Introduction to Computational Neuroscience
Full course descriptionThe human brain is regarded by many scientists 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 mechanisms describing how a certain function can be brought about: what kind of information processing is carried out? Finally, we need to identify how such information processing can be implemented 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 lies at the junction of these three points with a strong focus on the second. Specifically, it studies the information processing carried out by different structures of the nervous system by investigating biologically plausible models of brain function. In this course students will receive an overview of the basic principles of connectionism and neural networks ranging from simple to complex models of neurons and their interconnections; learn how these models are used to study brain function for a wide range of topics including learning, decision making, and vision; and learn how computational neuroscience and more empirical fields such as neuroimaging and psychophysics can benefit from each other.
Course objectivesKnowledge of: a range of typical models used in computational neuroscience; how these models advance our understanding of the brain; the relation of these models to empirical research; the advantages and limitations of individual models as well as of the field as a whole.
1 Sep 2021
22 Oct 2021
Teaching methods:Lecture(s), PBL, Work in subgroups
Assessment methods:Attendance, Final paper, Participation
Keywords:connectionism, neural networks, neuroscience, inter-disciplinary integration.