Computational motor control

Summary

The course gives (1) a review of different types of numerical models of control of locomotion and movement in animals, from fish to humans, (2) a presentation of different techniques for designing models, and (3) an analysis of the use and testing of those models in robotics and neuroprosthetics.

Content

Keywords

Numerical models of animal motor control, locomotion, biomechanics, neural control of movement, numerical models

Learning Prerequisites

Required courses

None

Recommended courses

None

Important concepts to start the course

Programming in Python,  good mathematical background (dynamical systems)

Learning Outcomes

By the end of the course, the student must be able to:

• Argue about the validity of models
• Formulate models of motor control
• Hypothesize mechanisms of motor control
• Design models of motor control
• Test the models

Transversal skills

• Write a scientific or technical report.
• Access and evaluate appropriate sources of information.

Teaching methods

Lectures and numerical exercises on a computer using Python and FARMS, a dynamic simulator of animals and robots (with weekly sessions with assistants and the professor)

Expected student activities

• Attending lectures
• Read scientific articles
• Develop numerical models of the locomotor control circuits of a simulated animal in Python and FARMS
• Writting short scientific reports describing the models and analyzing the results of the simulations

Assessment methods

60% of the grade comes from the modeling projects (by groups of 3 students), and 40% comes from a written exam during the semester.

Resources

Moodle Link

• https://go.epfl.ch/CS-432