This course provides an introduction to
Computational Neuroscience, and also into related engineering
disciplines (large scale simulation of brain systems, neuromorphic
engineering). Furthermore we will discuss efforts by major
companies such as IBM to change paradigms for the design of
hardware and software in digital computer. In particular some of
the content of the new book "Smart Machines: IBM's Watson and the
Era of Cognitive Computing" will be discussed (a copy of the book
is available for the students of this course on the course webpage
This course 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 one of the goals of the 10-year EU Flagship Project "Human Brain Project" https://www.humanbrainproject.eu/
whose research strategies will be discussed in this course.
Our institute is responsible for the Work Package "Principles of Brain Computation" in this project, which started in October 2013.
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.