The development of optoelectronics mimicking the functions of the biological nervous system is important to artificial intelligence. This work demonstrates an optoelectronic, artificial, afferent-nerve strategy based on memory-electroluminescence spikes, which can realize multiple action-potentials combination through a single optical channel. The memory-electroluminescence spikes have diverse morphologies due to their history-dependent characteristics and can be used to encode distributed sensor signals. As the key to successful functioning of the optoelectronic, artificial afferent nerve, a driving mode for light-emitting diodes, namely, the non-carrier injection mode, is proposed, allowing it to drive nanoscale light-emitting diodes to generate a memory-electroluminescence spikes that has multiple sub-peaks. Moreover, multiplexing of the spikes can be obtained by using optical signals with different wavelengths, allowing for a large signal bandwidth, and the multiple action-potentials transmission process in afferent nerves can be demonstrated. Finally, sensor-position recognition with the bio-inspired afferent nerve is developed and shown to have a high recognition accuracy of 98.88%. This work demonstrates a strategy for mimicking biological afferent nerves and offers insights into the construction of artificial perception systems.

模仿生物神经系统功能的光电子学的发展对人工智能非常重要。这项工作展示了一种基于记忆电致发光尖峰的光电人工传入神经策略，可以通过单个光通道实现多个动作电位组合。记忆电致发光尖峰由于其依赖于历史的特性而具有不同的形态，并且可用于对分布式传感器信号进行编码。作为光电人工传入神经成功发挥作用的关键，提出了一种发光二极管的驱动模式，即非载流子注入模式，使其能够驱动纳米级发光二极管产生记忆电致发光具有多个子峰的尖峰。此外，可以通过使用不同波长的光信号来获得尖峰的复用，从而允许大信号带宽，并且可以演示传入神经中的多个动作电位传输过程。最后，开发了利用仿生传入神经的传感器位置识别，并显示出具有 98.88% 的高识别准确率。这项工作展示了一种模仿生物传入神经的策略，并为人工感知系统的构建提供了见解。