MNEMOSYNE Research team
Team presentationAt 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.
Research themesIn our systemic approach, we are thus mainly interested in the design of loops between the brain, the body and the external world and of their interactions to promote autonomous behavior. We pay a special attention to the fact that different kinds of memories are elaborated in these loops and, consequently, that the observed phenomena result from information exchange between these memories.
- In so-called “Perception loops”, we explore cerebral circuits that allow to identify phenomena in the internal (bodily, neuronal) and external (sensory) worlds, towards the evaluation of the present state. Several kinds of learning are involved in the acquisition and representation of information allowing to discriminate states (cues, configurations, sequences, etc) that might participate in the decision for the future actions. The modeled structures correspond to the amygdala, the hippocampus and the sensory cortex.
- In so-called "Action loops", the perceptive analysis evoked above is exploited by motor territories of the brain to trigger actions in the internal (bodily, neuro-hormonal, decision) and external (motor) worlds. These territories include the basal ganglia and the prefrontal cortex.
International and industrial relations
- Université de Hyderabad (Inde) et IIT Madras (Inde)
- Centre Interdiciplinario de Neurociencia de Valparaiso, Universidad de Valparaiso (Chili)
Research teams of the same theme :
- ARAMIS - Algorithms, models and methods for images and signals of the human brain
- ASCLEPIOS - Analysis and Simulation of Biomedical Images
- ATHENA - Computational Imaging of the Central Nervous System
- BIOVISION - Biologically plausible Integrative mOdels of the Visual system : towards synergIstic Solutions for visually-Impaired people and artificial visiON
- CAMIN - Control of Artificial Movement & Intuitive Neuroprosthesis
- GALEN - Organ Modeling through Extraction, Representation and Understanding of Medical Image Content
- MATHNEURO - Mathématiques pour les Neurosciences
- MIMESIS - Computational Anatomy and Simulation for Medicine
- NEUROSYS - Analysis and modeling of neural systems by a system neuroscience approach
- PARIETAL - Modelling brain structure, function and variability based on high-field MRI data.
- SISTM - Statistics In System biology and Translational Medicine
- VISAGES - Vision, Action and information manaGement System in health
- XPOP - statistical modelling for life sciences