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Adaptation and learning of spatio-temporal thresholds in spiking neural networks

Neurocomputing 2025年 644卷

作者： Fu, Jiahui Gou, Shuiping Wang, Peizhao Jiao, Licheng Guo, Zhang Li, Jisheng Liu, Rong Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Artificial Intelligence Xidian University Shannxi Xi'an710071 China School of Physics and Information Technology Shaanxi Normal University Shannxi Xi'an710119 China

Spiking neural networks (SNNs) have attracted substantial attention in recent years due to their brain-inspired and event-driven characteristics. To mimic the behavior of biological neurons, the neuron in SNNs generates spikes to transmit information across the network once its membrane potential surpasses a certain firing threshold. Due to model complexity and computational challenges, the threshold is often set as a fixed value, which limits the rich dynamical features of neurons and is inconsistent with the dynamic nature of thresholds observed in biological systems. Additionally, treating the threshold as an optimized parameter presents challenges in achieving convergence and maintaining stability. Therefore, we introduce a spatio-temporal adjustment strategy for the firing threshold. We propose a Learnable Temporal Factor (LTF) to dynamically adapt the threshold over time and an Adaptive Learnable Spatial Factor (ALSF) to spatially extend the threshold. By coupling these factors with the neuronal dynamics, we achieve a stronger spike coding capacity by utilizing more information in the generation of spikes. Our experiments show that the proposed method yields remarkable performance on both static and neuromorphic datasets. Our code is available at ***/gzxdu/ST-Thresholds-SNN. © 2025

关键词： Dynamic vision sensor Spatio-temporal thresholds spike coding capacity Spiking neural networks

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