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.

我们提出了一种受人类工作记忆启发的元可塑性模型，在没有任何预处理或后处理的情况下，使深度神经网络(DNN)在连续学习中实现了避免灾难性遗忘。该模型通过实施不同灵活程度的突触连接，并随机混合它们以训练连接具有不同灵活程度的神经元，实现了成功学习连续信息流的能力。该模型在记忆容量和性能之间取得了平衡，不需要额外的训练或结构修改，并动态分配内存资源以保留旧信息和新信息。此外，该模型通过选择性地过滤错误的记忆和利用赫布重复效应来增强重要数据的保留，展示了对数据中毒攻击的强大抵抗能力。

神经仿真元可塑性用于自适应持续学习