Title: Cognitively Inspired Learning of Incremental Drifting Concepts. Abstract:  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.