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.

通过建立一个稳定性与可塑性均衡、具有多个学习模块的学习系统，我们提出了一种通用方法，可以适度减弱旧记忆在参数分布中的影响，以提高学习可塑性，并通过协调多学习者架构确保解决方案的兼容性。经过大量的理论和实证验证，我们的方法不仅明显提高了持续学习的性能，尤其是在任务增量设置中超过了突触正则化方法，还可能推进神经适应机制的理解，成为推动人工智能和生物智能共同进步的新范例。