Multi‐Mode Artificial Vision Neurons for High Precision Image Recognition

ABSTRACT Real‐time edge perception is increasingly constrained by conventional frame‐based imaging and von Neumann processing, in which the continuous acquisition of dense image streams incurs excessive bandwidth demands, memory traffic, and energy consumption, ultimately limiting response speed and scalability. Neuromorphic vision offers a compelling alternative by suppressing redundancy and encoding salient visual dynamics into sparse spikes. However, most reported vision neuron systems require peripheral signal conditioning and analog‐to‐digital conversion, thereby introducing substantial latency and power overhead. Here, we report a reconfigurable multi‐mode artificial visual neuron based on Ta 2 O 5 /PDVT‐10‐CsPbBr 3 quantum dots that integrates retina‐inspired and cortex‐inspired spiking behaviors within a single optoelectronic device. The neuron supports bias‐free optical Leaky Integrate‐and‐Fire spiking, remaining effectively quiescent under static scenes while directly converting light‐intensity variations into single‐spike coding at the source, enabling rapid, energy‐efficient event‐based representation. In addition, its electrically accessible dynamics provide further programmability for cortex‐like processing and learning. The system demonstrates high recognition accuracy, highlighting the potential of Ta 2 O 5 ‐based artificial visual neurons for compact, low‐latency, always‐on machine vision in edge intelligence.