Neuromorphic Computing: Architectures, Learning Mechanisms, Hardware Platforms and Emerging Applications

Neuromorphic computing is an emerging brain-inspired paradigm that enables efficient information processing by integrating computation and memory in massively parallel, event-driven architectures. Unlike traditional von Neumann systems, it is capable of alleviating—though not necessarily eliminating—constraints related to memory, power inefficiency and real-time learning latency, particularly in applications that are sparse, event-driven and time-sensitive designed for spike-based or in-memory computing; however, in applications that are dense, are throughput-oriented and involve batch processing, traditional computing systems may be more efficient. This review highlights key neuromorphic models, including spiking neural networks and biologically inspired learning rules such as spike-timing-dependent plasticity, which support adaptive and energy-efficient computation. It also discusses the evolution of neuromorphic hardware from digital platforms to analog and mixed-signal implementations using emerging technologies such as memristors and phase-change memory. Major applications and key challenges in neuromorphic computing are briefly outlined.