Neuromorphic computing can potentially solve the von Neumann bottleneck of current mainstream computing because it excels at self‐adaptive learning and highly parallel computing and consumes much less energy. Synaptic devices that mimic biological synapses are critical building blocks for neuromorphic computing. Inspired by recent progress in optogenetics and visual sensing, light has been increasingly incorporated into synaptic devices. This paves the way to optoelectronic synaptic devices with a series of advantages such as wide bandwidth, negligible resistance–capacitance (RC) delay and power loss, and global regulation of multiple synaptic devices. Herein, the basic functionalities of synaptic devices are introduced. All kinds of optoelectronic synaptic devices are then discussed by categorizing them into optically stimulated synaptic devices, optically assisted synaptic devices, and synaptic devices with optical output. Existing practical scenarios for the application of optoelectronic synaptic devices are also presented. Finally, perspectives on the development of optoelectronic synaptic devices in the future are outlined.

MoS2 is a typical two-dimensional material that has attracted considerable attention in the field of electromagnetic wave absorption because of its high specific surface area and excellent electrical and semiconductor...

Neuromorphic visual systems based on optoelectronic synaptic devices have been recently studied to simulate the retina and visual cortex of a human being. Now it is shown that an array of optoelectronic synaptic devices based on the two‐terminal structure of Si/perovskite/Au may mimic the functionalities of lateral geniculate nucleus (LGN) cells. Benefiting from the photovoltaic effect, the devices can work under a self‐powered mode. Diverse synaptic functionalities such as postsynaptic current, paired‐pulse facilitation/depression, spike duration‐dependent plasticity, spike number‐dependent plasticity, and spike rate‐dependent plasticity have been simulated. By modulating the electric bias of the devices in the array the simulation of the positional and orientational recognition of the LGN cells is demonstrated.