Spike-based neural networks, which use spikes or action potentials to represent information, have gained a lot of attention because of their high energy efficiency and low power consumption. To fully leverage its advantages, converting the external analog signals to spikes is an essential prerequisite. Conventional approaches including analog-to-digital converters or ring oscillators, and sensors suffer from high power and area costs. Recent efforts are devoted to constructing artificial sensory neurons based on emerging devices inspired by the biological sensory system. They can simultaneously perform sensing and spike conversion, overcoming the deficiencies of traditional sensory systems. This review summarizes and benchmarks the recent progress of artificial sensory neurons. It starts with the presentation of various mechanisms of biological signal transduction, followed by the systematic introduction of the emerging devices employed for artificial sensory neurons. Furthermore, the implementations with different perceptual capabilities are briefly outlined and the key metrics and potential applications are also provided. Finally, we highlight the challenges and perspectives for the future development of artificial sensory neurons.

基于尖峰的神经网络使用尖峰或动作电位来表示信息，因其高能效和低功耗而受到广泛关注。为了充分利用其优势，将外部模拟信号转换为尖峰信号是必不可少的先决条件。包括模数转换器或环形振荡器以及传感器在内的传统方法存在高功率和面积成本高的问题。最近的努力致力于基于受生物感觉系统启发的新兴设备构建人工感觉神经元。它们可以同时进行传感和尖峰转换，克服了传统传感系统的不足。本文总结并基准了人工感觉神经元的最新进展。它首先介绍了生物信号转导的各种机制，然后系统地介绍了用于人工感觉神经元的新兴设备。此外，简要概述了具有不同感知能力的实现，并提供了关键指标和潜在应用。最后，我们强调了人工感觉神经元未来发展的挑战和前景。