International Computational Neuroscience

Models of Neural Systems: the module provides the relevant, basic neurobiological knowledge and the relevant theoretical approaches as well as the findings resulting from these approaches. Students will learn to appropriately choose the theoretical methods for modelling neural systems and how to apply these methods while taking into account the neurobiological findings.
Models of Higher Brain Functions: the module provides 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. Students will learn to appropriately choose the theoretical methods for modelling higher brain functions and cognitive processes. They will learn how to apply these methods while taking into account the neurobiological findings.
Acquisition and Analysis of Neural Data: students will gain knowledge about the most important methods for experimental acquisition of neural data and the respective analytical methods. Students will learn about the different fields of application, the advantages and disadvantages of the different methods and will become familiar with the respective raw data. They will be enabled to choose the most appropriate analysis methods and apply them to experimental data.
Machine Intelligence: students will learn about the most important methods in artificial intelligence and machine learning. After completing the module, 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 programme (in collaboration with other students) 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. The module will impart the competence to follow the progress in neuroscience with a reflective, ethical sensibility.
Courses on Advanced Topics: in this module students will deepen their studies on specific topics in computational neuroscience according to their individual interests.
Lab Rotation: students will learn to work on a research-related scientific question in collaboration with the supervising research group and to present their results adequately. The abilities trained in this module comprise: literature research, further specification of a given scientific question, project planning and scientific working, adequate evaluation, presentation and discussion of the resulting findings, training of social competence necessary for a successful career in science.
Further elective courses on advanced topics in Computational Neuroscience may be attended, for example, "Neural Noise and Neural Signals - Spontaneous Activity and Information Transmission in Models of Single Nerve Cells", "Stochastic Processes in Neuroscience" or "Stochastic Partial Differential Equations".
The BCCN Berlin also offers preparatory courses in mathematics and neurobiology for admitted students, which take place in September-October before the beginning of the winter semester.

Educational organisation

The programme consists of modules. A module covers a certain topic using different teaching methods (see below). A module's mean workload is given in credit points. A credit point is defined in agreement with the European Credit Transfer and Accumulation System - ECTS, i.e. 30 hours of student invested time correspond to one credit point. The Master's programme covers 120 credit points in total, each of the four semesters covering 30 credit points, i.e. 900 working hours. Each module is followed by an exam.

The teaching methods employed in the Master's programme are:

• 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
• Seminars

Within a module, the different teaching methods complement each other by covering different aspects of the same topic.

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 of the programme is strongly research-oriented, including lab rotations and the Master's thesis.

Foundations (first and second semesters)
The modules "Models of Neural Systems" (12 CP), "Models of Higher Brain Functions" (12 CP), "Acquisition and Analysis of Neural Data" (12 CP), each cover both the theoretical as well as the experimental aspects of the respective field. The module "Machine Intelligence" (12 CP) covers topics in machine learning and artificial neural networks. The module "Programming Course and Project" (6 CP) teaches the students a programming language and how to use it to specify, develop, document and test a larger programme. Within the first two semesters students also have the opportunity to fill gaps in their knowledge by individual studies tailored to their needs with the aid of their mentor (6 CP).

Research-oriented phase (third and fourth semesters)
The third semester is devoted to lab rotations. Every student will participate in research projects in three different laboratories affiliated with the Bernstein Center. Each of the three projects lasts for approx. two months (3 x 9 CP). The projects will be tailored to give intensive hands-on experience to the students. They will carry out individual research projects, and will be supervised by a senior researcher. The three projects include at least one theoretical and one experimental project. Additionally, students will take an obligatory course on ethical issues and the societal implications of brain research (3 CP).

The fourth semester is primarily devoted to thesis research (20 CP) and complemented by courses on advanced topics (10 CP). The Master's thesis is concluded by an oral presentation (defence).

This is a joint degree programme.

Study abroad unit(s)

Students will be supported if they want to do a lab rotation or an advanced course in a Bernstein Center affiliated lab or within the Network of European Neuroscience Schools (NENS).

Internships

During the second year students do three lab rotations in different labs.

Forms of assessment

Successful solving of mathematical exercises
Successful completion and presentation of programming projects
Active participation in laboratory practical sessions
Oral exams
Presentation of research results
Defence of Master's thesis

Course objectives

The aim of this multidisciplinary programme is to foster a new generation of scientists who have been trained in both mathematical/computational skills and neuroscientific methodologies. Neuroscience is one of the most intensively developing and important sciences of the 21st century. Understanding the functioning of the brain requires the collaborative efforts of neurobiologists, neuropsychologists, cognitive scientists, medical researchers, computer scientists, mathematicians, physicists and engineers. Students who have completed the Master's programme will have the ability to communicate across these diverse disciplines which will help them make their own contribution to the fast growing field of neuroscience.
Graduates of the programme will be well qualified for academic positions in modern brain research or a career in artificial intelligence or machine learning.

Language requirements

Proficiency in English
Non-native speakers should document this by the TOEFL test (570 points in the paper-based test or 230 points in the computer-based test or 88 points in the internet-based test) or an equivalent certificate. Students holding a degree from a programme taught entirely in English are not required to submit a language certificate.

Academic requirements

Bachelor's degree (or equivalent) in natural sciences, engineering or mathematics
Sufficient mathematical knowledge (at least 24 credit points) particularly in linear algebra (at least six credit points), analysis - including dynamical systems (at least six credit points), probability theory and statistics (at least six credit points)
Proficiency in English: non-native speakers should document this by the TOEFL test (570 points in the paper-based test or 230 points in the computer-based test or 88 points in the internet-based test) or an equivalent certificate.

Enrolment fees

The total enrolment fee amounts to approx. 305 EUR per semester including a matriculation fee, a compulsory fee to the student service, a semester ticket for public transport and a compulsory social fee.

Costs of living

Approx. 800 EUR per month

Job opportunities

The Bernstein Center for Computational Neuroscience offers research assistantships to gifted students to integrate them early on into research groups. Interested students have to file their application with the head of the respective research group, who then decides on acceptance. Working hours range from 41 hours per month (more common) to 80 hours per month, paid at an hourly rate of 10.98 EUR.

Arrival support

Short-term accommodation in a students' residence can be organised upon arrival if requested by the student in advance. Students are supported in all visa and matriculation matters and are advised to seek assistance at the international office of the TU Berlin.

Services and support for international students

The programme's Coordination Office supports international students in all visa matters, in all translation matters, in finding accommodation, and all in other matters that might arise. All students have a mentor who supports them in all study-related issues. English textbooks are available, covering the most important topics of the programme. The Bernstein Center offers a computer room where students can work and a tea room where they can meet and talk or read and discuss.

Accommodation

Students of the programme have several possibilities to find accommodation. Whatever district they prefer to live in, it is advisable to find a place with access to public transport. This is very convenient in everyday life and can save a lot of time.
If students are interested in residential accommodation, the "Berliner Studentenwerk" offers a number of different options, such as single rooms, apartments, or a shared flat. Rent starts from approx. 150 EUR. Many rooms are furnished but you need your own bedding and kitchen items.
Students can also choose to find a room or flat privately; the coordination office provides links and recommendations for finding accommodation.