Recently, models in Computational Cognitive Neuro-science (CCN) have gained a renewed interest because they could help analyze current limitations in Artificial Intelligence (AI) and propose operational ways to address them. These limitations are related to difficulties in giving a semantic grounding to manipulated concepts , in coping with high dimensionality and in managing uncertainty. In this paper, we describe the main principles and mechanisms of these models and explain that they can be directly transferred to Computational Creativity (CC), to propose operational mechanisms but also a better understanding of what creativity is.