This course provides an introduction to Computational Neuroscience, and also into related engineering disciplines (simulation of brain systems, neuromorphic engineering). This course is independent from the course "Neural Networks" (which covers artificial neural networks), and does not require knowledge from it.

Motivation for this course:
Computer science is sometimes misunderstood as the science of digital computers. But actually, computer science covers also computation and information processing in biological systems, in particular in the brain. In addition computer simulations have become an important tool in brain sciences, and a large number of computer scientists are engaged in interdisciplinary work with neuroscientists and cognitive scientists. In addition many computer scientists work in companies and research institutes on the design of innovative "neuromorphic" computing hardware, such as the TrueNorth chip at IBM https://en.wikipedia.org/wiki/TrueNorth .

The brain is at present still the best performing and most energy efficient computing system. Hence there are good chances that computer science will benefit from further insight into computational processes in the brain. In particular, computer science needs insight into principles of brain computation, since computations in the brain are differently organized than those in our present generation of vonNeumann computers. We will discuss in this course the main principles of brain computation that have become known so far, and related open problems and topics of current research.

Since our institute is responsible for the Work Package "Principles of Brain Computation"  in the 10-year EU Flagship Project "Human Brain Project" https://www.humanbrainproject.eu/ that started in 2013, we will also discuss the strategy of this project, and possibilities for students to get involved.

On the more practical side the course will present standard models for biological neurons, synapses, and brain connectivity.In the practical exercises, the students learn to implement several of these models with state-of-the-art software systems, so that they can explore on their own some principles of brain computation.