Courses
Courses
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Seminars
Advance Context Recognition ACR
Background
ACR introduces and discusses current scientific topics in context recognition (i.e. identifying and describing a situation or state of the environment in the computer) from sensor data, including time series data and image data. Students can choose from a new offer of projects and medical applications each term. The seminar’s learning objectives are in machine learning and pattern recognition and remain the same across all projects. The learning objectives are supported by practical implementation and validation of context recognition stacks in a project application selected by the student.
Learning Objectives
- Understand current scientific topics in context recognition of time series and image data.
- Apply machine learning and pattern recognition algorithms in context recognition.
| ECTS | For seminars: 2.5, 5, 7.5, default: 5 |
| Language | English |
| Period | Summer term 2025 |
| Virtual Seminar | No |
| Useful knowledge | Python |
| Work distribution | 25% algorithm design, 50% programming, 25% running experiments |
| Med. Eng. designation | Advanced Context Recognition (ACR) |
| StudOn link | https://www.studon.fau.de/campo/course/493161 |
| First meeting | |
| Registration | Via StudOn, obligatory after introduction. |
Contact
Computational Neural Engineering CNE
Background
This seminar offers an overview of the interdisciplinary methods and applications at the intersection of digital health and neural engineering, focusing on the simulation of neural engineering technologies applied in, on, or around the human body. Students will gain the skills and perspectives needed to execute the full workflow of computational neuroscience, virtual prototyping, and clinical decision support, enabling the clinical translation of neurotechnologies. Key topics covered in the module include:
Multi-Modal Medical Image Processing: Techniques for processing and integrating data from diverse imaging modalities, such as MRI or CT to support personalized model development and digital twinning.
Multi-Scale 3D Computational Modeling: Computer-aided design (CAD) techniques for modeling the human body and neural systems from macro- to microanatomy by integrating multimodal data with neurophysiological and neuroanatomical insights.
Biophysical Simulations and Finite Element Method (FEM): Methods to simulate physical and electrical interactions between neural systems and biomedical devices, crucial for understanding and optimizing their therapeutic impact.
Conductance-Based Cable Models: Tools for simulating signal propagation along neural substrates, offering insights into cellular mechanisms and their impact on neural circuits.
Spiking and Non-Spiking Neural Networks: Modeling and analysis of neural circuits to understand and predict neural activity on a population level.
Model-Based Optimization: Techniques such as surrogate-based and multi-objective optimization to identify Pareto-optimal solutions that maximize desired neural recruitment while minimizing unintended co-activation of other tissues.
The seminar also provides an overview of state-of-the-art virtual prototyping tools and clinical decision support systems, showcasing their applications in neurological, psychiatric, and multi-organ disorders. Emphasis is placed on how these tools facilitate biomedical innovation, enabling students to engage in therapy design, validation, and clinical decision support of precision biomedical interventions.
Learning Objectives
- Explain the foundational principles of computational neuroscience. This includes the logic of information transfer within the nervous system and the mechanisms driving interactions between neurotechnologies and the nervous system.
- Deconstruct interactions between neurotechnologies and the nervous system,
- Virtually prototype both existing and novel neurotechnologies.
- Evaluate existing and novel neurotechnologies efficacy and safety at both individual and population levels.
- Develop clinical decision support tools that enhance (pre-operative) treatment planning, (intra-operative) guidance, and (post-operative) care.
- Give an overview of the neural engineering field and an understanding of commercial standards for in-silico therapy design, therapy verification, safety assessments, and clinical decision support.
| ECTS | For seminars: 5 |
| Language | English |
| Period | Summer term 2025 |
| Virtual seminar | No |
| Useful knowledge | Python |
| Work distribution | 50% algorithm design, 50% programming |
| Med. Eng. designation | Seminar Computational Neural Engineering |
| StudOn link | https://www.studon.fau.de/campo/course/492828 |
| First meeting | |
| Registration | Via StudOn, obligatory after introduction. |
Contact
Sensorimotor Neuroprosthetics SMN
Background
This seminar offers a comprehensive, multidisciplinary introduction to the foundational knowledge and methods required for the development and clinical application of neuroprosthetic and neurorehabilitative solutions addressing sensorimotor impairments. It integrates perspectives from neuroscience, engineering, and clinical practice to equip students with a holistic understanding of this rapidly evolving field. The seminar covers:
Sensorimotor Neurophysiology and Neuroscience
An introduction to the neurophysiology of the sensorimotor system, focusing on motor control, neural encoding, and the mechanisms of somatosensory feedback.
Interface Technologies
An introduction to brain-computer interfaces, human-machine interfaces, and neuromuscular interfaces, emphasizing their roles in detecting movement intention and sensory feedback.
Neurostimulation and Neuromodulation
An overview of technologies used to modulate neural activity, including methods for inducing or blocking signals within neural substrates to achieve specific physiological outcomes.
Clinical and Technological Context
A critical comparison of the clinical standard of care versus contemporary, state-of-the-art biomedical solutions, focusing on their applications in neuroprosthetics and neurorehabilitation.
Learning Objectives
- Explain the human sensorimotor system and its interactions with sensorimotor neuroprosthetic technologies.
- Give an overview of the clinical standards of care and state-of-the-art translational research.
- Design and construct sensorimotor neuroprosthetics. Using provided materials and instructions, students can combine non-invasive neurostimulation with neuromuscular interfaces to create systems that enable motor control across limbs and between individuals.
- Apply the technical and practical skills to design the transformative principles of sensorimotor neuroprosthetics.
- Process, analyze, and interpret electrophysiological data and patient-reported outcomes.
- Apply and critically evaluate neurostimulation and neuromodulation technologies and discuss their therapeutic applications in clinical and research settings.
- Critically assess existing neuroprosthetic solutions and innovate new ones based on applying foundational knowledge from current challenges and trends in practical applications.
| ECTS | For seminars: 5 |
| Language | English |
| Period | Summer term 2025 |
| Virtual Seminar | No |
| Useful knowledge | Python |
| Work distribution | 25% algorithm design, 25% programming, 50% running experiments |
| Med. Eng. designation | Sensorimotor Neuroprosthetics (SMN) |
| StudOn link | https://www.studon.fau.de/campo/course/492829 |
| First meeting | |
| Registration | Via StudOn, obligatory after introduction. |
Contact
First Published on September 22, 2024
Last Updated on March 25, 2025 by Claudia Uebelein