让脉冲神经网络同时用多种学习机制，提升性能与适应性。
Multi-Plasticity Synergy with Adaptive Mechanism Assignment for Training Spiking Neural Networks
- 引入多种协同的突触可塑性机制，模拟大脑多策略学习。
- 在静态图像和动态神经形态数据集上均显著提升精度与鲁棒性。
- 适合研究类脑计算、低功耗神经网络的开发者参考。
脉冲神经网络（SNN）是受大脑启发的低功耗模型，具备优异的时序处理潜力，但合适的训练机制仍难确定。尽管大脑中存在多种共存的学习策略，现有SNN训练方法通常仅依赖单一突触可塑性机制，限制了其适应性和表征能力。本文提出一种生物启发的训练框架，融合多种协同的可塑性机制，使不同学习算法能协同调节信息积累，同时保持各自独立的更新动态。我们在静态图像和动态神经形态数据集上评估该方法，结果表明相比传统单机制模型，本框架显著提升了性能与鲁棒性。该工作为基于多策略类脑学习构建更强大的SNN提供了通用且可扩展的基础。
原文摘要 · Abstract (English)
Spiking Neural Networks (SNNs) are promising brain-inspired models known for low power consumption and superior potential for temporal processing, but identifying suitable learning mechanisms remains a challenge. Despite the presence of multiple coexisting learning strategies in the brain, current SNN training methods typically rely on a single form of synaptic plasticity, which limits their adaptability and representational capability. In this paper, we propose a biologically inspired training framework that incorporates multiple synergistic plasticity mechanisms for more effective SNN training. Our method enables diverse learning algorithms to cooperatively modulate the accumulation of information, while allowing each mechanism to preserve its own relatively independent update dynamics. We evaluated our approach on both static image and dynamic neuromorphic datasets to demonstrate that our framework significantly improves performance and robustness compared to conventional learning mechanism models. This work provides a general and extensible foundation for developing more powerful SNNs guided by multi-strategy brain-inspired learning.
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