In today’s data-driven world, the ability to process large data volumes is crucial. Key tasks, such as pattern recognition and image classification, are well suited for artificial neural networks (ANNs) inspired by the brain. Neuromorphic computing approaches aimed towards physical realizations of ANNs have been traditionally supported by micro-electronic platforms, but recently, photonic techniques for neuronal emulation have emerged given their unique properties (e.g. ultrafast operation, large bandwidths, low cross-talk). Yet, hardware-friendly systems of photonic spiking neurons able to perform processing tasks at high speeds and with continuous operation remain elusive. This work provides a first experimental report of Vertical-Cavity Surface-Emitting Laser-based spiking neurons demonstrating different functional processing tasks, including coincidence detection and pattern recognition, at ultrafast rates. Furthermore, our approach relies on simple hardware implementations using off-the-shelf components. These results therefore hold exciting prospects for novel, compact and high-speed neuromorphic photonic platforms for future computing and Artificial Intelligence systems.

在当今数据驱动的世界中，处理大量数据的能力至关重要。模式识别和图像分类等关键任务非常适合大脑激发的人工神经网络（ANN）。传统上，微电子平台支持旨在实现ANN物理实现的神经形态计算方法，但近来，由于其独特的特性（例如超快操作，大带宽，低串扰），出现了用于神经元仿真的光子技术。然而，能够以高速且连续操作执行处理任务的光子尖刺神经元的硬件友好系统仍然难以捉摸。这项工作提供了基于垂直腔表面发射激光的尖峰神经元的第一份实验报告，展示了不同的功能处理任务，包括巧合检测和模式识别，速度超快。此外，我们的方法依靠使用现成组件的简单硬件实现。因此，这些结果为未来的计算和人工智能系统的新型，紧凑和高速神经形态光子平台带来了令人兴奋的前景。