  Photonic technologies offer great prospects for novel ultrafast,
energy-efficient and hardware-friendly neuromorphic (brain-like) computing
platforms. Moreover, neuromorphic photonic approaches based upon ubiquitous,
technology-mature and low-cost Vertical-Cavity Surface Emitting Lasers (VCSELs)
(devices found in fibre-optic transmitters, mobile phones, automotive sensors,
etc.) are of particular interest. Given VCSELs have shown the ability to
realise neuronal optical spiking responses (at ultrafast GHz rates), their use
for spike-based information processing systems has been proposed. In this work,
Spiking Neural Network (SNN) operation, based on a hardware-friendly photonic
system of just one Vertical Cavity Surface Emitting Laser (VCSEL), is reported
alongside a novel binary weight 'significance' training scheme that fully
capitalises on the discrete nature of the optical spikes used by the SNN to
process input information. The VCSEL-based photonic SNN is tested with a highly
complex, multivariate, classification task (MADELON) before performance is
compared using a traditional least-squares training method and the alternative
novel binary weighting scheme. Excellent classification accuracies of >94% are
reached by both training methods, exceeding the benchmark performance of the
dataset in a fraction of processing time. The newly reported training scheme
also dramatically reduces training set size requirements as well as the number
of trained nodes (<1% of the total network node count). This VCSEL-based
photonic SNN, in combination with the reported 'significance' weighting scheme,
therefore grants ultrafast spike-based optical processing with highly reduced
training requirements and hardware complexity for potential application in
future neuromorphic systems and artificial intelligence applications.
