioners to discover.

Special Issue on Honor of Dr. Alexander Poznyak 80th birthday celebration
截稿日期: 2026-01-31

Dr. Alexander Poznyak, a distinguished scholar and pioneer in the field of artificial neural networks, will celebrate his 80th birthday in December 2026. His groundbreaking research and visionary leadership have significantly shaped the trajectory of neural networks, inspiring countless researchers and practitioners worldwide. To commemorate his extraordinary contributions, we propose a special issue in the Neurocomputing Journal dedicated to honoring Dr. Poznyak's legacy. This special issue aligns perfectly with the journal's mission to advance the theory and application of neural networks. By showcasing the latest advancements in the field, we aim to inspire future generations of researchers and highlight the enduring impact of Dr. Poznyak's work.

Guest editors:

Dr Isaac Chairez
Tecnologico de Monterrey, Monterrey, Mexico
Email: isaac.chairez@tec.mx 
Fields of interest: Robotics, Neural networks, Fuzzy systems, Adaptive control, Data mining, Biomedical systems, Metabolic Networks and Dynamic Games

Dr Wen Yu 
Centro de Investigación y Estudios Avanzados del Instituto Politécnico Nacional (CINVESTAV-IPN), Ciudad de México, Mexico
Email: yuw@ctrl.cinvestav.mx 
Fields of interest: Robotics, Neural networks, Fuzzy systems, Adaptive control, Data mining

Special issue information:

This special issue invites comprehensive survey/review papers that address the latest advancements in neural networks and their applications. We encourage submissions of survey/review that build upon Dr. Poznyak's pioneering work and explore new frontiers in the field. Please note that only survey/review papers will be considered for this special issue; other types of papers, including original research articles, will not be accepted. Potential topics include, but are not limited to:

* Novel neural network architectures: Exploring innovative topologies for recurrent neural networks (RNNs) and differential neural networks (DNNs) with a focus on improved performance and efficiency.

* Game theory and neural networks: Developing theoretical frameworks for differential and static games with uncertain models using RNNs and DNNs, with applications in various domains.

* System identification and learning: Advancing parametric and non-parametric identification methods for deriving effective learning laws for RNNs and DNNs.

* Control and estimation: Designing robust controllers and estimators for systems with approximate dynamics based on RNN and DNN models.

* Stability analysis and learning: Applying Lyapunov stability theory to develop novel learning algorithms for neural networks with guaranteed stability and performance.

* Constrained optimization: Addressing state and control constraints in the dynamics of artificial neural networks for practical applications.

* Distributed parameter systems: Approximating complex distributed parameter systems using neural networks for efficient modeling and control.

* Physics-informed neural networks: Developing and applying physics-informed neural networks to solve challenging engineering and scientific problems.

* Biomedical and biotechnological applications: Exploring the use of neural networks in biomedical imaging, drug discovery, and other related areas.

* Chemical systems modeling and control: Applying neural networks to model and control complex chemical processes.

Manuscript submission information:

Important Dates:

    Manuscript Submission Deadline: January 31, 2026
    Final Acceptance Deadline: September 30, 2026

Prospective authors should follow standard author instructions for Neurocomputing and submit their manuscripts online at https://www.editorialmanager.com/neucom/default.aspx. Authors must select “VSI: Dr. Alexander Poznyak 80th birthday" when they reach the "Article Type" step.

Please refer to the Guide for Authors (https://www.sciencedirect.com/journal/neurocomputing/publish/guide-for-authors) to prepare your manuscript.

For any further information, the authors may contact the Guest Editors.

Keywords:

Differential Neural Networks; Recurrent Neural Networks; Advanced learning laws; Intelligent control

Special Issue on Advanced Machine Learning Techniques for Earth Observation Data Analysis: From Multi-Modal Sensing to Intelligent Environmental Monitoring
截稿日期: 2026-03-02

The huge amount of data currently produced by modern Earth Observation (EO) missions has raised new challenges for the Remote Sensing communities. EO sensors are now able to offer (very) high spatial resolution images with revisit time frequencies never achieved before considering different kinds of signals, e.g., multi-(hyper)spectral optical, radar, LiDAR and Digital Surface Models.

In this context, modern machine learning and deep learning techniques can play a crucial role to deal with such a large amount of heterogeneous, multi-scale and multi-modal data. Some examples of techniques that are gaining attention in this domain include transfer learning method, semi-supervised approaches, self-supervised learning and weakly supervised techniques.

Even though the use of machine learning and the development of ad-hoc techniques are gaining increasing popularity in the EO domain, we can witness that a significant lack of interaction between domain experts and machine learning researchers still exists. This Special Issue aims to advance the state-of-the-art in neural computing and machine learning for specific applications in the frame of Earth Observation data analysis. The Special Issue particularly encourages interdisciplinary contributions that bridge the gap between machine learning researchers and domain experts in Earth observation, fostering collaborative approaches that leverage complementary expertise from both communities.

Guest editors:

Dr. Roberto Interdonato (Primary Guest Editor)
French Agricultural Research Centre for International Development (Cirad), Joint Research Unit “Territories, Environment, Remote Sensing and Spatial Information” (UMR TETIS), Montpellier, France
Email: roberto.interdonato@cirad.fr
Research Interests: Data science, complex network analysis, remote sensing, agricultural landscapes, food security

Dr. Thomas Corpetti
French National Centre for Scientific Research (CNRS), Rennes, France
Email: thomas.corpetti@cnrs.fr
Research Interests: Remote sensing image processing, environmental monitoring, data assimilation, urban and land cover analysis

Dr. Dino Ienco
National Research Institute for Agriculture, Food and Environment (INRAE), Joint Research Unit “Territories, Environment, Remote Sensing and Spatial Information” (UMR TETIS), Montpellier, France
Email: dino.ienco@inrae.fr
Research Interests: Machine learning, data science, spatio-temporal data analysis, remote sensing

Prof. Minh-Tan Pham
University of Bretagne Sud (UBS), Institute for Research in Informatics and Random Systems (IRISA), Vannes / Rennes, France
Email: minh-tan.pham@univ-ubs.fr
Research Interests: Image processing, computer vision, self-supervised/weakly-supervised learning, remote sensing scene recognition

Dr. Cassio Fraga Dantas
National Research Institute for Agriculture, Food and Environment (INRAE), Joint Research Unit “Territories, Environment, Remote Sensing and Spatial Information” (UMR TETIS), Montpellier, France
Email: cassio.fraga-dantas@inrae.fr
Research Interests: Spatio-temporal data analysis, explainable AI, machine learning for remote sensing, agricultural and environmental applications

Special issue information:

This Special Issue will serve as a comprehensive resource for researchers seeking to develop and deploy cutting-edge neural computing techniques to Earth Observation challenges. By fostering collaboration between machine learning experts and domain specialists, we anticipate significant advances in both methodological development and practical applications, ultimately contributing to better understanding and management of our planet's environmental systems.

We invite original research articles, comprehensive reviews, and technical notes addressing (but not limited to) the following topics:

    Supervised Classification and Regression for Multi(Hyper)-spectral data
    Supervised Classification and Regression for Satellite Image Time Series data
    Unsupervised Learning of EO Data
    Machine Learning approaches for the analysis of multi-scale EO Data
    Machine Learning approaches for the analysis of multi-source EO Data
    Semi-supervised classification approaches for EO Data
    Active learning for EO Data
    Transfer Learning and Domain Adaptation for EO Data
    Interpretability and explainability of machine learning methods in the context of EO data analysis
    Bayesian machine learning for EO Data
    Dimensionality Reduction and Feature Selection for EO Data
    Graphicals models for EO Data
    Structured output learning for EO Data
    Multiple instance learning for EO Data
    Multi-task learning for EO Data
    Online learning for EO Data
    Embedding and Latent factor for EO Data
    Foundation Models for Earth Observation
    Multi-Modal approaches for EO Data
    Self-supervised learning for EO Data
    Physics-informed machine learning for EO Data

The special issue would be the follow up of the 7th edition of the MACLEAN workshop (Machine Learning for Earth Observation data - https://sites.google.com/view/maclean25) that will be held jointly to the ECML/PKDD 2025 conference in Porto (European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases), Portugal (https://ecmlpkdd.org/2025/). We would like to underline that the submission will not be limited to the workshop audience but open to the general scientific community.

Manuscript submission information:

Important Dates:

    Submission Open Date: October 20th, 2025
    Submission Deadline: March 2nd, 2026
    Final Acceptance Deadline: December 1st ,2026

Prospective authors should follow standard author instructions for Neurocomputing and submit their manuscripts online at Editorial Manager system. Authors must select “Neurocomputing/VSI: Advanced Machine Learning for Earth Observation" when they reach the "Article Type" step.

Please refer to the Guide for Authors to prepare your manuscript.

For any further information, the authors may contact the Guest Editors.

Keywords:

Earth Observation, Remote Sensing, Machine Learning, Environmental Monitoring, Multi-Modal

Special Issue on Positive Noise Learning
截稿日期: 2026-06-15

Noise is an emerging and popular keyword in recent years. The noise-based models have attracted more and more attention in the artificial intelligence community, including but not limited to random forest, dropout in neural networks (a kind of structural positive noise), generative adversarial networks, adversarial training, noisy augmentation, positive-incentive noise, diffusion models, and flow matching models. Although most of these models don’t explicitly claim that they aim to learn noise, they actually utilize the positive noise implicitly. In many current studies, it is pointed out that noise can also be beneficial to large models. Therefore, noise should not be simply regarded as a harmful component any more. The positivity of noise deserves more systematic studies.

Although there are plenty of noise-related models, scientific studies of beneficial noise learning are still lacking to some extent. Most of these noise-based models just use positive noise in a heuristic way. This Special Issue calls for papers that study several attractive, natural, and urgent questions: (1) how a model learns the positive noise in a controllable manner; (2) what kind of noise will be beneficial to specific models/tasks; (3) the theoretical bound of positive noise.

Guest editors:

Dr. Hongyuan Zhang (Executive Guest Editor)
The University of Hong Kong, Hong Kong
Email: hyzhang98@gmail.com 
Research Interests: Noise Learning, Representation Learning, Generative AI, Embodied AI

Prof. Xuelong Li
Chief Technology Officer (CTO) and Chief Scientist of China Telecom, China
Email: xuelong_li@ieee.org 
Research Interests: Noise Analysis, Computer Vision, Imaging

Special issue information:

This Special Issue seeks to cover a wide range of topics related to positive noise learning and analysis, including but not limited to:

1. Positive-incentive noise;
2. Noise-based generative models such as GAN, diffusion models, and flow matching;
3. Positive noisy and uncertain structure in deep learning models;
4. Noisy model training such as positive noisy labels and adversarial training;
5. Noisy augmentations in diverse fields such as representation learning and signal detection;
6. Positive noise in large models;
7. Positive noise in data acquisition;
8. Explainable analysis for beneficial noise.

Manuscript submission information:

Important Dates:

    Submission Open Date: August 20, 2025
    Submission Deadline: May 15, 2026
    Final Acceptance Deadline: January 15, 2027

Prospective authors should follow standard author instructions for Neurocomputing and submit their manuscripts online at Editorial Manager system. Authors must select “VSI: Positive Noise Learning" when they reach the "Article Type" step.

Please refer to the Guide for Authors to prepare your manuscript.

For any further information, the authors may contact the Guest Editors.

Keywords:

Noise Learning, Information Theory, Explainability, Generative Models