The students who completed this course will have obtained a broad understanding of the ever-expanding role of AI in neurotechnology. They will have familiarized with the state-of-the-art developments in the fields brain reading and writing, covering topics such as neural decoding, brain computer interfacing, visual neuroprosthetics, cochlear implants.
They will further gain first-hand experience in implementing the AI components of a selection of the above-mentioned technologies in the practicals.
After successfully completing the course, the student can…
- Understand and explain the role of AI in neurotechnology
- Understand and explain how various brain reading and writing methods work
- Discuss and critically think about the state-of-the-art developments in neurotechnology
- Implement AI components of various brain reading and writing technologies.
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This course provides students with a broad understanding of neurotechnology with a particular emphasis on the role of AI in brain reading and writing technologies. Brain reading broadly covers technologies and applications involving decoding of information from neural signals, and brain writing refers to neurotech applications where information is introduced to the brain unconventionally, by external means, such as via a neural implant.
In the lectures, we will touch upon topics ranging from the neuroimaging techniques fundamental to neurotechnology to emerging technologies such as cortical visual implants that may allow blind people to see in the future. We will have guest lecturers who will provide the students with insights from their respective fields. The main text of the course will be lecture slides and reading in form of research papers which will be provided on Brightspace. Furthermore, students will form groups to study a brain reading or writing study in detail and prepare a presentation about their selected study. The practicals will be used to implement a number of (AI) methods used in neurotech studies.
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● SOW-BKI136 Brain or equivalent ● SOW-BKI114 Human-Computer Interaction or equivalent ● SOW-BKI230A Neural Networks or equivalent ● Python experience (including automatic differentiation packages such as Chainer/MxNet/PyTorch)
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The final grade will be a weighted sum of the assignments (50%) and the final exam (50%). There will be one retake for the exam and no retakes for the assignments.
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