The recent surge in artificial intelligence (AI) and spiking neural network (SNN) has generated a burgeoning interest among scholars in investigating artificial spiking neurons. This exploration holds immense promise in propelling robots toward attaining human-level intelligence, as well as facilitating autonomous learning and adaptive control. Conventional electronic devices lack the nuanced nonlinear behaviors inherent in neuromorphic systems, necessitating convoluted circuitry and a multitude of components to replicate even the simplest functions of biological neurons, all while consuming substantial amounts of power. Consequently, researchers have turned to the operational principles of biological neurons, devising novel artificial spiking neuron models leveraging emerging technologies like memristors. These innovative artificial spiking neurons offer notable advantages such as reduced power consumption, streamlined structures, and well-established fabrication processes, leading to substantial strides in emulating diverse biological neuron functions. This study comprehensively reviewed and analyzed various implementation strategies for artificial spiking neurons utilizing state-of-the-art neuromorphic devices. Moreover, it delved into the practical application of artificial spiking neurons in achieving neuromorphic perceptual capabilities encompassing touch, vision, olfaction, taste, audition, and temperature sensing. Finally, this work contemplated the future trajectory of this field, envisioning potential advancements and trends. By offering valuable insights and perspectives, this paper aims to serve as a reference guide, inspiring further research and practical utilization of artificial spiking neurons.