The recent surge in enthusiasm for cutting-edge artificial intelligence and neuromorphic computing paradigms, such as spiking neural networks (SNNs) and oscillatory neural networks (ONNs), has sparked significant interest. This has motivated researchers worldwide to delve into the study and realization of artificial spiking neurons. Conventional electronic devices, such as metal–oxide–semiconductor field-effect transistors (MOSFETs) and diodes, lack the subtle nonlinear behaviors inherent in neuromorphic systems, thus requiring complex circuits and many devices to replicate the simplest functions of biological neurons at the expense of substantial energy and hardware costs. Consequently, to emulate biological neurons, researchers are turning to novel artificial neuron devices, aiming to simplify structure, reduce power consumption, and lower hardware cost. This study provides a comprehensive review and benchmark of recent progress in artificial spiking neurons based on emerging electronic devices for neuromorphic perception and computation. It summarizes various artificial neurons using different spiking mechanisms, including Mott, ion diffusion, phase-change, and ovonic threshold switching (OTS) memristor neurons and transistor neurons. Furthermore, the work reviews practical applications in neuromorphic perception and computing. Finally, it discusses future directions and trends. By offering valuable insights, this work aims to serve as a reference guide, inspiring further research and practical use of artificial spiking neurons.