The Rise of Adaptive Spiking Visual Neuron

Vision, as a core modality for environmental perception, has continuously inspired numerous technological upgrades to enable machines to perceive like humans. However, most existing machine vision systems rely on frame‐based sensors and von Neumann processing architectures, which lead to severe overhead due to redundant data and frequent data transfer, exhibiting low adaptability in dynamic environments. In contrast, biological visual systems encode visual information as event flows based on spike trains and employ a series of long‐evolved adaptive mechanisms to achieve efficient and robust perception. In recent years, adaptive visual neurons (AVNs) based on neuromorphic components have been explored for directly embedding adaptability at the sensing frontend. More recently, the adaptive spiking visual neuron (ASVN) has emerged, which aims to introduce the spike encoding mechanism into AVNs, expecting to further enhance their efficiency and robustness. This perspective outlines the rapidly advancing ASVNs through reviewing adaptive mechanisms in biological systems and their implications for machine vision, summarizing the shift from AVNs to ASVNs, and highlighting potential opportunities and challenges for integrating ASVNs into promising application scenarios for next‐generation machine vision systems.