Introduction to Computational Neuroscience
Module EI7322
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.
Whether the module’s courses are offered during a specific semester is listed in the section Courses, Learning and Teaching Methods and Literature below.
available module versions | ||
---|---|---|
WS 2017/8 | SS 2017 | SS 2014 |
Basic Information
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
Content
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.
Learning Outcome
Preconditions
Basic of Computer Science
Courses, Learning and Teaching Methods and Literature
Learning and Teaching Methods
Media
- Script
- Exercises with Programming examples on the computer
Literature
MIT Press, ISBN 978-0262541855
- T. Trappenberg, Fundamentals of Computational Neuroscience (2010), Oxford UP, ISBN 978-0199568413
Module Exam
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.
Exam Repetition
There is a possibility to take the exam in the following semester.