FTP: Filtered Temporal-Population for time series encoding in Spiking Neural Network

Spiking Neural Networks (SNNs) offer promising solutions for efficient real-time processing of time-series data by closely emulating biological neuronal dynamics. However, existing encoding methods for converting raw input data into spike trains often introduce significant temporal distortions, complexity, or limitations in learnability, hindering their practical deployment. In this study, we propose the Filtered Temporal-Population (FTP) encoding method, a novel technique that integrates filtering operations into SNN encoding. FTP encoding effectively captures both temporal and spatial correlations within data segments while aligning inputs directly with the temporal axis, making it highly suitable for real-time applications. Evaluations on the MIT-BIH electrocardiogram dataset and other time-series datasets demonstrate that FTP encoding outperforms traditional encoding methods in terms of accuracy, speed, and robustness. Our findings highlight FTP encoding’s potential as a practical and effective solution for real-time SNN-based time-series classification tasks.