Computational Neuroscience (M.Sc.)

Computational neuroscience is a young, growing discipline within the exciting field of neuroscience. It uses theoretical approaches from a variety of disciplines including mathematics, physics, computer science and engineering to understand the brain. Computational neuroscience integrates experiments, data analysis and modelling. Furthermore, it makes a scientific language available that can be used across disciplines and levels for neurobiology, cognitive science and information technology. Computational neuroscience may thus help to solve long-standing research questions, contribute to better prevention and treatment strategies for neural disorders, lead to unified concepts about biological processes, advance information technologies and human-machine interactions and, last but not least, provide new insight for designing efficient strategies for teaching and learning.

The objective of the interdisciplinary masters program in Computational Neuroscience is to thematically group, deepen and systematically supplement the knowledge, abilities and skills regarding studies on the characteristics and function of neural systems, which were acquired during the first degree program. The education shall enable students to work scientifically at a high level, and to bring the acquired knowledge into the areas of application of computational neuroscience in health sciences and IT. The study program is research-oriented and closely tied to current research.

The students shall be enabled to:

• develop new theoretical concepts on the function of neural systems,
• set up, evaluate and examine mathematic models of neural systems in close coordination with experimental and clinical research,
• develop new experimental paradigms from theoretical concepts and mathematic models,
• enhance experimental and clinical methods by developing new procedures for data acquisition and analysis,
• perform a transfer of ideas between the study of neural systems and IT-applications - above all in the area of machine intelligence,
• use the acquired technologies also in the clinical field,
• deal with the ethical and social consequences of this direction of research.
• These study objectives require that:
• the transfer of specialized knowledge will include theoretical, methodical and experimental bases,
• the ability for interdisciplinary scientific work and for a successful theoretical-experimental cooperation in scientific projects (“lab rotations”) will be trained,
• students will be made familiar with potential areas of application in IT and health sciences and can deepen their knowledge in this area.
• social competence will be developed for the interdisciplinary work in joint projects.

Therefore, one special focus is the extensive practical work within the framework of lab rotations and of the masters thesis, during which the students - together with their supervising working group - shall work on current research issues. The students shall be explicitly instructed to carry out combined experimental-theoretical projects; the bases for this are provided in the courses of the first year, during which theoretical, experimental and application-oriented subjects are combined. The increased demands on the quality of the education due to the interdisciplinary character of the program will be satisfied by a teaching staff that includes lecturers from the theoretical field, from the area of experimental neurosciences and from the clinical field.

Furthermore, the students should learn to see their area of expertise in the social context and to realize their responsibility in such conditions. Creative cooperation in interdisciplinary groups as well as gender competence should be supported by means of project and team work.

All students who are interested in neuroscience and have a strong mathematical background are welcome to apply. In accordance with the interdisciplinary nature of computational neuroscience, the program encourages applications from students from diverse disciplines such as natural sciences, engineering, or mathematics.

The following is required for an application:

• a bachelors or equivalent certificate qualifying applicants for professional work, e.g. German Diplom but not Vor-Diplom), typically in the natural sciences, in an engineering discipline, or in mathematics,
• proficiency in English above B.2 level - non-native speakers should document this with:
• TOEFL test (570 pts paper-based, 230 pts computer-based, or 88 pts internet-based)
• Cambridge Certificate of English: Advanced (CAE, min. grade C),
• IELTS with at least band 6.5,
• TOEIC with a minimum of 785 points (395 points in each section)
• UniCert III (min. grade 3,0)
• or an equivalent certificate like e.g. proof that your previous studies were conducted in an international program with English as the official language (a simple interview is not sufficient)
• sufficient mathematical knowledge (i.e. at least 24 credit points according to the ECTS) particularly in linear algebra (at least 6 credit points), analysis/calculus [including dynamical systems] (at least 6 credit points), probability theory and statistics (at least 6 credit points).

The following is recommended but not compulsory for students applying for the program:

• Some prior knowledge of neuroscience. If you don't have any prior knowledge, be prepared to read at least the introductory chapters of E. Kandel et al. 'Principles of Neural Science'.
• Some prior programming knowledge.

More information on the admission requirements and process can be found here.