International Master's Programme in Computational Neuroscience

Computational Neuroscience is a fast-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 experiment, 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. Structure of the programme Within the first year of the programme, students are individually brought to a high level of competence in the basic fields of the programme. The second year is strongly research-oriented, including lab rotations and the Master's thesis. The BCCN Berlin also offers preparatory courses in mathematics and neurobiology for admitted students, which take place from September to October before the beginning of the winter semester. Joint Degree This is a joint degree programme of the TU Berlin and the HU Berlin, organised by the BCCN Berlin. Teaching takes place at HU Berlin (BCCN Berlin, Campus Nord, postal code 10115) and at TU Berlin (postal code 10587). The programme consists of modules totalling 120 credit points (CP). Most modules are completed with an oral exam. The teaching methods employed are as follows: lectures; tutorials, i.e. solving of analytical and mathematical exercises, solving of programming tasks; practicals, i.e. experimental laboratory work; projects, i.e. programming projects; and seminars. Within a module, the different teaching methods complement one another by covering different aspects of the same topic. Models of Neural Systems: Basic neurobiological knowledge and the relevant theoretical approaches as well as the findings resulting from these approaches. Students learn to appropriately choose the theoretical methods for modelling neural systems and how to apply these methods. Models of Higher Brain Functions: Basic knowledge about how to model higher brain functions with an emphasis on basic neurobiological and psychophysical concepts. Examples will be drawn from vision, memory, attention and executive functions. Acquisition and Analysis of Neural Data: Students gain knowledge about the most important methods for experimental acquisition of neural data and the respective analytical methods. Students learn about the different fields of application, the advantages and disadvantages of the different methods, and become familiar with the respective raw data. Machine Intelligence: Students learn about the most important methods in artificial intelligence and machine learning. Students will be able to evaluate the performance of the methods discussed and to apply them successfully to the respective application domains. Programming Course and Project: Students will be able to understand and use basic and advanced concepts of a programming language and to develop complex programmes. Furthermore, they will be able to develop a larger collaborative programme including the necessary specifications, documentation, and tests. Individual Studies: As the Master's programme in Computational Neuroscience is an interdisciplinary study programme, this module serves to fill individual gaps in the student's background knowledge. Ethical Issues and Implications for Society: Students will learn to reflect on the ethical and societal consequences of modern neuroscience. Courses on Advanced Topics: Students will deepen their studies on specific topics in computational neuroscience according to their individual interests. Lab Rotation: St