Introduction to Computational Neuroscience

Full course description

Many scientists regard the human brain as the most complex object in the known universe. It is not surprising therefore that studying the brain and its function is a challenging task. Any successful attempt at it requires neuroscientists to tackle it from several perspectives, each offering complementary insights. If we want to understand the brain and its structures, we need to identify their function: what do these structures do and why? A second requirement for understanding neural structures is identification of potential algorithms describing how they realize their identified function: what kind of information processing is carried out? Finally, we need to identify how such information processing can be implemented mechanistically in a neural structure as opposed to, for example, a personal computer: what are the physical and biological constraints under which the brain implements function?

Computational neuroscience integrates across these three points as it studies the information processing carried out by different structures of the nervous system in terms of biologically constrained models of brain function.

In this course students will receive an introduction into deep learning, spiking neuron models, and dynamical systems theory; learn the necessary mathematics and programming skills to design and simulate computational models; learn how computational models are applied for studying brain function (exemplified for decision making as well as for the structure-function relationship in the cortex); and discuss computational neuroscience from a philosophy of science perspective.