Stud.IP Uni Oldenburg
Universität Oldenburg
06.12.2021 22:14:58
neu710 - Neuroscientific Data Analysis in Matlab (Vollständige Modulbeschreibung)
Originalfassung Englisch PDF Download
Modulbezeichnung Neuroscientific Data Analysis in Matlab
Modulkürzel neu710
Kreditpunkte 6.0 KP
Workload 180 h

180 h

2 SWS Lecture (VL) and Seminar (SE)
Total workload 90 h: 28 h contact / 62 h individual preparation and working on assignments

2 SWS Supervised exercise (UE)
Total workload 90 h: 28 h contact / 62 h individual preparation and working on assignments

Einrichtungsverzeichnis Department für Neurowissenschaften
Verwendbarkeit des Moduls
  • Master Neuroscience (Master) > Skills Modules
Zuständige Personen
Kretzberg, Jutta (Modulverantwortung)
Kretzberg, Jutta (Prüfungsberechtigt)

+ Neurosci. knowlg.
+ Social skills
+ Interdiscipl. knowlg.
++ Maths/Stats/Progr.
+ Scientific English

Upon successful completion of this course, students

  • understand basic programming concepts.
  • have good knowledge about the most important aspects of the programming language Matlab and are able to write their own programs.
  • have basic knowledge in statistical testing.
  • have developed and applied a programs for the analysis of electrophysiological data.
  • have practiced the interpretation of data analysis results in a neuroscience context

In each of the seven weeks, one or two specific topics are introduced in the lecture, practiced in the exercises and applied to electrophysiological data in a programming task:

Matlab basics: Matlab windows, work space, vectors & matrices, saving & loading, graphics, scripts, functions

  • Data types: numbers, logicals, text, categorical
  • Control flow: if statements, loops (for, while)
  • Software development: Flow charts, testing, debugging
  • Working with data: Searching & sorting, logical indexing
  • Advanced data types: sparse matrices, 3D matrices, cell arrays, structures, tables
  • Statistics: random numbers, probability distributions, descriptive statistics, inferential statistics
  • Application data analysis: Implementation of spike train analysis methods and graphics, function handles
  • Application Modelling: curve fitting, simulation of time series

With completing the seven tasks, each participant develops a toolbox of the most common analysis methods for electrophysiological (spike and continuous) data. In addition to writing and commenting code, the programs are applied to experimental data. The tasks include questions about the interpretation of these analysis results.

Hence, the goal of this module is two-fold: Learning the programming language Matlab and analysis methods for electrophysiological data.

Pascal Wallisch: MATLAB for Neuroscientists, Elsevier, Oxford
Unterrichtssprache Englisch
Dauer in Semestern 1 Semester
Angebotsrhythmus Modul annually, winter term
Aufnahmekapazität Modul 24
Modullevel / module level MM (Mastermodul / Master module)
Modulart / typ of module je nach Studiengang Pflicht oder Wahlpflicht
Lehr-/Lernform / Teaching/Learning method
Vorkenntnisse / Previous knowledge basic knowledge of math and statistics
Lehrveranstaltungsform Kommentar SWS Angebotsrhythmus Workload Präsenz
1 14
2 28
1 14
Präsenzzeit Modul insgesamt 56 h
Prüfung Prüfungszeiten Prüfungsform
during the course
practical exercise - hand in code and interpretation each week