Teaching

Major-related courses

Systems Neuroscience 2nd year course
The aim of the course is to present an integrated view of animal (and human) behaviour. The course commences with a comparative study of the structure and function of sense organs, emphasizing that an understanding of an animal's behaviour is impossible without a detailed knowledge of the sense organs. Various aspects of intra- and interspecific behaviours, e.g., orientation, feeding, territoriality, courtship, etc. are then explained, before predominantly ecological aspects (e.g., competition, parasitism, pest control, etc.) wrap up the course.
<link to the course catalogue>

Computational Neuroscience3rd year course
The course will cover methods and knowledge for addressing structure - function relationships at different scales of the nervous system through mathematical analyses and computational modeling. Lectures will review neurobiological concepts and currently available data as well as mathematical approaches for representing neural systems. Complementary lab session will provide an opportunity to become familiar with widely used neural modeling packages and to carry out individual course projects
A selection of the individual student projects completed for this course can be found here.
<link to the course catalogue>

   Lab course Neurophysiology, 2006

Advanced Lab Course Biology I (Neurobiology) 
This is the major-specific mandatory lab course for Biology majors in their second year. Lab course modules include selected experiments from neurobiology and microbiology. The course will be continued in the fourth semester with experiments from behavioral neurobiology, microbiology, and cell biology.

During the first weeks, students will be introduced to the theoretical methodology of the chemical and biochemical sciences. Lectures and hands-on exercises will include chemical and biological safety, note-taking in the laboratory and the writing of lab protocols but also the various techniques used to obtain scientifically valid information on a particular topic from the literature and the databases on the internet. A brief introduction to computing in the chemical and biochemical sciences, to image analysis, and to animal model systems will also be given.
<link to the course catalogue>

Transdisciplinary courses

Mind, Brain and Body 
This course (taught in collaboration with Dr. Paul Crowther) looks at the human mind from two different perspectives, those of philosophy and neuroscience. These two fields have not always been the best of friends, with many philosophers (as well as neuroscientists) doubting that the complex questions surrounding mind and consciousness could be solved by the empirical, reductionist research approach employed by the natural sciences; and neuroscientists finding the considerations of philosophy far too general to be of any real value and help in their experimental work. However, the stunning progress made by the neurosciences in the last several years has allowed them to start addressing age-old philosophical topics, such as perception, memory or awareness, through practical experiments.

In this course, we present an introduction to the modes of thinking in philosophy and neuroscience, will summarize the current knowledge in both disciplines about matters of the mind, and will discuss if the two perspectives can be unified.

A selection of the group student projects completed for this course can be found here. <link to the course catalogue>

From Genes to the Internet: Complex Networks in Engieering and Science
Networks research is about the design principles of complex Systems. Within this USC we will approach a variety of biological and technical networks from this large-scale system-wide perspective. We will critically assess the merits the networks approach: What are universal organizing principles? Does the unifying view of abstracting Systems as networks help understand these Systems? In particular, we will study networks from the perspective of very different disciplines: statistical physics, engineering, Systems biology and neuroscience.

Statistical physics provides a set of methods and the theoretical background for understanding dynamical processes on networks. In particular the theories of percolation and synchronization may be utilized in network analyses. Percolation theory is relevant for processes of epidemic spreading of viruses (independently of whether the viruses refer to Internet attacks or to diseases). Synchronization is a phenomenon that is widely found in natural Systems, and artificial Systems are often constructed in a way that their units synchronize. We shall exemplify the type of predictions, control and understanding which can be provided by the physicists' approach to these processes.

Another field where networks with a dynamics on them is important is machine learning. Here, Artificial Neural Networks (ANNs), which are remote echoes of biological brain structures, are "trained" to perform nontrivial tasks for instance in Speech recognition, control of robots, or Video sequence analysis. Network structures also play an important role in Bayesian Networks, which provide today's Standard tools for diagnosis and decision-making in domains as diverse as medicine, nuclear power plant monitoring, or user modeling for "intelligent" Software Systems. Interestingly, both some ANNs and Bayesian networks have connections to the spin glass models from statistical physics which are likewise a topic of this USC. The USC will give introductions to all of these methods of machine learning, with examples of their applications.

The brain, as a third field of application, is a System of mindboggling complexity, consisting of more than 10^10 individual neuronal elements, each of which may receive inputs from more than a 1,000 different neurons, and in turn may send Signals to as many. The 'neuro sessions" of the USC will present experimental and conceptual strategies by which modern neuroscience approaches this complexity. Specifically, the classes will look at how the specific spatial layout and topologic organization of nerve fiber networks underlies the diverse, stable and adaptable functions of the brain.

Systems biology, finally, aims at understanding the functioning of a cell due to the concerted action of its constituents. This is a tremendously difficult goal, äs many spatial and temporal scales contribute to cellular organization and turn it into a complex interplay of regulatory processes. Here networks are extremely helpful in organizing the step to a system-wide view and in finding informative patterns in the data. Within this USC we will look at the networks of metabolism and gene regulation, which are at the core of Systems biology.

In a mixture of overview presentations, in-depth lectures on particular topics, guest lectures and panel discussions we will get a feeling for the common set of tools this unifying view relies upon. We will also learn to appreciate the truly transdisciplinary nature of networks research. In the end, active and regular participants will be able to speak their own first few phrases in "networks", this newest dialect in the language of complex Systems theory.

<link to the course catalogue>