Our team is working on the modeling of biologically inspired artificial neural networks. These models are derived from existing models in theoretical biology or built from collaborations with neuroscientists. They are used to endow robots with different cognitive capacities related to autonomous behavior, like perception, sensorimotor coordination, exploration, planning, reasoning, and other tasks linked to memory and internal representation building. As engineers, we have to adapt these models to have them effective on real robots and real tasks. The most difficult aspect in this task is to integrate different models designed for different cognitive aspects like for example feature extraction and planning. To do so, we have to wonder about the levels of time involved in behavioral schemes and to integrate the corresponding models accordingly. Now, as a feedback to biology, we propose to discuss these levels of time and to relate them first to biological mechanisms and second to fundamental properties like information coding, learning, planning, motivation.