Inspired by the dynamic visual perception of flying insects, rapid collision warning systems are crucial for advancing autonomous driving and machine control. Although neuromorphic devices show significant potential for replicating insect vision systems, they are hindered by limitations in the sensing frequency, signal-to-noise ratio, and flicker noise. Here, we use a combination of a homojunction and heterojunction to emulate the two different transmission modes of nerve signals via gate-voltage modulation. The structural design and heterojunction effects enabled artificial neurons to respond to high-frequency visible-light signals and achieve an information transmission rate of 2100 bits s-1. By connecting the leaky integrate-and-fire neural device in series with the synaptic device, we successfully generated action potentials and postsynaptic potential responses, significantly reducing cumulative threshold flicker noise. Using in-sensor reservoir computing, we achieved trajectory recognition across four car orientations with an optimized training process, providing valuable insights into device design and applications in visual bionics.
Xie et al. (2026) studied this question.