Neuromorphic Computing: Devices, Architectures, and Algorithms for Brain-Inspired Intelligence

The human brain performs perception, learning, and decision-making with remarkable energy efficiency, motivating the development of computing systems that emulate biological neural principles. Neuromorphic computing has, therefore, become an important interdisciplinary field linking neuroscience, nanotechnology, electronics, and artificial intelligence. Recent progress in emerging memory devices has created new opportunities to implement artificial synapses and neurons using nanoscale materials and device physics. Memristive, resistive-switching, phase-change, ferroelectric, spintronic, electrochemical, and other memory technologies can support non-volatility, analog state tuning, plasticity, and local information storage. At the same time, advances in crossbar arrays, in-memory computing, spiking neural networks, and hardware-aware algorithms are moving neuromorphic systems from proof-of-concept devices toward integrated intelligent platforms.The goal of this Research Topic is to address the urgent need for energy-efficient, scalable, and adaptive computing technologies for next-generation artificial intelligence. Current AI hardware faces major challenges associated with data movement, memory bottlenecks, high power consumption, and limited ability to support real-time learning at the edge. Neuromorphic computing can help overcome these barriers by combining computation and memory, exploiting parallelism, and implementing brain-inspired learning mechanisms directly in hardware.This collection aims to gather research that spans the full neuromorphic stack: nanoscale memory devices, artificial synapses and neurons, circuits, architectures, learning algorithms, and application demonstrations. By creating a common platform for device engineers, materials scientists, circuit designers, AI researchers, and system architects, the Research Topic will accelerate translation of emerging memory technologies into practical brain-inspired intelligent systems.This Research Topic welcomes Original Research, Brief Research Reports, Methods, Reviews, Mini Reviews, Perspectives, Technology and Code articles. Topics of interest include but are not limited to the following:• Emerging memory devices for synapses and neurons• Memristors, RRAM, CBRAM, PCM, FeRAM/FeFET, MRAM, ECRAM, flash, charge-trap, organic, polymer, two-dimensional, optoelectronic, and flexible memories• Device physics, materials, fabrication, and characterization• Analog switching, stochasticity, plasticity, retention, endurance, variability, and reliability• Crossbar arrays, selectors, and peripheral circuits• CMOS-memory co-integration• In-memory and near-memory computing• Spiking and artificial neural networks• Hardware-aware algorithms• Neuromorphic sensing and edge AI• Benchmarking, modeling, and design automation• System demonstrations in robotics, healthcare, autonomous systems, and intelligent electronics Topic editors: Muhammd Umair Khan, Haider Abbas, Yawar Abbas Source: https://www.frontiersin.org/research-topics/83611/neuromorphic-computing-devices-architectures-and-algorithms-for-brain-inspired-intelligence