Multi-Plasticity Synergy with Adaptive Mechanism Assignment for Training Spiking Neural Networks
This work addresses the problem of enhancing SNN training for researchers and practitioners in neuromorphic computing, though it appears incremental by building on existing brain-inspired learning strategies.
The paper tackles the challenge of limited adaptability in Spiking Neural Networks (SNNs) by proposing a biologically inspired training framework that incorporates multiple synergistic plasticity mechanisms, resulting in significant improvements in performance and robustness on static image and dynamic neuromorphic datasets compared to conventional models.
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.