In this discussion group we aim to summarise material, examples and experience of how to educate students that are interested to learn about neuromorphic engineering and problem solving using modern neural network concepts, methods, implementations and neuromorphic hardware.
For example, which topics and threshold concepts are central? How to establish an appropriate abstraction level and balance between fundamental questions and problem solving methods? What learning activities and examination methods are effective? Can we identify resources and laboratory exercises that are widely useful for course development?
The discussion focuses on education of undergraduate students and new PhD students with basic knowledge in calculus, programming and electronics, which are interested to contribute to the development of neural network applications based on both state of the art software packages and neuromorphic hardware. Thus, we aim to contribute to the education of a new generation of engineers, who can make use of future neuromorphic technologies like current engineers understand digital design and use digital hardware.