Brain-Inspired Artificial Intelligence Across Domains: Models, Systems and Applications
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".
Deadline for manuscript submissions: 15 November 2026 | Viewed by 237
Editors
Interests: brain-inspired artificial intelligence; AI for systems; neuromorphic software and architecture
Special Issue Information
Dear Colleagues,
Recent advances in artificial intelligence have substantially accelerated the development of brain-inspired computing, computational neuroscience, and machine learning. At the same time, the growing demand for efficient, adaptive, and explainable intelligent systems has revealed important limitations of conventional deep learning paradigms, particularly in energy efficiency, biological plausibility, temporal information processing, continual adaptation, and deployment on resource-constrained platforms. This Special Issue aims to bring together recent advances in theoretical, algorithmic, and application-oriented research at the intersection of brain-inspired artificial intelligence, computational neuroscience, and machine learning.
The focus of this Special Issue is on new models, architectures, learning mechanisms, and system designs that are inspired by neural computation and cognitive principles while also contributing to the broader machine learning community. Relevant topics include, but are not limited to, spiking neural networks, neuromorphic computing, event-driven perception, biologically plausible learning, neural coding and temporal representation, brain-inspired optimization, hybrid neural–symbolic and brain-inspired learning frameworks, and hardware–software co-design for efficient intelligent systems. We also welcome studies on practical applications in healthcare, robotics, edge intelligence, autonomous systems, pattern recognition, and multimodal data analysis.
The purpose of this Special Issue is twofold. First, it seeks to provide a focused venue for disseminating cutting-edge research that bridges neuroscience-inspired principles and modern artificial intelligence. Second, it aims to promote interdisciplinary dialogue among researchers working in machine learning, computational neuroscience, neuromorphic engineering, embedded intelligence, and real-world intelligent services. By emphasizing both foundational methods and emerging applications, this collection seeks to help clarify how brain-inspired approaches can complement and extend existing AI methodologies.
This Special Issue aims to supplement the current literature by highlighting a rapidly expanding research direction that goes beyond traditional ANN-centered frameworks. While existing studies are often scattered across neuroscience, hardware design, and AI application domains, this collection hopes to provide an integrated platform for presenting advances in algorithms, systems, and practical deployments. Its objective is to offer timely insights into how brain-inspired intelligence can contribute to the next generation of robust, low-power, adaptive, and trustworthy intelligent systems.
Dr. Xin Du
Dr. Linshan Jiang
Guest Editors
Manuscript Submission Information
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Keywords
- brain-inspired artificial intelligence
- computational neuroscience
- machine learning
- spiking neural networks
- neuromorphic computing
- event-driven intelligence
- biologically inspired learning
- edge intelligence
- intelligent systems
- multimodal perception
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