Course details


Neuroinformatics (Lecture + Tutorial)

L + T
WS 2020 Prof. Dr. Gordon Pipa ONLINE
B.Sc modules:
CS-BP-NI - Neuroinformatics
CS-BWP-NI - Neuroinformatics
KOGW-PM-NI - Neuroinformatics
KOGW-WPM-NI - Neuroinformatics

CS-MW - Master elective course
Thu: 14-16
Fri: 10-12
Fri: 12-14

Prerequisites: Applied Mathematics, or Linear Algebra, Analysis. In this lecture, we will discuss cutting edge approaches from the field of neuroinformatics. The aim of the lecture is to familiarize the students with the concept of modelling and abstracting data and the up to date knowledge about computational processes in the brain. After a short introduction covering probability theory as well as linear models for regression and classification, we will start a journey through the fields of graphical models and liquid computing. In the last part of the lecture we will conclude with an outlook on self-organization with the goal of optimizing information processing in complex systems such as the brain. In order to link the knowledge acquired in this course with scientific questions, a 30 minute “3W session” will be offered in every second lecture. The three big W’s are: Why should I learn this / what can I use it for / how can it be important for my bachelor’s / master’s thesis? The lecture will be supplemented by a block seminar on decoding neuronal activity, at the beginning of the semester break. This course is intended for bachelor students in their third term and for master students as an elective course.