Institut de Neurosciences Cognitives et Intégratives d'Aquitaine (UMR5287)

Aquitaine Institute for Cognitive and Integrative Neuroscience

Université de Bordeaux

Zone nord Bat 2 2ème étage
146, rue Léo Saignat
33076 Bordeaux cedex


Supervisory authorities

CNRS Ecole Pratique des Hautes Etudes Université de Bordeaux

Our partners

Neurocampus Unitéde Formation de Biologie


GDR Robotique GDR Mémoire GDR Multi-électrodes


Home > Teams > HYBRID (A. De RUGY)


by Daniel Cattaert - published on , updated on

Hybrid Sensorimotor Performance

The central theme of our project is to use hybrid systems, mixing biological control with artificial devices, in order to (i) increase our understanding of the fundamental mechanisms of sensorimotor control and (ii) exploit this knowledge to restore and optimize movement.

Team Composition


JPEGTheme 1: Natural Sensorimotor Mapping
Real-time simulation of realistic musculoskeletal models, including the regulatory functions of spinal networks, are used to assess and reinstall important contributions from limbs biomechanics and from low level sensorimotor loops to movement control.

JPEGTheme2: Developmental robotics
We use myoelectric controls of an open source robotic platform that exploits recent 3D printing technologies to explore different control schemes, and the impact of morphological variations on movement controllability

Theme 3: Co-adaptation strategies
We explore co-adaptation strategies whereby both the user and the artificial controller adapt simultaneously to achieve a common goal

Theme 4: Hybrid crayfish
We analyze the neuronal basis of sensorimotor circuits involved in locomotor network operation and plasticity. Our studies combine cellular and integrative neurobiology (electrophysiology, pharmacology, neuro- anatomy), modeling (realistic simulations of neurones, networks and biomechanics) and hybrid system experiments (biological neural system interfaced with an artificial body).

Theme 5: Hand Biomechanics
The biomechanics theme of the Hybrid Sensorimotor Performance team aims at understanding how the CNS answers the redundancy issue (as pointed out by Bernstein, 1967) taking into account kinematic, dynamic and muscle tension data.

Theme 6: Computer Vision and Gaze Information.

We explore possibilities to augment prosthesis control with gaze information and computer vision, using eye tracking and visual scene recognition to detect reaching intention of amputees and help in reaching motion.