A neural account of learning adaptations in manual reaching movements

 

Host Institution: Ruhr-University Bochum

Department that hosts the PhD: Institute for Neural Computation

Contact: Prof. dr. Schöner (use this email contact only to request additional information, do NOT use this email for applications)

 

Project description

The goal of this theoretical project is to develop a neurally grounded account for the changes in the temporal shape of motor commands and their coordination across degrees of freedom over learning that bring about adaptations in goal-directed reaching movement of the hand. The question how the muscles and joints involved in moving the hand through space are harnessed has been approached by uncovering the structure of variance in joint space (using the concept of the “uncontrolled manifold”, UCM). The fact that humans adapt to external force fields or varied visuo-motor mappings and recover the smooth trajectories of the hand in space typical of human movement implies that these motor commands can be modulated in space and time to compensate for force or visuomotor perturbations. The concern of the current project is how the changes in structure of variances emerges from neural constraints. Current explanatory accounts of adaptations are limited to abstract computational descriptions that remain unconnected to physiological mechanisms. Because motor pathologies typically affect both coordination and the achievement of smooth goal-directed movement, an integrated, neurally based account is critical to understand and address clinical issues. The theoretical work will build on prior modelling, that will be expanded through the notion of banks of neural timers that enable modulation of motor commands in response to visuo-motor remapping or force-field perturbation. Interactions between this project and PhD2 will ensure that the work will be exploited to improve rehabilitation training.

 

People involved

Prof. dr. Schöner

Dr. Zhang

Dr. Bongers

Dr. Tacconi

 

Key publications:

Martin, V., Reimann, H., & Schöner, G. (2019). A process account of the uncontrolled manifold structure of joint space variance in pointing movements. Biological Cybernetics, 113(3), 293–307. https://doi.org/10.1007/s00422-019-00794-w

Reimann, H., & Schöner, G. (2017). A multi-joint model of quiet, upright stance accounts for the “uncontrolled manifold” structure of joint variance. Biological Cybernetics, 111(5–6), 389–403. https://doi.org/10.1007/s00422-017-0733-y

Scholz, J. P., & Schöner, G. (2014). Use of the Uncontrolled Manifold (UCM) Approach to Understand MotorVariability, Motor Equivalence, and Self-motion. In M. F. Levin (Ed.), Progress in Motor Control (Vol. 826, p. Chapter 7). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-47313-0

 

 

Specific required skills of PhD student

Skill area
Language (writing) English
Language (speaking) English
Programming Basic skills, capacity to learn Matlab or Python
Statistics
Statistical programs
Background Training in mathematics, physics, engineering, or computer science would be helpful.
Project specific knowledge Differential equations, numerics

 

Project characteristics

Primary focus project Mathematical modeling in close alignment with experimental data and based on good conceptual understanding
Methods Dynamical systems, neural networks, numerical simulation, muscle models, biomechanical models