The dynamic vision sensor captures visual information as discrete events, enabling high-speed imaging with reduced data redundancy, but is limited by lack of color sensitivity, a speed-noise trade-off, and inefficient data transfer. Here we show an amphibian-inspired dynamic vision system (ADVS) based on ferroelectric field-effect transistors that emulates the hierarchical functions of amphibian retinas, including spectral perception, spatial preprocessing, and event-driven neural encoding. The ferroelectric transistors exhibit broadband photosensitivity (365–637 nm) and bidirectional photoresponses, enabling multichannel spectral recognition from ultraviolet to visible light. Device arrays further reproduce center–surround receptive-field-like processing, enhancing spatial contrast while suppressing background noise under weak illumination. Owing to the steep switching characteristics (SSmin = 53.8 mV dec−1) of the transistors, the system also supports microsecond-scale event-driven spiking responses. Combined with bioinspired hierarchical preprocessing framework and event-driven convolutional neural network, the ADVS achieves 96.5% accuracy in dynamic facial expression recognition and real-time multi-agent trajectory prediction with <5% error. Overcoming the color blindness, speed-noise trade-offs, and bandwidth bottlenecks of traditional dynamic vision sensors, Zhai et al. report a frog-inspired device combining multispectral perception, spatial preprocessing, and event-driven encoding for low noise visual recognition and real-time multi-objects trajectory prediction.
Zhai et al. (2026) studied this question.