Fig. 1: Owl-inspired Neuromorphic Near-sensor Computing.
a Owl optic neurobiological synaptic transmission. Spatiotemporal integration of spiking signals (postsynaptic currents, PSC) via neurotransmitter release and synaptic weight regulation. b Artificial neural system implementation. Near-sensor computing architecture (left) simulates biological signal perception and processing, and the artificial neural network (right) simulates synaptic plasticity through programmable weights. c Schematic of light adaptation thresholds. Comparison of light sensitivity ranges of complementary metal-oxide semiconductor (CMOS) photoreceptor devices (~1 lux, corresponding to a light intensity of ~10−7 W cm−2 @ 555 nm), human nocturnal vision (10−2 to 10−1 lux, corresponding to a light density of approximately 10−9–10−8 W cm−2 @ 555 nm), and barn owl nocturnal vision (~10−3 lux, corresponding to a light intensity of about 10−10 W cm−2 @ 555 nm). d Schematic of the adaptive mechanism of nighttime photoreception in owls. As time-dependent photosensitivity (Pt) increases with increasing light duration, dark adaptation of retinal optic rod cells leads to progressively clearer target imaging. e Schematic of autonomous unmanned aerial vehicle applications: integration of optical sensing, visual adaptation and neuromorphic computing enables target detection in photon-starved environments without the auxiliary lighting and post-processing techniques.
