| ID |
study objective |
| L1 |
The ability to describe and model biological and technical systems mathematically |
| L3 |
Understanding of how technical components and systems operate at the bio-technical interface and in human-machine interfaces. Knowledge of neural and cognitive-affective parameters in brain-computer interfaces, neuroprosthetics and neurostimulation |
| L6 |
Fundamental principles and mechanisms of machine learning and neuro-inspired artificial intelligence (e.g. neural networks and common machine learning methods) and their applicability to practical problems. Understanding of the similarities and differences between mathematical neuron models, modern AI techniques and machine learning |
| L7 |
Understanding of physiological processes and medical-pathological issues |
| L8 |
Central and autonomous correlates of cognitive-affective processing, with a particular focus on neuroergonomics |
| L9 |
The ability to independently source and evaluate information. Knowledge of specific sources of information in medical research. |
| L10 |
The application of the methods learnt in technical and scientific practice |
| L12 |
The ability to work in a team, key social and intercultural skills |
|
|
|
| Module code: NE3103.NCS |
|
3V+2P (5 hours per week) |
|
6 |
| Semester: 1 |
| Mandatory course: yes |
Language of instruction:
English |
Assessment:
Written exam (50%), project presentation(50%)
[updated 18.06.2026]
|
NE2104.NCS (P213-0141, P213-0142, P213-0189) Neural Engineering, Master, regulation 01.04.2020
, semester 1, mandatory course
NE3103.NCS Neural Engineering, Master, regulation 01.10.2025
, semester 1, mandatory course
|
75 class hours (= 56.25 clock hours) over a 15-week period.
The total student study time is 180 hours (equivalent to 6 ECTS credits).
There are therefore 123.75 hours available for class preparation and follow-up work and exam preparation.
|
Recommended prerequisites (modules):
None.
|
Recommended as prerequisite for:
|
Module coordinator:
Prof. Dr. Dr. Daniel Strauß |
Lecturer: Prof. Dr. Dr. Daniel Strauß
[updated 29.11.2024]
|
Learning outcomes:
The students develop skills for analyzing biological neural and cognitive systems. Topics include systems neuroscience, sensory processing and perception, and the basics of computational neuroscience. This course covers the fundamental theory but also covers recent applications in forefront research. It provides students with the ability to analyze biological cognitive systems by decoding their correlates in the central and autonomic nervous systems. Moreover, the students learn to design technical cognitive systems, especially for pattern recognition purposes in neural engineering;
Participation in actual research studies and/or lab projects to complement course topics is required. That way, students receive soft skill training related to safe patient handling.
[updated 15.06.2026]
|
Module content:
1. Systems Neuroscience
1.1. The Human Nervous System
1.2. From Neurons to Circuits
1.3. Patch & Voltage Clamp Techniques
1.4. From Circuits to Systems
1.5. VSD & MEA Techniques
1.6. From Systems to Behavior
1.7. ECoG, EEG, MEG, fMRI, fNIRS Techniques
1.8. Plasticity, Learning, Memory
1.9. Executive Functions & Decision Making
1.10. Computational Models
1.11. Ethics in Neuroscience Research
2. Sensory Processing & Perception
2.1. Gestalt Psychology
2.2. Exogenous & Endogenous Processing
2.3. Models of Attention & Cognitive Effort
2.4. Models of Affective Processing
2.5. Designing Experimental Paradigms
3. Psychophysiology & Affective Computing
3.1. Intrusive and Minimal-Intrusive Psychophysiology
3.2. Non-Intrusive Psychophysiology
3.3. Affective Computing
3.4. Neuroergonomics & Human-Machine-Interaction
[updated 15.06.2026]
|
Teaching methods/Media:
Lecture notes, digital projector, software in the computer lab
[updated 12.03.2020]
|
Recommended or required reading:
• Abeles, M.: Corticonics: Neural Circuits of the Cerebral Cortex, Cambridge University Press, 1991
• Alberts, B.; Bray, D.; Lewis, J.: Molecular Biology of the Cell, Garland Science, 2002
• Andreassi, John L.: Psychophysiology: Human Behavior and Physiological Response, Taylor & Francis, 2006, ISBN 978-0805849516
• Bear, M.F.; Connors, B.W.; Paradiso, M.A.: Neuroscience, Lippincott Williams and Wilkins, 2001
• Churchland, P.S:; Sejnowski, T.J.: The Computational Brain, MIT Press, 1992
• Dayan, P.; Abbott, L.F.: Theoretical Neuroscience, MIT Press, 1992
• Eliasmith, C.; Anderson, C.H.: Neural Engineering - Computation, Representation, and Dynamics in Neurobiological Systems, MIT Press, 2003, ISBN 0-262-05071-4
• Levine, Daniel S.: Introduction to Neural and Cognitive Monitoring, Lawrence Erlbaum Associates, 2000
• Malmivuo, Jaakko; Plonsey, Robert: Bioelectromagnetism, Oxford University Press, 1995
• Pesenson, Misha Z.: Multiscale Analysis and Nonlinear Dynamics: From Genes to the Brain, Wiley VCH, 2013, ISBN 978-3527411986
• Ripley, Brian D.: Pattern Recognition and Neural Networks, Cambridge University Press, 1996
• Rosner, Jorge: Peeling the Onion: Gestalt Theory and Methodology, Gestalt-Institute of Toronto, 1990
[updated 15.06.2026]
|
