Graduate School of Computational Neuroscience (Neural Information Processing)

Area of Study and Research
The latest offshoot, the Graduate School of Neural Information Processing, started teaching in the winter term of 2011. This school covers theoretical and computational aspects of neuroscience, a field of research that has become increasingly important in the past years. Please note that during reaccreditation in 2023, “Neural Information Processing” changes to “Computational Neuroscience”.

Apart from the Centre for Integrative Neuroscience – CIN,  major partners of this graduate school are the Tübingen Bernstein Centre for Computational Neuroscience – BCCN and the newly established MPI for Intelligent Systems, which provide – in addition to financial support – scientists who make a considerable contribution to teaching and laboratory training.

Teaching Program – Curricular Focus

  • neural data analysis and models of neural coding and computation,
  • computational motor control and computational vision,
  • rehabilitation robotics and brain-computer interfaces,
  • physical and physiological basis of neural recordings and brain imaging,
  • systems neuroscience and neurophysiology,
  • basic mechanisms of learning and memory,
  • mathematics, statistics and programming,
  • machine learning for neuroscience,
  • theoretical neuroscience,
  • behaviour and cognition.

Details on the curriculum can be found in the ‘Module Handbook – M.Sc. Neural Information Processing’ (see Regulations).

Requirements for Application
This masters program aims at students with a first degree in physics, mathematics, computer science, bioinformatics, engineering or a related field who have a strong interest in biomedical and neural sciences and technical applications. Profound knowledge in maths (linear algebra, analysis), statistics, elementary probability theory, and programming skills in at least one language are compulsory and an asset for a successful accomplishment of the course.