There has a recent surge of computational architectures inspired by how the brain works, and unlike previously, they actually perform well on certain tasks. At the same time advanced computational analysis is increasingly used to analyse and understand neural data. This course covers both these developments. After describing rigorous ways to describe neural activity mathematically, we present a number of the architectures recently used to do tasks like object recognition, memory, bayesian sampling, and cognition, as well as some hardware brain inspired hardware ideas.
The background needed to successfully take this course is a good grounding in mathematics, particularly with regard to probability and statistics, vectors and matrices. The mathematical level required is similar to that which would be obtained by students who did not have significant difficulties with the courses Mathematics for Informatics 1-4 taken in the first two years of the Informatics undergraduate syllabus. The Neural Computation (NC) course is a helpful but not necessary prerequisite. Biological realism is not as important an objective as in the NC course. Machine learning courses (LfD, PMR, IAML) will be also useful preparations.
Mark van Rossum.
Course Tutor: Sander Keemink
There will be two assessed assignments worth in total 25%. There will be an exam worth 75%.
First assignment . Deadline March 9. 4pm.
Deadline March 30.
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