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University of Oldenburg
28.11.2021 03:32:21
neu250 - Computational Neuroscience - Statistical Learning (Complete module description)
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Module label Computational Neuroscience - Statistical Learning
Module code neu250
Credit points 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)

Institute directory Department of Neurosciences
Applicability of the module
  • Master's Programme Neuroscience (Master) > Background Modules
Responsible persons
Anemüller, Jörn (Module responsibility)
Anemüller, Jörn (Module counselling)
Rieger, Jochem (Module counselling)
Anemüller, Jörn (Authorized examiners)
Rieger, Jochem (Authorized examiners)
attendance in pre-meeting
Skills to be acquired in this module
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
Module contents
  • 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
Reader's advisory
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
Language of instruction English
Duration (semesters) 1 Semester
Module frequency jährlich
Module capacity 18 (
Recommended in combination with neu240 Computational Neuroscience - Introduction
Shared course components with (cannot be credited twice): psy220 Human Computer Interaction
Reference text
Course in the first half of the semester Students without Matlab experience should take the optional Matlab course (1. week) of Computational Neuroscience - Introduction
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
Course type Comment SWS Frequency Workload of compulsory attendance
1 -- 14
3 -- 42
1 -- 14
Total time of attendance for the module 70 h
Examination Time of examination Type of examination
Final exam of module
during the course
Portfolio, consisting of daily short tests, programming exercises and short reports