Computational motor control
Summary
The course gives (1) a review of different types of numerical models of control of locomotion and movement in animals, (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
- General concepts: Importance of numerical models in a scientific approach, introduction to nonlinear dynamical systems and neural network models.
- Numerical models of motor systems : Neural network models of control of locomotion, rhythm generation in central pattern generators, reflexes, force fields, sensory-motor coordination, and balance control.
- Numerical models of the musculo-skeletal system: muscle models, biomechanical models of locomotion, gait classification, applications to bio-inspired robots.
- Numerical models of arm movements: invariants of human arm movements, different hypotheses about human motor control: inverse models and equilibrium point hypothesis. Muscle synergies.
- Numerical models of sensory systems : Proprioception and vestibular system. Visual processing in the retina, salamander and primate visual systems, applications to machine vision.
- Neuroprosthetics: short overview of current developments, analysis of how modeling can be used to improve interfaces between machines and the central nervous system
- Numerical exercises: The course will also involve numerical exercises in which students will develop their own numerical simulations of sensory-motor systems in Python and in a dynamical robot simulator (with weekly sessions with assistants and the professor).
Teaching methods
Lectures and numerical exercises on a computer using Python, Matlab 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
50% of the grade comes from the modeling projects (by groups of 3 students), and 50% comes from a written exam during the semester.
In the programs
- Semester: Spring
- Exam form: During the semester (summer session)
- Subject examined: Computational motor control
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Semester: Spring
- Exam form: During the semester (summer session)
- Subject examined: Computational motor control
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Semester: Spring
- Exam form: During the semester (summer session)
- Subject examined: Computational motor control
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Semester: Spring
- Exam form: During the semester (summer session)
- Subject examined: Computational motor control
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Semester: Spring
- Exam form: During the semester (summer session)
- Subject examined: Computational motor control
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Semester: Spring
- Exam form: During the semester (summer session)
- Subject examined: Computational motor control
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Semester: Spring
- Exam form: During the semester (summer session)
- Subject examined: Computational motor control
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Semester: Spring
- Exam form: During the semester (summer session)
- Subject examined: Computational motor control
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Semester: Spring
- Exam form: During the semester (summer session)
- Subject examined: Computational motor control
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Semester: Spring
- Exam form: During the semester (summer session)
- Subject examined: Computational motor control
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Semester: Spring
- Exam form: During the semester (summer session)
- Subject examined: Computational motor control
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Semester: Spring
- Exam form: During the semester (summer session)
- Subject examined: Computational motor control
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Semester: Spring
- Exam form: During the semester (summer session)
- Subject examined: Computational motor control
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Semester: Spring
- Exam form: During the semester (summer session)
- Subject examined: Computational motor control
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Semester: Spring
- Exam form: During the semester (summer session)
- Subject examined: Computational motor control
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Exam form: During the semester (summer session)
- Subject examined: Computational motor control
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
Reference week
| Mo | Tu | We | Th | Fr | |
| 8-9 | |||||
| 9-10 | |||||
| 10-11 | |||||
| 11-12 | |||||
| 12-13 | |||||
| 13-14 | |||||
| 14-15 | |||||
| 15-16 | |||||
| 16-17 | |||||
| 17-18 | |||||
| 18-19 | |||||
| 19-20 | |||||
| 20-21 | |||||
| 21-22 |
Légendes:
Lecture
Exercise, TP
Project, other