MNEMOSYNE

At the frontier between integrative and computational neuroscience, we propose to model the brain as a system of active memories in synergy and in interaction with the internal and external world and to simulate it as a whole and in situation. Major cognitive and behavioral functions (eg. attention, recognition, planning, decision) emerge from adaptive sensorimotor loops involving the external world, the body and the brain. We study, model and implement such loops and their interactions toward a fully autonomous behavior. With such a “systemic” approach, we mean that such complex systems can only be truly apprehended as a whole and in natural behavioral situation. To design the functioning and learning characteristics of such models at the level of the neuronal circuitry and to implement them in systems interacting in loops with the world, we combine principles, methods and tools from different fields of science.

• We model the main cerebral structures and flows of information in the brain (as in integrative and cognitive neuroscience), stressing the links between brain, body and environment (embodied cognition).
• We use distributed computing formalisms allowing us to implement such models at different levels of description (as in computational neuroscience).
• We deploy our models at large scale (high performance computing), incarnate them in bodies interacting with the environment (autonomous robotics) and simulate them interactively with respect to events encountered by a (virtual/real) robot.

Not only do we expect to share back such an integrative approach among these different fields of science, but beyond, we also aim at contributing to other related areas of both life sciences (neuroscience, medicine) and digital science (computer science, machine learning).