Multi-Plasticity Synergy with Adaptive Mechanism Assignment for Training Spiking Neural Networks

TL;DR

This paper introduces a biologically inspired training framework for Spiking Neural Networks that combines multiple plasticity mechanisms to enhance learning effectiveness, robustness, and adaptability beyond traditional single-mechanism approaches.

Contribution

It proposes a novel multi-plasticity synergy framework allowing diverse learning strategies to cooperatively improve SNN training and performance.

Findings

Significant performance improvements on static and dynamic datasets.

Enhanced robustness compared to single-mechanism models.

Demonstrates the effectiveness of multi-strategy brain-inspired learning.

Abstract

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.