期刊信息

Neurocomputing

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影响因子:
6.5
出版商:
Elsevier
ISSN:
0925-2312
浏览:
167532
关注:
201
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征稿

Neurocomputing is an academic journal published by Elsevier. (ISSN 0925-2312, impact factor 6.5, CCF C).

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.
由 Dou Sun 最后更新于

特刊

特刊：Neural Dynamics in Intelligent Computing and Applications 截稿日期: 2026-10-30 In recent decades, neural dynamics has emerged as a fundamental mechanism for understanding and enhancing intelligent computing, offering powerful tools for modeling, prediction, optimization, and adaptive decision-making. With the rapid growth of computational resources and the increasing complexity of real-world tasks, neural dynamics has found wide-ranging applications in domains such as autonomous systems, healthcare, financial technology, robotics, and smart manufacturing. By capturing the temporal evolution of neural states and interactions, neural dynamics provides adaptive, data-driven solutions with strong nonlinear modeling and representation capabilities, enabling intelligent systems to achieve improved accuracy, robustness, and generalization in dynamic and uncertain environments. Neural dynamics emphasizes the time-varying processes, feedback mechanisms, and emergent behaviors of neural systems, thereby offering a novel perspective on learning, adaptation, and control. The integration of neural dynamics with control theory, optimization, reinforcement learning, and neuromorphic computing has opened up new research frontiers, such as interpretable dynamic modeling, safe and robust decision-making under uncertainty, and real-time adaptive control in rapidly changing environments. Given the significant progress and growing body of research in this area, this Special Issue now specifically invites survey/review articles that comprehensively summarize and critically evaluate recent advances in neural dynamics for intelligent computing. Survey/review papers should provide insightful syntheses of existing work, identify key challenges and emerging trends, and propose future research directions. The aim of this Special Issue is to gather high-quality survey/review articles that survey the latest developments in neural dynamics, with particular emphasis on theoretical foundations, innovative modeling approaches, algorithmic design, and applications in emerging technologies. We encourage contributions that explore the integration of neural dynamics with control systems, robotics, natural language processing, and real-time decision-making in complex environments from a survey/review perspective. Guest editors: Prof. Long Jin (Executive Guest Editor) Lanzhou University, Lanzhou, China Email: [email protected] Areas of Expertise: dynamics, neural networks, robotics, and optimization Prof. Shuai Li University of Oulu, Oulu, Finland Email: [email protected] Areas of Expertise: dynamics, neural networks, robotics, and intelligent control Special issue information: Topics for this call for survey/review papers include but not restricted to: Theoretical foundations of neural dynamics in intelligent computing (robustness, convergence, generalization, interpretability) Novel neural dynamics architectures and learning paradigms for intelligent computing Neural dynamics-based adaptive control, predictive control, and robust optimization Neural dynamics for multi-agent systems, distributed intelligence, and collaborative decision-making Neural dynamics for robotics: motion planning, autonomous navigation, and humanrobot interaction Neural dynamics in medical diagnosis, bio-signal analysis, and healthcare decision support Neural dynamics in financial forecasting, recommender system, risk management, and smart manufacturing Integration of neural dynamics with reinforcement learning, evolutionary algorithms, and signal processing techniques Real-time learning and deployment of neural dynamics in time-varying, uncertain, or safety-critical environments Emerging applications of neural dynamics-based intelligent computing in braincomputer interfaces, autonomous driving, soft robotics, and industrial intelligence Manuscript submission information: Important Dates: Submission Open Date: December 20, 2025 Submission Deadline: October 30, 2026 Final Acceptance Deadline: December 31, 2026 Prospective authors should follow standard author instructions for Neurocomputing and submit their manuscripts online at Editorial Manager system. Authors must select “VSI: Neural Dynamics" 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: Neural dynamics, intelligent computing https://www.sciencedirect.com/special-issue/328893/neural-dynamics-in-intelligent-computing-and-applications
由 Dou Sun 最后更新于

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