Computational neuroscience is the field of study in which mathematical tools and theories are used to investigate brain function. It can also incorporate diverse approaches from electrical engineering, computer science and physics in order to understand how the nervous system processes information.
Neuroscience has played a key role in the history of artificial intelligence. It has been an inspiration for building human-like AI. There are two ways that neuroscience inspires us to design AI systems. One, that emulates human intelligence, and two, to build neural networks that mimic brain structure.
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Computational Neuroscience Degree
Master in Computational Neuroscience
The masters in computational neuroscience consists of modules. A module in the masters of computational neuroscience 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 masters in computational neuroscience 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 MSc in computational neuroscience 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 masters in computational neuroscience
Within the first year of the computational neuroscience masters degree, 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 (defense).
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The aim of this multidisciplinary masters in computational neuroscience 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 masters in computational neuroscience degree 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 masters in computational neuroscience degree will be well qualified for academic positions in modern brain research or a career in artificial intelligence or machine learning.
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Admission Prerequisite For The Master in Computational Neuroscience
The required documents:
- Printed and signed online application form
- Students must hold a Bachelor’s degree with the grade “good” or better in a field relevant to the master degree in water resource management, e.g. civil engineering, environmental sciences, natural sciences or geosciences. Basic knowledge in mathematics and water sciences (hydrology, hydraulics, sanitary engineering) is required.
- Applicants must have an excellent command of the English language.
- CV with photo
- Copy of passport or national ID card
- If necessary, proof of previous studies and results
Tuition Fee
There are no tuition fees for this degree program.