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, and introducing methods how high-dimensional neural activity patterns can be represented and modelled, we present a number of the architectures recently used to do tasks like image understanding, memory and cognition, as well as some brain inspired hardware implementations.
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, as biological realism is not such an important objective as in the NC course. Machine learning courses (LfD, PMR, IAML) will be also useful preparations.
There will be two assessed assignments worth in total 25%. There will be an exam worth 75%.
First assignment . Deadline March 16th, 2016.Data file for first assignment
Deadline April 5th, 2016
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