Humans continually expand their learned knowledge to new domains and learn new concepts without any interference with past learned experiences. In contrast, machine learning models perform poorly in a continual learning setting, where input data distribution changes over time. Inspired by the nervous system learning mechanisms, we develop a computational model that enables a deep neural network to learn new concepts and expand its learned knowledge to new domains incrementally in a continual learning setting. We rely on the Parallel Distributed Processing theory to encode abstract concepts in an embedding space in terms of a multimodal distribution. This embedding space is modeled by internal data representations in a hidden network layer. We also leverage the Complementary Learning Systems theory to equip the model with a memory mechanism to overcome catastrophic forgetting through implementing pseudo-rehearsal. Our model can generate pseudo-data points for experience replay and accumulate new experiences to past learned experiences without causing cross-task interference.

翻译：人类能够不断将所学知识扩展到新领域，并学习新概念而不干扰过去的学习经验。相比之下，机器学习模型在持续学习场景中表现不佳，其中输入数据分布会随时间变化。受神经系统学习机制的启发，我们开发了一种计算模型，使深度神经网络能够在持续学习场景中逐步学习新概念并将其知识增量扩展到新领域。我们基于并行分布式处理理论，以多模态分布的形式在嵌入空间中编码抽象概念。该嵌入空间由隐藏网络层中的内部数据表示建模。我们还利用互补学习系统理论，通过实现伪重放为模型配备记忆机制以克服灾难性遗忘。我们的模型能够生成用于经验重放的伪数据点，并将新经验累积到过去的学习经验中，而不会引起跨任务干扰。