Introduction to Computational Neuroscience
This module handbook serves to describe contents, learning outcome, methods and examination type as well as linking to current dates for courses and module examination in the respective sections.
Module version of WS 2017/8 (current)
There are historic module descriptions of this module. A module description is valid until replaced by a newer one.
|available module versions|
|WS 2017/8||SS 2017||SS 2014|
EI7322 is a semester module in English language at Master’s level which is offered in summer semester.
This module description is valid from SS 2014 to WS 2018/9.
|Total workload||Contact hours||Credits (ECTS)|
|150 h||45 h||5 CP|
Content, Learning Outcome and Preconditions
Further topics include the analysis of spike trains (reverse correlation) and firing rate (regression and system identification). On a behavioral level students gain knowledge about optimal estimation: minimum variance, maximum likelihood, maximum a-posteriori, and mechanisms of sensory fusion. In the lab course students will utilize and apply the gained knowledge in a multi-level example: the modelling of sensorimotor systems.
Basic of Computer Science
Courses, Learning and Teaching Methods and Literature
Learning and Teaching Methods
- Exercises with Programming examples on the computer
MIT Press, ISBN 978-0262541855
- T. Trappenberg, Fundamentals of Computational Neuroscience (2010), Oxford UP, ISBN 978-0199568413
Description of exams and course work
suitability of different principles for modeling tasks. Students need to demonstrate simple transfer and extension of
models to possibly new sensory or motor modalities.
There is a possibility to take the exam in the following semester.