Stud.IP Uni Oldenburg
University of Oldenburg
08.12.2021 02:44:31
neu241 - Computational Neuroscience - Introduction (Complete module description)
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Module label Computational Neuroscience - Introduction
Module code neu241
Credit points 12.0 KP
Workload 360 h

360 h

2 SWS Lecture
Total workload 60h: 30h contact/30h individual revision of lecture contents, test preparation

1 SWS Seminar
Total workload 45h: 15h contact/30h individual reading and test preparation

10.5 SWS Supervised exercise
Total workload 255h: 145h contact/110h individual work on portfolio tasks (programming, interpretation of simulation results)

Institute directory Department of Neurosciences
Applicability of the module
  • Master's Programme Neuroscience (Master) > Background Modules
Responsible persons
Kretzberg, Jutta (Module responsibility)
Kretzberg, Jutta (Module counselling)
Kretzberg, Jutta (Authorized examiners)
Greschner, Martin (Authorized examiners)
Ashida, Go (Authorized examiners)
Programming experience in Matlab (e.g. acquired by a 6 ECTS programming course)
Skills to be acquired in this module
++ Neurosci. knowlg.
+ Scient. Literature
+ Social skills
++ Interdiscipl. knowlg
++ Maths/Stats/Progr.
+ Data present./disc.

+ Scientific EnglishUpon successful completion of this course, students
• are able to implement and apply algorithms in Matlab
• have learned to handle scientific data independently
• have acquired theoretical and practical knowledge of advanced data analyis techniques
• know about computational model approaches on different levels of abstraction
• know how to perform model simulations for single cells and small neuronal networks
• can interpret simulation results in a neuroscientific context
Module contents

This course consists of six weeks with different topics, which are introduced in lectures, discussed in depth using selected literature in the seminar and consolidated in computer-based hands-on exercises (in Matlab). Portfolio tasks, mainly interpretation of programming results are given every day.

Weeks 1 and 2: Spike train analysis
response tuning, spike triggered average, receptive fields, linear-nonlinear model, spike correlation, linear reconstruction, classification

Weeks 3 and 4: Neuron models
Conductance-based single cell models using differerential equations (passive membrane equation, integrate and fire, Hodgkin Huxley, alpha synapses)

Weeks 5 and 6: Small network models
Feed-forward and feed-back networks, lateral inhibition, central pattern generator, spike-timing dependent plasticity, multi-compartment models

Reader's advisory

Skripts for each course day will be provided prior to / during the course

Copies of scientific articles for the seminar and as basis for portfolio assignments will be provided prior to the course

Recommended textbooks or other literature:
Dayan / Abbott: Theoretical Neuroscience: Computational and Mathematical Modeling of Neural
Systems. MIT Press (More text book chapters will be suggested prior to the course).

Language of instruction English
Duration (semesters) 1 Semester
Module frequency annually
Module capacity 18 (

Registration procedure / selection criteria: StudIP; sequence of registration, attandance in pre-meeting

Recommended in combination with:
neu770 Neuroscientific data analysis in Matlab (prior to the course)
neu250 Computational Neuroscience - Statistical Learning (after the course)

Modullevel / module level
Modulart / typ of module Pflicht o. Wahlpflicht / compulsory or optional
Lehr-/Lernform / Teaching/Learning method Master of Science: Neuroscience
Vorkenntnisse / Previous knowledge Programming experience, preferably in Matlab (e.g. acquired by a 6 ECTS programming course)
Course type Comment SWS Frequency Workload of compulsory attendance
2 WiSe 28
1 WiSe 14
10 WiSe 147
Total time of attendance for the module 189 h
Examination Time of examination Type of examination
Final exam of module
during the course
Portfolio, consisting of daily short tests, programming exercises, short reports