Stud.IP Uni Oldenburg
Universität Oldenburg
06.12.2021 21:45:59
neu241 - Computational Neuroscience - Introduction (Veranstaltungsübersicht)
Department für Neurowissenschaften 12 KP
Modulteile Semesterveranstaltungen Wintersemester 2021/2022 Prüfungsleistung
Vorlesung
  • Eingeschränkter Zugang 6.03.241 - Computational Neuroscience - Introduction (Lecture) Lehrende anzeigen
    • Martin Greschner
    • Prof. Dr. Jutta Kretzberg
    • Dr. Go Ashida

    Montag: 09:00 - 12:00, wöchentlich (ab 06.12.2021)
    Dienstag: 09:00 - 12:00, wöchentlich (ab 07.12.2021)
    Mittwoch: 09:00 - 12:00, wöchentlich (ab 08.12.2021)
    Donnerstag: 09:00 - 12:00, wöchentlich (ab 09.12.2021)
    Freitag: 09:00 - 12:00, wöchentlich (ab 10.12.2021)

    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
  • Eingeschränkter Zugang 6.03.241 - Computational Neuroscience - Introduction (Lecture) Lehrende anzeigen
    • Martin Greschner
    • Prof. Dr. Jutta Kretzberg
    • Dr. Go Ashida

    Montag: 09:00 - 12:00, wöchentlich (ab 06.12.2021)
    Dienstag: 09:00 - 12:00, wöchentlich (ab 07.12.2021)
    Mittwoch: 09:00 - 12:00, wöchentlich (ab 08.12.2021)
    Donnerstag: 09:00 - 12:00, wöchentlich (ab 09.12.2021)
    Freitag: 09:00 - 12:00, wöchentlich (ab 10.12.2021)

    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.

Übung
  • Eingeschränkter Zugang 6.03.243a - Computational Neuroscience - Introduction (Exercises) Lehrende anzeigen
    • Martin Greschner
    • Prof. Dr. Jutta Kretzberg
    • Dr. Go Ashida

    Dienstag: 14:15 - 15:45, wöchentlich (ab 07.12.2021)
    Donnerstag: 14:15 - 15:45, wöchentlich (ab 09.12.2021)

    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.

  • Eingeschränkter Zugang 6.03.243b - Computational Neuroscience - Introduction (Exercises) Lehrende anzeigen
    • Martin Greschner
    • Prof. Dr. Jutta Kretzberg
    • Dr. Go Ashida

    Dienstag: 14:15 - 15:45, wöchentlich (ab 07.12.2021)
    Donnerstag: 14:15 - 15:45, wöchentlich (ab 09.12.2021)

    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.

Hinweise zum Modul
Teilnahmevoraussetzungen
Programming experience in Matlab (e.g. acquired by a 6 ECTS programming course)
Kapazität/Teilnehmerzahl 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)

)
Prüfungszeiten
during the course
Prüfungsleistung Modul
Portfolio, consisting of daily short tests, programming exercises, short reports
Kompetenzziele
++ 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