Continual learning aims to empower artificial intelligence (AI) with strong adaptability to the real world. For this purpose, a desirable solution should properly balance memory stability with learning plasticity, and acquire sufficient compatibility to capture the observed distributions. Existing advances mainly focus on preserving memory stability to overcome catastrophic forgetting, but remain difficult to flexibly accommodate incremental changes as biological intelligence (BI) does. By modeling a robust Drosophila learning system that actively regulates forgetting with multiple learning modules, here we propose a generic approach that appropriately attenuates old memories in parameter distributions to improve learning plasticity, and accordingly coordinates a multi-learner architecture to ensure solution compatibility. Through extensive theoretical and empirical validation, our approach not only clearly enhances the performance of continual learning, especially over synaptic regularization methods in task-incremental settings, but also potentially advances the understanding of neurological adaptive mechanisms, serving as a novel paradigm to progress AI and BI together.

翻译：持续学习旨在赋予人工智能（AI）对现实世界的强大适应能力。为此，理想的解决方案应恰当平衡记忆稳定性与学习可塑性，并获得足够的兼容性以捕捉观测分布。现有研究主要侧重于通过保持记忆稳定性来克服灾难性遗忘，但仍难以像生物智能（BI）那样灵活适应增量变化。通过建模一个具有多个学习模块、主动调控遗忘的健壮果蝇学习系统，我们提出了一种通用方法：一方面在参数分布中适当衰减旧记忆以提升学习可塑性，另一方面协调多学习器架构以确保解决方案的兼容性。通过广泛的理论与实证验证，我们的方法不仅显著提升了持续学习的性能（特别是在任务增量场景下优于突触正则化方法），还有望推进对神经自适应机制的理解，成为促进AI与BI协同发展的新型范式。