Being able to emulate the human brain (or approximations or parts of it) is one of the next major biological/technological breakthroughs that will have impacts in our daily life (health, autonomous machines, artificial intelligence, etc.). If recent progresses in machine learning have roots in bio-inspired techniques, mostly dated from the 70's, they usually represent very simple and specific structures without catching the essence of structure-function brain mechanisms. In machine learning, computer scientists build usually bio-inspired algorithms but not transdisciplinary biocognitive algorithms. In biology and cognition, researches are limited by the lack of experimental data (only lacunar data can be collected on animals and even less on humans). Our main goal is to develop a computational brain tool as an intermediate layer between high-level features (behavioral and cognitive functions at the human level) and low-level features (biological layers studied in mice, i.e., an animal brain that can be a model of human brain, as well as modifications observed in pathologies).