Photonic spiking neural network built with a single VCSEL for high-speed time series prediction

Abstract Photonic technologies hold significant potential for creating innovative, high-speed, efficient and hardware-friendly neuromorphic computing platforms. Neuromorphic photonic methods leveraging ubiquitous, technologically mature and cost-effective Vertical-Cavity Surface Emitting Lasers (VCSELs) are of notable interest. VCSELs have demonstrated the capability to replicate neuronal optical spiking responses at ultrafast rates. Previously, a photonic Spiking Neural Network (p-SNN) using a single VCSEL has been demonstrated for use in classification tasks. Here, it is applied to a more complex time-series prediction task. The VCSEL p-SNN combined with a technique to induce network memory, is applied to perform multi-step-ahead predictions of a chaotic time-series. By providing the feedforward p-SNN with only two temporally separated inputs excellent accuracy is experimentally demonstrated over a range of prediction horizons. VCSEL-based p-SNNs therefore offer ultrafast, efficient operation in complex predictive tasks whilst enabling hardware implementations. The inherent attributes and performance of VCSEL p-SNNs hold great promise for use in future light-enabled neuromorphic computing hardware.