With the exponential growth in data availability and advancements in computing power, the importance of intelligence computing in neural dynamics is increasingly evident. Intelligence computing has substantial application value in fields such as robotics, artificial intelligence, information technology, and intelligent manufacturing, enhancing capabilities in perception, decision-making, and execution. Neural dynamics, an interdisciplinary field resulting from the artificial intelligence, dynamical systems, and control theory, describes system states as neurons and focuses on their evolving dynamic behavior, with rigorous guarantees of convergence and stability. Serving as equations that describe the relationship between functions and their derivatives, differential equations possess rich mathematical analysis methods, making them integral to classical mathematical theory.

Intelligence computing on neural dynamics ingeniously integrates neural networks with differential equations, leveraging differential equations to represent physical processes and combining this with the robust fitting capabilities of neural networks, make it a powerful modeling approach applied across fields such as engineering, physics, economics and so on. Compared to traditional neural networks, which overlook physical information and rely solely on numerous neurons for fitting, intelligence computing on neural dynamics achieves higher accuracy with fewer neurons, while ensuring robustness, generalization, and interpretability within the learned systems. However, fully realizing the potential of intelligence computing on neural dynamics requires addressing a series of technological challenges, including model and data noise, safety, cost, generalization, real-time applicability, and interpretability. Efficiency remains paramount in the face of these multifaceted challenges, reflecting the unique and evolving landscape of intelligence computing in neural dynamics.

The primary aim of this special issue is to encourage researchers to publish their latest work on the challenges and solutions of intelligence computing in neural dynamics, with applications in emerging technologies such as soft robotics, autonomous driving, multi-robot control, brain-computer interface technology, and electroencephalogram analysis. Submissions should include original and unpublished works focusing on but not limited to these areas. The proposed submissions and presentations should be original and unpublished works including but not limited to

• Theory of neural dynamics for convergence, robustness, and other characteristics
• Innovative design of neural dynamics (for improving performance, continuous-time/discrete-time systems, software/hardware implementation, etc.)
• Robust control algorithms and techniques based on robot
• Neural dynamics for adaptive control and model predictive control strategy
• Machine learning and deep learning techniques for robot control
• Applications of neural dynamics including robotics, signal processing and analysis, multi-agent systems, and other fields of industrial intelligence

SUBMISSION GUIDELINES

Papers submitted to this journal for possible publication must be original and must not be under consideration for publication in any other journals. Prospective authors should submit an electronic copy of their completed manuscript to https://mc03.manuscriptcentral.com/tst with manuscript type as “Special Issue on Intelligence Computing on Neural Dynamics with Applications”. Further information on the journal is available at: https://www.sciopen.com/journal/1007-0214.

IMPORTANT DATES

Submission deadline: July 31, 2025

GUEST EDITORS

Prof. Long Jin, Lanzhou University

Prof. Mei Liu, The Chinese University of Hong Kong