Advances in Neuromorphic Systems and Hardware: Circuits, Architectures, and Brain-Inspired Computation

Recent advances in materials, devices, and system architectures are driving a new generation of computing beyond traditional CMOS, with neuromorphic engineering emerging as a pivotal field. The growing demand for energy-efficient, scalable, and application-tailored neural computation - in AI, biomedical signal processing, autonomous systems, and edge computing - motivates research into hardware that implements biologically inspired and event-driven information processing principles. Neuromorphic systems, devices, and architectures are enabling more robust, efficient, and brain-like computation, highlighting the need for interdisciplinary study and innovative engineering approaches.Foundational work has demonstrated neuromorphic circuits and brain-inspired hardware as promising routes for next-generation computing, yet challenges remain. These include developing devices and materials that support neural-inspired computation, designing architectures and circuits that efficiently implement neural principles, and translating device-level innovations into fully realized neuromorphic systems and real-time demonstrations. Progress has been made in memristive, spintronic, and photonic elements for synaptic-like behavior, in-memory computing for weight storage, and integration of analog, mixed-signal, and digital circuits for event-driven neural processing. Stochastic and probabilistic computing approaches that support neural-inspired inference, uncertainty representation, and biologically motivated decision-making are also of interest when implemented within neuromorphic system architectures.Contributions on novel devices, materials, or alternative computing paradigms are welcome only when they demonstrate clear integration into biologically inspired, neuromorphic circuits, architectures, or system-level implementations. Pure device- or material-focused studies without explicit neuromorphic relevance and system-level validation fall outside the scope of this Research Topic.This Research Topic provides a platform for high-quality contributions on neuromorphic and neural-inspired computation, with a primary focus on architecture, circuit design, and fully realized neuromorphic and probabilistic systems. By consolidating research across hardware, devices, and systems, the goal is to advance brain-inspired and uncertainty-aware computing while maintaining strong grounding in neuromorphic engineering principles.Topics include, but are not limited to:Neuromorphic and brain-inspired computing, including analog and hybrid analog/digital circuits implementing neural processesMemristive, spintronic, phase-change, and photonic devices integrated into neuromorphic circuits and architecturesIn-memory and near-memory computing for synaptic weight storage and event-driven neural processingReliability, variability, and robustness of neuromorphic hardware implementationsStochastic and probabilistic computing architectures and circuits for neural-inspired and event-driven computationEmbedded neuromorphic systems for sensory processing and event-based interfacingHardware accelerators that explicitly implement neuromorphic or neural-inspired computational principlesCMOS integration with neuromorphic devices and circuits, where such integration demonstrably supports neuromorphic architectures, neural-inspired computation, or full system-level implementationsSecurity primitives and architectural elements inspired by neuromorphic computationArticle Types: Original research, reviews, and perspectives are welcome. Topic editors: Hooman Farkhani, Tim Böhnert Source: https://www.frontiersin.org/research-topics/77146/advances-in-neuromorphic-systems-and-hardware-circuits-architectures-and-brain-inspired-computation