Conventional intelligent systems based on deep neural network (DNN) models encounter challenges in achieving human-like continual learning due to catastrophic forgetting. Here, we propose a metaplasticity model inspired by human working memory, enabling DNNs to perform catastrophic forgetting-free continual learning without any pre- or post-processing. A key aspect of our approach involves implementing distinct types of synapses from stable to flexible, and randomly intermixing them to train synaptic connections with different degrees of flexibility. This strategy allowed the network to successfully learn a continuous stream of information, even under unexpected changes in input length. The model achieved a balanced tradeoff between memory capacity and performance without requiring additional training or structural modifications, dynamically allocating memory resources to retain both old and new information. Furthermore, the model demonstrated robustness against data poisoning attacks by selectively filtering out erroneous memories, leveraging the Hebb repetition effect to reinforce the retention of significant data.

翻译：基于深度神经网络（DNN）模型的传统智能系统，由于灾难性遗忘问题，在实现类人持续学习方面面临挑战。本文提出一种受人类工作记忆启发的元可塑性模型，使DNN能够在不进行任何预处理或后处理的情况下，执行无灾难性遗忘的持续学习。我们方法的一个关键方面在于实现从稳定到灵活的不同类型突触，并随机混合它们以训练具有不同灵活度的突触连接。该策略使得网络即使在输入长度意外变化的情况下，也能成功学习连续的信息流。该模型在无需额外训练或结构修改的情况下，实现了记忆容量与性能之间的平衡权衡，动态分配记忆资源以同时保留新旧信息。此外，该模型通过选择性过滤错误记忆，并利用赫布重复效应来强化重要数据的保留，展现了对数据投毒攻击的鲁棒性。