Stud.IP Uni Oldenburg
Universität Oldenburg
28.11.2021 02:51:52
neu250 - Computational Neuroscience - Statistical Learning (Vollständige Modulbeschreibung)
Originalfassung Englisch PDF Download
Modulbezeichnung Computational Neuroscience - Statistical Learning
Modulkürzel neu250
Kreditpunkte 6.0 KP
Workload 180 h

1 SWS Lecture (VL)
Total workload 36 h: 14 h contact / 22 h individual revision of lecture contents, test preparation, and application to portfolio tasks

1 SWS Seminar (SE)
Total workload 36 h: 14 h contact / 22 h individual reading and test preparation

3 SWS Supervised exercise
Total workload 108 h: 42 h contact/ 66 h individual work on portfolio tasks (programming and interpretation of simulation or analysis results)

Einrichtungsverzeichnis Department für Neurowissenschaften
Verwendbarkeit des Moduls
  • Master Neuroscience (Master) > Background Modules
Zuständige Personen
Anemüller, Jörn (Modulverantwortung)
Anemüller, Jörn (Modulberatung)
Rieger, Jochem (Modulberatung)
Anemüller, Jörn (Prüfungsberechtigt)
Rieger, Jochem (Prüfungsberechtigt)
attendance in pre-meeting
Upon successful completion of this course, students
  • have refined their programming skills (in Matlab) in order to efficiently analyze large-scale experimental data
  • are able to implement a processing chain of prefiltering, statistical analysis and results visualization
  • have acquired an understanding of the theoretical underpinnings of the most common statistical analysis methods and basic machine learning principles
  • have practised using existing toolbox functions for complex analysis tasks
  • know how to implement new analysis algorithms in software from a given mathematical formulation
  • can interpret analysis results in a neuroscientific context
  • have applied these techniques to both single channel and multi-channel neurophysiological data

++ Neurosci. knowlg.
+ Scient. literature
+ Social skills
++ Interdiscipl. knowlg.
++ Maths/Stats/Progr.
+ Data present./disc.
+ Scientific English
  • data preprocessing, e.g., artifact detection and rejection, filtering, z-scoring, epoching
  • data handling for high-volume data in Matlab
  • introduction to relevant analysis toolbox software
  • theory of multi-dimensional statistical analysis approaches, such as multi-dimensional linear
  • regression, principal component analysis, independent component analysis, logistic regression,
  • gradient-based optimization
  • practical implementation from mathematical formulation to software code, debugging and unit testing
  • postprocessing and results visualization
  • consolidation during hands-on computer-based exercises (in Matlab)
  • introduction to selected specialized analysis approaches during the seminar
Wallisch et al.: MATLAB for Neuroscientists, 2nd Ed. Academic Press. More text books will be suggested prior to the course. Scientific articles: Copies of scientific articles for the seminar will be provided prior to the course
Unterrichtssprache Englisch
Dauer in Semestern 1 Semester
Angebotsrhythmus Modul jährlich
Aufnahmekapazität Modul 18 (
Recommended in combination with neu240 Computational Neuroscience - Introduction Shared course components with (cannot be credited twice): psy220 Human Computer Interaction

Course in the first half of the semester

Students without Matlab experience should take a Matlab course (e.g. neu710) first

Modullevel / module level
Modulart / typ of module Wahlpflicht / Elective
Lehr-/Lernform / Teaching/Learning method
Vorkenntnisse / Previous knowledge Programming experience is highly recommended, preferably in Matlab
Lehrveranstaltungsform Kommentar SWS Angebotsrhythmus Workload Präsenz
1 -- 14
3 -- 42
1 -- 14
Präsenzzeit Modul insgesamt 70 h
Prüfung Prüfungszeiten Prüfungsform
during the course
Portfolio, consisting of daily short tests, programming exercises and short reports