Multi-Synaptic Cooperation: A Bio-Inspired Framework for Robust and Scalable Continual Learning

Penghui Li, Zhuang Ma, Yunliang Zang, Qiang Yu

Keywords: Continual Learning, Catastrophic Forgetting, Brain-inspired Computing, Multi-Synaptic Cooperation

TL;DR: We propose the Multi-Synaptic Cooperative Network (MSCN) for continual learning. MSCN achieves higher accuracy and superior robustness compared to state-of-the-art baselines.

Abstract: Continual learning aims to acquire new knowledge incrementally while retaining prior information, with catastrophic forgetting (CF) being a central challenge. Existing methods can mitigate CF to some extent but are constrained by limited capacity, which often requires dynamic expansion for long task sequences and makes performance sensitive to task order. Inspired by the richness and plasticity of synaptic connections in biological nervous systems, we propose the Multi-Synaptic Cooperative Network (MSCN), a generalized framework that models cooperative interactions among multiple synapses through multi-synaptic connections modulated by local synaptic activity. This design enhances model representational capacity and enables task-adaptive plasticity by means of multi-synaptic cooperation, providing a new avenue for expanding model capacity while improving robustness to task order. During learning, our MSCN dynamically activates task-relevant synapses while suppressing irrelevant ones, enabling targeted retrieval and minimizing interference. Extensive experiments across four benchmark datasets, involving both spiking and non-spiking neural networks, demonstrate that our method consistently outperforms state-of-the-art continual learning methods with significantly improved robustness to task-order variation. Furthermore, our analysis reveals an optimal trade-off between synaptic richness and learning efficiency, where excessive connectivity can impair circuit performance. These findings highlight the importance of the multi-synaptic cooperation mechanism for achieving efficient continual learning and provide new insights into biologically inspired, robust, and scalable continual learning.

Primary Area: applications to neuroscience & cognitive science

Submission Number: 12202