Stud.IP Uni Oldenburg
University of Oldenburg
01.12.2021 07:42:45
neu241 - Computational Neuroscience - Introduction
Department of Neurosciences 12 KP
Module components Semester courses Wintersemester 2021/2022 Examination
Lecture
  • Limited access 6.03.241 - Computational Neuroscience - Introduction (Lecture) Show lecturers
    • Martin Greschner
    • Prof. Dr. Jutta Kretzberg
    • Dr. Go Ashida

    Monday: 09:00 - 12:00, weekly (from 06/12/21)
    Tuesday: 09:00 - 12:00, weekly (from 07/12/21)
    Wednesday: 09:00 - 12:00, weekly (from 08/12/21)
    Thursday: 09:00 - 12:00, weekly (from 09/12/21)
    Friday: 09:00 - 12:00, weekly (from 10/12/21)

    Content of the module: The topics - Biophysical neuron models - Network models - Spike train analysis - Statistical learning in neuroscience - Statistical learning for the analysis of neuronal population activity will be introduced in lectures, discussed in depth using selected literature in the seminar and consolidated in computer-based hands-on exercises.

Seminar
  • Limited access 6.03.241 - Computational Neuroscience - Introduction (Lecture) Show lecturers
    • Martin Greschner
    • Prof. Dr. Jutta Kretzberg
    • Dr. Go Ashida

    Monday: 09:00 - 12:00, weekly (from 06/12/21)
    Tuesday: 09:00 - 12:00, weekly (from 07/12/21)
    Wednesday: 09:00 - 12:00, weekly (from 08/12/21)
    Thursday: 09:00 - 12:00, weekly (from 09/12/21)
    Friday: 09:00 - 12:00, weekly (from 10/12/21)

    Content of the module: The topics - Biophysical neuron models - Network models - Spike train analysis - Statistical learning in neuroscience - Statistical learning for the analysis of neuronal population activity will be introduced in lectures, discussed in depth using selected literature in the seminar and consolidated in computer-based hands-on exercises.

Exercises
  • Limited access 6.03.243a - Computational Neuroscience - Introduction (Exercises) Show lecturers
    • Martin Greschner
    • Prof. Dr. Jutta Kretzberg
    • Dr. Go Ashida

    Tuesday: 14:15 - 15:45, weekly (from 07/12/21)
    Thursday: 14:15 - 15:45, weekly (from 09/12/21)

    Content of the module: The topics - Biophysical neuron models - Network models - Spike train analysis - Statistical learning in neuroscience - Statistical learning for the analysis of neuronal population activity will be introduced in lectures, discussed in depth using selected literature in the seminar and consolidated in computer-based hands-on exercises.

  • Limited access 6.03.243b - Computational Neuroscience - Introduction (Exercises) Show lecturers
    • Martin Greschner
    • Prof. Dr. Jutta Kretzberg
    • Dr. Go Ashida

    Tuesday: 14:15 - 15:45, weekly (from 07/12/21)
    Thursday: 14:15 - 15:45, weekly (from 09/12/21)

    Content of the module: The topics - Biophysical neuron models - Network models - Spike train analysis - Statistical learning in neuroscience - Statistical learning for the analysis of neuronal population activity will be introduced in lectures, discussed in depth using selected literature in the seminar and consolidated in computer-based hands-on exercises.

Notes for the module
Prerequisites
Programming experience in Matlab (e.g. acquired by a 6 ECTS programming course)
Capacity / number of participants 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)

)
Time of examination
during the course
Module examination
Portfolio, consisting of daily short tests, programming exercises, short reports
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
 

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