The cone photoreceptors in our eyes selectively transduce the natural light into spiking representations, which endows the brain with high energy-efficiency color vision. However, the cone-like device with color-selectivity and spike-encoding capability remains challenging. Here, we propose a metal oxide-based vertically integrated spiking cone photoreceptor array, which can directly transduce persistent lights into spike trains at a certain rate according to the input wavelengths. Such spiking cone photoreceptors have an ultralow power consumption of less than 400 picowatts per spike in visible light, which is very close to biological cones. In this work, lights with three wavelengths were exploited as pseudo-three-primary colors to form ‘colorful’ images for recognition tasks, and the device with the ability to discriminate mixed colors shows better accuracy. Our results would enable hardware spiking neural networks with biologically plausible visual perception and provide great potential for the development of dynamic vision sensors.

The human brain that relies on neural networks communicated by spikes is featured with ultralow energy consumption, which is more robust and adaptive than any digital system. Inspired by the spiking framework of the brain, spike-based neuromorphic systems have recently inspired intensive attention. Therefore, neuromorphic devices with spike-based synaptic functions are considered as the first step toward this aim. Photoelectric neuromorphic devices are promising candidates for spike-based synaptic devices with low latency, broad bandwidth, and superior parallelism. Here, the indium-gallium-zinc-oxide-based photoelectric neuromorphic transistors are fabricated for Morse coding based on spike processing, 405-nm light spikes are used as synaptic inputs, and some essential synaptic plasticity, including excitatory postsynaptic current, short-term plasticity, and high-pass filtering, can be mimicked. More interestingly, Morse codes encoded by light spikes are decoded using our devices and translated into amplitudes. Furthermore, such devices are compatible with standard integrated processes suitable for large-scale integrated neuromorphic systems.

Neuromorphic perceptual system (NPS) is inspired by the interaction between organisms and environment, which has boosted many emerging fields like human–machine interfaces and bionic robots. Laser‐induced graphene (LIG) can be a fast, low cost, and accurate patterning technology toward a flexible and biological plausible NPS. While, to unleash the full potential of LIG, thorny issues like low endurance and limited stretchability should be addressed. Herein, high‐performance ultraviolet LIG (UV‐LIG) based sensors and electrodes are introduced for building flexible artificial sensory neuron (ASN). The UV‐LIG can be fabricated with a fine linewidth of ≈75 µm and a high conductivity of 3900 ± 150 S cm−1, which facilitates the demonstration of LIG toolbox, containing bending sensors, flexible heaters, etc. The transferred LIG electrodes retain good conducting property (only ≈12.7% increment in resistance) and enable large stretchability (up to 150% strain), outperforming most of the LIG‐based strain sensors. As a proof‐of‐concept, an artificial sensory neuron that is able to mimic the strain perception of somatosensory system is realized based on the integration of LIG‐based functional components and an InGaZnO (Indium‐Gallium‐Zinc Oxide, IGZO)‐based synaptic transistor. This work can provide an efficient patterning methodology as well as essential components for neuromorphic perceptual systems.

The cone photoreceptors in our eyes selectively transduce the natural light into spiking representations, which endows the brain with high energy-efficiency color vision. However, the cone-like device with color-selectivity and spike-encoding capability remains challenging. Here, we propose a vertical integrated spiking cone photoreceptor (VISCP) array with the structure of ITO/Ta2O5/Ag/IGZO/ITO, which can directly transduce persistent lights into spike trains at a certain rate according to the input wavelengths. The VISCPs have an ultralow power consumption of ≤400 pW per spike in visible light, which is very close to biological cones. In this work, lights with three wavelengths were exploited as pseudo three primary colors to form ‘colorful’ images for recognition tasks, and the device with the ability to discriminate mixed colors shows a better accuracy. Our results would enable hardware spiking neural networks with biologically plausible visual perception and provide great potential for the development of dynamic vision sensors.