A Photoelectric Spiking Neuron for Visual Depth Perception. Issue 20 (11th April 2022)

• Record Type: Journal Article
• Authors: Chen, Chunsheng; He, Yongli; Mao, Huiwu; Zhu, Li; Wang, Xiangjing; Zhu, Ying; Zhu, Yixin; Shi, Yi; Wan, Changjin; Wan, Qing
• Is Part Of: Advanced materials. Volume 34:Issue 20(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-04-11
• Subjects: artificial visual systems -- leaky integrate‐and‐fire neurons -- photoelectric spiking neuron -- TaOX memristors; Materials -- Periodicals; Chemical vapor deposition -- Periodicals; 620.11
• DOI: 10.1002/adma.202201895
• Languages: English
• ISSNs: 0935-9648

Abstract

Abstract: The biological visual system encodes optical information into spikes and processes them by the neural network, which enables the perception with high throughput of visual processing with ultralow energy budget. This has inspired a wide spectrum of devices to imitate such neural process, while precise mimicking such procedure is still highly required. Here, a highly bio‐realistic photoelectric spiking neuron for visual depth perception is presented. The firing spikes generated by the TaO X memristive spiking encoders have a biologically similar frequency range of 1–200 Hz and sub‐micro watts power. Such spiking encoder is integrated with a photodetector and a network of neuromorphic transistors, for information collection and recognition tasks, respectively. The distance‐dependent response and eye fatigue of biological visual systems have been mimicked based on such photoelectric spiking neuron. The simulated depth perception shows a recognition improvement by adapting to sights at different distances. The results can advance the technologies in bioinspired or robotic systems that may be endowed with depth perception and power efficiency at the same time. Abstract : This work reports a photoelectric spiking neuron for visual depth perception based on the Ag/TaO x /indium tin oxide memristive spiking encoder with high biological plausibility as well as ultralow power consumption. The biological visual systems are built and can estimate the depth of field based on the spiking rate difference, and consequently adapt its focus to optimize performances in visual tasks.