Optoelectronic graded neurons for bioinspired in-sensor motion perception

Inspired by the visual systems of agile insects, Chen et al. emulate their graded neurons using optoelectronic devices to realize bioinspired in-sensor motion perception and demonstrate high recognition accuracy with limited computational resources. Motion processing has proven to be a computational challenge and demands considerable computational resources. Contrast this with the fact that flying insects can agilely perceive real-world motion with their tiny vision system. Here we show that phototransistor arrays can directly perceive different types of motion at sensory terminals, emulating the non-spiking graded neurons of insect vision systems. The charge dynamics of the shallow trapping centres in MoS2 phototransistors mimic the characteristics of graded neurons, showing an information transmission rate of 1,200 bit s(-1) and effectively encoding temporal light information. We used a 20 x 20 photosensor array to detect trajectories in the visual field, allowing the efficient perception of the direction and vision saliency of moving objects and achieving 99.2% recognition accuracy with a four-layer neural network. By modulating the charge dynamics of the shallow trapping centres of MoS2, the sensor array can recognize motion with a temporal resolution ranging from 10(1) to 10(6) ms.