Learning about the conceptual and technical foundations of the major modelling paradigms in brain and cognitive science. Learning to critically asssess models in these fields, distinguishing between appropriate abstractions and inappropriate simplifications and understanding relations to models formulated in other paradigms.
How do brains implement high-level cognitive functions? How can modelling contribute to answering that question? In this course we consider the conceptual and technical foundations of the major modelling approaches in the brain and cognitive sciences, and explicitly investigate the commonalities and differences. As case studies, we look at models of single neurons (Hodgkin-Huxley, Fitzhugh-Nagumo, McCulloch-Pits, Rosenblatt), models of networks of neurons (Hopfield, Kohonen, Rumelhart & McClelland, Elman, Hebbian learning, backpropagation), and some basic symbolic and probabilistic models of categorization, reasoning, planning and language (k-means clustering, mixtures of Gaussians, expectation-maximization, natural deduction,situation calculus, HMM, CFG, PCFG). The lectures give brief refreshers on the used mathematical techniques (e.g., ordinary differential equations, vector- and matrix-algebra, logic, probability theory and rewrite grammars), but the emphasis will be on conceptual discussion of the various models, the acceptability of the simplifications they make, and their relations to each other. In the computerlabs we study the properties of the various models (using existing implementations in R). At the end of the course all students present an evaluation of a modelling paper from their own favorite field, discuss its relation to other modelling paradigms and to the modelling methodology discussed in the course.
Recommended prior knowledge
Students taking this course need to have a strong interest in modelling and brain and cognitive science. No specific computational and mathematical background is required, although bachelor-level linear algebra and some programming experience will help. The course is also open for motivated students in artificial intelligence and logic. Contact the lecturer (firstname.lastname@example.org) when in doubt.
Lectures, seminars, computer labs
Selection of original research papers, tutorial papers and textbook chapters.
Assignments/active participation (40%), presentation (20%), exam (40%)