Brain-inspired neural networks: neuromorphic devices and their practical applications

Neuromorphic devices are revolutionizing the field of artificial intelligence (AI) by emulating the neural structure and computational efficiency of the human brain. These devices offer a new computing paradigm that integrates processing and memory, sidestepping the constraints of traditional von Neumann architecture. With capabilities like synaptic plasticity and energy efficiency, neuromorphic devices hold the promise of transforming AI systems into more powerful, adaptive, and efficient platforms. This review focuses on the advanced materials and their applications in neuromorphic devices, such as memristors, ferroelectrics, phase change materials and ionic conductor are at the forefront, enabling the simulation of synaptic weights and the potential for hardware-implemented neural networks. Despite challenges in device uniformity and system-level integration, continuous research and development are pushing the boundaries, aiming to fully realize the potential of neuromorphic computing hardwares.