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.Neuromorphic computing has seen a surge in interest for data intense processing tasks for which brain-inspired artificial neural networks (ANNs) have proven very powerful 1 . Demand for Artificial Intelligence (AI) and machine learning systems, using ANNs for operation, has dramatically exploded with increasingly challenging applications (e.g. AI assistants, autonomous vehicles, big data, meteorological predictions and assistive robotics) 2-4 . However, traditional Von Neumann computing architectures struggle to achieve the efficiency and parallelism required to recreate complex ANNs 5 . Hence, neuromorphic computing platforms made up of artificial spiking neurons and synapses, supported by mature electronic technologies, have attracted increasing interest. Current systems, such as TrueNorth 6 , SpiNNaker 7 , Neurogrid 8 and HICANN 9 , have each demonstrated impressive performance. However, neuromorphic electronic realisations suffer from the same limitations as modern day microprocessors: slowed progress of Moore's law 10 and use of electrical signals (which inherently limits bandwidth, speed, communication distance and energy efficiency) 11 .Consequently, neuromorphic photonic approaches have started to emerge given their unique advantages, e.g. ultrafast performance, large bandwidths, low cross talk, and high parallelism. Light enabled operation of brain-inspired photonic systems means that speeds up to 9 order of magnitude faster than biological neurons, and crucially up to 6 orders of magnitude faster than electronic approaches 12 , can be achieved. Due to the appealing properties of these photonic systems, growing interest has given rise to many reports of electro-optic artificial synaptic devices 13,14 and spiking photonic neuronal models based on numerous different systems, e.g. phase change materials 15 , resonant tunnelling diodes...