A quantum-inspired online spiking neural network for time-series predictions

Spiking neural networks (SNNs) are considered the most promising new generation of artificial neural networks, due to their superior dynamic structures and low energy consumption, resembling that of the biological brain. Recent studies have suggested that SNNs could benefit from online learning in dynamic scenarios involving temporal sequences. However, the network performance of traditional spiking encoding methods is significantly affected by noise. As such, this study proposes a quantum-inspired online spiking neural network (QiSNN), which combines a quantum particle swarm optimization algorithm and a Kalman filtering technique to smooth and denoise the original time-series data. Additionally, a novel adaptive threshold selection method is developed to determine the similarity between neurons in a repository. The resulting model is applied to a dataset from the Department of Environment, Food, and Rural Affairs (DEFRA) in the UK, and used to predict ozone and PM10 concentrations that characterize air quality. Experimental results demonstrate that the proposed QiSNN outperforms baseline models across multiple evaluation metrics.