Mnemosyne : Mnemonic synergy

Research subject

Mnemosyne is a team in computer science embedded in a biological and clinical environment. We design models of the brain circuitry and of its pathologies. We aim to better explore strategies of the cerebral architecture to learn and represent different kinds of information. We insist on the effectiveness of our models, including performances, reproducibility and application to realistic tasks.

We work on four thematics :

• Integrative and cognitive neuroscience

The aim is to build, on an overwhelming quantity of data, a simplifying and interpretative grid suggesting homogenous local computations and a structured and logical plan for the development of cognitive functions.

• Computational neuroscience

It consists in exploring more technically and theoretically the relations between structures and functions in the brain using methods from computer science and applied mathematics.

• Machine learning

Our objective is to propose on-line learning systems, where several modes of learning have to collaborate and where the protocols of training are realistic.

• Autonomous robotics

Our goal is to make autonomy possible, by various means corresponding to endow robots with an artificial physiology, to give instructions in a natural and incremental way and to prioritize the synergy between reactive and robust schemes over complex planning structures.

Our research work is 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 work.

On the basis of current knowledge and experimental data, we develop models of the main cerebral structures, taking a specific care of the kind of mnemonic function they implement and of their interface with other cerebral and external structures. Then, in a systemic approach, we build the main behavioral loops involving cerebral structures connecting a wide spectrum of actions to various kinds of sensations. We observe at the behavioral level the properties emerging from the interaction between these loops.

Our original approach is particularly fruitful for investigating cerebral structures which are difficult to comprehend because of the rich and multimodal information flows they integrate. It also permits to revisit and enrich algorithms and methodologies in machine learning and in autonomous robotics. In addition, it enables to elaborate hypotheses to be tested in neuroscience and medicine, while offering to these latter domains a new ground of experimentation similar to their daily experimental studies.

Research axes

• Refinement of the models of pavlovian and reinforcement conditioning, taking a better account of the nature (input and output) and of the modulation of information.

• Design of software libraries that allow checking characteristics needed to switch from experimental models to effective models and promotion of good practice in model design.

• Uniting mechanisms of information acquisition and representation in a single architecture performing a single task.

• Design of a platform to generate virtual environments

Complete bibliography >>>

Latest publications