STDP-Based Unsupervised Spike Pattern Learning in a Photonic Spiking Neural Network With VCSELs and VCSOAs

We propose a photonic spiking neural network (SNN) consisting of photonic spiking neurons based on vertical-cavity surface-emitting lasers (VCSELs). The photonic spike timing dependent plasticity (STDP) is implemented in a vertical-cavity semiconductor optical amplifier (VCSOA). A versatile computational model of the photonic SNN is presented based on the rate equation models. Through numerical simulation, a spike pattern learning and recognition task is performed based on the photonic STDP. The results show that the post-synaptic spike timing (PST) is eventually converged iteratively to the first spike timing of the input spike pattern via unsupervised learning. Additionally, the convergence rate of the PST can be accelerated for a photonic SNN with more pre-synaptic neurons. The effects of VCSOA parameters on the convergence performance of the unsupervised spike learning are also considered. To the best of our knowledge, such a versatile computational model of photonic SNN for unsupervised learning and recognition of arbitrary spike pattern has not yet been reported, which would contribute one step forward toward numerical implementation of a large-scale energy-efficient photonic SNN, and hence is interesting for neuromorphic photonic systems and spiking information processing.