This course provides an introduction to Computational Neuroscience, and also into related engineering disciplines (large scale simulation of brain systems, neuromorphic engineering). It is independent from the course "Neuronale Netzwerke A", and does not require knowledge from it. But it requires knowledge of the basic concepts related to neural networks that are presented in the undergraduate courses Computational Intelligence or Einführung in die Wissensverarbeitung). That material is contained in the scriptum
No prior knowledge from biology or brain science is assumed.

Computer science is not only the science of digital computing machines, but also the science of computation and information processing in biological systems, e.g. in the brain. In fact, the brain is at present still the best performing (and most energy efficient) information processing systems, hence there are good chances that computer science may profit from further insight into information processing in the brain. This is in fact the goal of several large EU-projects, see
whose research strategies will also be discussed in this course.

This course will present the best current models for biological neurons, synapses, and concepts for understanding information processing in networks of biological neurons. We will discuss several competing hypotheses regarding the organization of information processing in the brain, in particular a rather new one where one views the brain as a probabilistic inference (and -learning) machine. Hence we will also provide a short self-contained introduction into probabilistic inference, which has turned out to be an essential tool for modern Artificial Intelligence and Machine Learning.

In the practical exercises, the students learn to implement several of these models with state-of-the-art software systems, and can experiment with them on their own.