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University of Oldenburg
28.11.2021 04:22:53
neu710 - Neuroscientific Data Analysis in Matlab (Complete module description)
Original version English Download as PDF
Module label Neuroscientific Data Analysis in Matlab
Module code neu710
Credit points 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

)
Institute directory Department of Neurosciences
Applicability of the module
  • Master's Programme Neuroscience (Master) > Skills Modules
Responsible persons
Kretzberg, Jutta (Module responsibility)
Kretzberg, Jutta (Authorized examiners)
Prerequisites
Skills to be acquired in this module

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

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
Module contents

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.

Reader's advisory
Pascal Wallisch: MATLAB for Neuroscientists, Elsevier, Oxford
Links
Language of instruction English
Duration (semesters) 1 Semester
Module frequency annually, winter term
Module capacity 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
Course type Comment SWS Frequency Workload of compulsory attendance
Lecture
1 14
Exercises
2 28
Seminar
1 14
Total time of attendance for the module 56 h
Examination Time of examination Type of examination
Final exam of module
during the course
practical exercise - hand in code and interpretation each week