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Neural and Cognitive Systems

Module name (EN):
Name of module in study programme. It should be precise and clear.
Neural and Cognitive Systems
Degree programme:
Study Programme with validity of corresponding study regulations containing this module.
Neural Engineering, Master, ASPO 01.04.2020
Module code: NE2104.NCS
SAP-Submodule-No.:
The exam administration creates a SAP-Submodule-No for every exam type in every module. The SAP-Submodule-No is equal for the same module in different study programs.
P213-0141, P213-0142, P213-0189
Hours per semester week / Teaching method:
The count of hours per week is a combination of lecture (V for German Vorlesung), exercise (U for Übung), practice (P) oder project (PA). For example a course of the form 2V+2U has 2 hours of lecture and 2 hours of exercise per week.
3V+2P (5 hours per week)
ECTS credits:
European Credit Transfer System. Points for successful completion of a course. Each ECTS point represents a workload of 30 hours.
6
Semester: 1
Mandatory course: yes
Language of instruction:
English
Assessment:
Written exam (50%), project work (50%)

[updated 15.06.2026]
Applicability / Curricular relevance:
All study programs (with year of the version of study regulations) containing the course.

NE2104.NCS (P213-0141, P213-0142, P213-0189) Neural Engineering, Master, ASPO 01.04.2020 , semester 1, mandatory course
NE3103.NCS Neural Engineering, Master, SO 01.10.2025 , semester 1, mandatory course
Workload:
Workload of student for successfully completing the course. Each ECTS credit represents 30 working hours. These are the combined effort of face-to-face time, post-processing the subject of the lecture, exercises and preparation for the exam.

The total workload is distributed on the semester (01.04.-30.09. during the summer term, 01.10.-31.03. during the winter term).
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 12.03.2020]
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]
 
[Tue Jun 16 12:07:06 CEST 2026, CKEY=nemNE2104.NCS, BKEY=nem, CID=NE2104.NCS, LANGUAGE=en, DATE=16.06.2026]