To gain specific knowledge in theories, methods and technologies for understanding and analysing the functionality of the human nervous system. In particular, the course gives practical examples of applications in the field of assistive technologies in disability, like brain computer interfaces and neuroprosthetics.
teacher profile teaching materials
Introduction to neural engineering
Passive models of excitable cells
Active models: Hodgkin-Huxley
Part II: neural signal processing
Mathematical models of spiking neural activity
Algorithms for spike detection
Algorithms for spike sorting
Practical implementations using Python
Part III: motor control
Introduction to motor control theories
Modular motor control
Muscle synergy analysis
Practical implementation using Python
He, B. (Ed.). (2007). Neural engineering. Springer Science & Business Media.
Aurelien Geron (2019). Hands on Machine Learning. O’Reilly
Programme
Part I: electrical models of the neuronsIntroduction to neural engineering
Passive models of excitable cells
Active models: Hodgkin-Huxley
Part II: neural signal processing
Mathematical models of spiking neural activity
Algorithms for spike detection
Algorithms for spike sorting
Practical implementations using Python
Part III: motor control
Introduction to motor control theories
Modular motor control
Muscle synergy analysis
Practical implementation using Python
Core Documentation
Horch, K. W., & Dhillon, G. S. (Eds.). (2004). Neuroprosthetics: theory and practice (Vol. 2). World Scientific.He, B. (Ed.). (2007). Neural engineering. Springer Science & Business Media.
Aurelien Geron (2019). Hands on Machine Learning. O’Reilly
Type of evaluation
Practical test (3 hours) Oral colloquium