Pattern classification based on a multi-spike learning algorithm in a photonic spiking neural network with VCSEL-SA

doi:10.1364/AO.574743

Pattern classification based on a multi-spike learning algorithm in a photonic spiking neural network with VCSEL-SA

2026-02-10

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DOI：10.1364/AO.574743

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In this paper, we propose a pattern classification method based on the modified multi-spike Tempotron-like ReSuMe algorithm in a VCSEL-SA-based photonic spiking neuron network. Based on the multi-spike triggering mechanism, the proposed method can capture the global information to overcome the limitation of the traditional single-spike triggering algorithm, which can be used to effectively process more complex temporal information tasks, accompanied by good robustness to noise. The pattern classification task for the digits 1 to 4 demonstrates the superior performance of the proposed method in the information processing task. By adopting the bias current management strategy for the post-synaptic neuron, we can further improve the network's noise robustness. Moreover, this proposed method is validated in a pattern classification task in the Wisconsin Breast Cancer (WBC) dataset, and a classification accuracy of 95.6% can be achieved. (c) 2026 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

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Pattern classification based on a multi-spike learning algorithm in a photonic spiking neural network with VCSEL-SA

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