We report on ultrafast artificial laser neurons and on their potentials for future neuromorphic (brain-like) photonic information processing systems. We introduce our recent and ongoing activities demonstrating controllable excitation of spiking signals in optical neurons based upon Vertical-Cavity Surface Emitting Lasers (VCSEL-Neurons). These spiking regimes are analogous to those exhibited by biological neurons, but at sub-nanosecond speeds (>7 orders of magnitude faster). We also describe diverse approaches, based on optical or electronic excitation techniques, for the activation/inhibition of sub-ns spiking signals in VCSEL-Neurons. We report our work demonstrating the communication of spiking patterns between VCSEL-Neurons towards future implementations of optical neuromorphic networks. Furthermore, new findings show that VCSEL-Neurons can perform multiple neuro-inspired spike processing tasks. We experimentally demonstrate photonic spiking memory modules using single and mutually-coupled VCSEL-Neurons. Additionally, the ultrafast emulation of neuronal circuits in the retina using VCSEL-Neuron systems is demonstrated experimentally for the first time to our knowledge. Our results are obtained with off-the-shelf VCSELs operating at the telecom wavelengths of 1310 and 1550 nm. This makes our approach fully compatible with current optical network and data centre technologies; hence offering great potentials for future ultrafast neuromorphic laser-neuron networks for new paradigms in brain-inspired computing and Artificial Intelligence.

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...

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We report experimentally and in theory on the controllable propagation of spiking regimes between two interlinked Vertical-Cavity Surface-Emitting Lasers (VCSELs). We show that spiking patterns generated in a first transmitter VCSEL (T-VCSEL) are communicated to a second receiver VCSEL (R-VCSEL) which responds by firing the same spiking response. Importantly, the spiking regimes from both devices had analogous temporal and amplitude characteristics, including equal number of spikes fired, same spike and inter-spike temporal durations and similar spike intensity properties. These responses are analogous to the spiking communication patterns of biological neurons yet at sub-nanosecond speeds, this is several (up to 8) orders of magnitude faster than the timescales of biological neurons. We have also carried out numerical simulations reproducing with high degree of agreement the experimental findings. These results obtained with inexpensive, commercially available VCSELs operating at important telecom wavelengths (1300nm) offer great prospects for the scaling of emerging VCSEL-based photonic neuronal models into network configurations for use in novel neuromorphic photonic systems. This offers high potentials for non-traditional computing paradigms beyond digital systems and able to operate at ultrafast speeds.

The ongoing growth of use-cases for artificial neural networks (ANNs) fuels the search for new, tailor-made ANN-optimized hardware. Neuromorphic (brain-like) computers are among the proposed highly promising solutions, with optical neuromorphic realizations recently receiving increasing research interest. Among these, photonic neuronal models based on vertical cavity surface emitting lasers (VCSELs) stand out due to their favourable properties, fast operation and mature technology. In this work, we experimentally demonstrate different strategies to encode information into ultrafast spiking events in a VCSEL-neuron. We evaluate how the strength of the input perturbations (stimuli) influences the spike activation time, allowing for spike latency input coding. Based on a study of refractory behaviour in the system, we demonstrate the capability of the VCSEL-neuron to perform reliable binary-to-spike information coding with spiking rates surpassing 1 GHz. We also report experimentally on neuro-inspired spike firing rate-coding with a VCSEL-neuron, where the strength of the input perturbation (stimulus) is continuously encoded into the spiking frequency (spike firing rate). With the prospects of neuromorphic photonic systems constantly growing, we believe the reported functionalities with the ultrafast spiking VCSEL-neurons provide a reliable, multifaceted approach for interfacing photonic neuromorphic platforms with existing computation and communication systems.