Department of Neurosciences |
6 KP |
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Module components |
Semester courses Sommersemester 2019 |
Examination |
Lecture
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6.03.250 - Computational Neuroscience - Statistical Learning
- Prof. Dr. Jutta Kretzberg
- Prof. Dr. Jochem Rieger
- Priv.-Doz. Dr. Jörn Anemüller
Dates on Tuesday, 26.03.2019 11:00 - 11:30, Tuesday, 02.04.2019 - Thursday, 04.04.2019 10:00 - 16:00, Friday, 05.04.2019, Tuesday, 09., 04.2019 09:00 - 16:00, Wednesday, 10.04.2019 10:00 - 16:00, Thursday, 11.04.2019, Tuesday, 23.04.2019 09:00 - 16:00, Wednesday, 24.04.2019 10:00 - 16:00, Thursday, 25.04.2019 09:00 - 16:00, Friday, 26.04.2019 09:00 - 12:00 ...(more)
Content of the module:
The topics
- 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
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Inhalt der Veranstaltung:
Die Themen
- Statistisches Lernen in den Neurowissenschaften
- Statistisches Lernen für die Auswertung neuronaler Populations-Daten
werden in der Vorlesung eingeführt, durch passende Literatur im Seminar vertieft und in Übungsaufgaben am Computer praktisch umgesetzt.
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Exercises
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6.03.250 - Computational Neuroscience - Statistical Learning
- Prof. Dr. Jutta Kretzberg
- Prof. Dr. Jochem Rieger
- Priv.-Doz. Dr. Jörn Anemüller
Dates on Tuesday, 26.03.2019 11:00 - 11:30, Tuesday, 02.04.2019 - Thursday, 04.04.2019 10:00 - 16:00, Friday, 05.04.2019, Tuesday, 09., 04.2019 09:00 - 16:00, Wednesday, 10.04.2019 10:00 - 16:00, Thursday, 11.04.2019, Tuesday, 23.04.2019 09:00 - 16:00, Wednesday, 24.04.2019 10:00 - 16:00, Thursday, 25.04.2019 09:00 - 16:00, Friday, 26.04.2019 09:00 - 12:00 ...(more)
Content of the module:
The topics
- 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
_____
Inhalt der Veranstaltung:
Die Themen
- Statistisches Lernen in den Neurowissenschaften
- Statistisches Lernen für die Auswertung neuronaler Populations-Daten
werden in der Vorlesung eingeführt, durch passende Literatur im Seminar vertieft und in Übungsaufgaben am Computer praktisch umgesetzt.
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Seminar
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-
6.03.250 - Computational Neuroscience - Statistical Learning
- Prof. Dr. Jutta Kretzberg
- Prof. Dr. Jochem Rieger
- Priv.-Doz. Dr. Jörn Anemüller
Dates on Tuesday, 26.03.2019 11:00 - 11:30, Tuesday, 02.04.2019 - Thursday, 04.04.2019 10:00 - 16:00, Friday, 05.04.2019, Tuesday, 09., 04.2019 09:00 - 16:00, Wednesday, 10.04.2019 10:00 - 16:00, Thursday, 11.04.2019, Tuesday, 23.04.2019 09:00 - 16:00, Wednesday, 24.04.2019 10:00 - 16:00, Thursday, 25.04.2019 09:00 - 16:00, Friday, 26.04.2019 09:00 - 12:00 ...(more)
Content of the module:
The topics
- 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
_____
Inhalt der Veranstaltung:
Die Themen
- Statistisches Lernen in den Neurowissenschaften
- Statistisches Lernen für die Auswertung neuronaler Populations-Daten
werden in der Vorlesung eingeführt, durch passende Literatur im Seminar vertieft und in Übungsaufgaben am Computer praktisch umgesetzt.
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Hinweise zum Modul |
Prerequisites |
attendance in pre-meeting |
Reference text |
Course in the first half of the semester Students without Matlab experience should take the optional Matlab course (1. week) of Computational Neuroscience - Introduction |
Kapazität/Teilnehmerzahl |
18 ( Recommended in combination with neu240 Computational Neuroscience - Introduction
Shared course components with (cannot be credited twice): psy220 Human Computer Interaction )
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Prüfungszeiten |
during the course |
Module examination |
Portfolio, consisting of daily short tests, programming exercises and short reports |
Skills to be acquired in this module |
Upon successful completion of this course, students
- have refined their programming skills (in Matlab) in order to efficiently analyze large-scale experimental data
- are able to implement a processing chain of prefiltering, statistical analysis and results visualization
- have acquired an understanding of the theoretical underpinnings of the most common statistical analysis methods and basic machine learning principles
- have practised using existing toolbox functions for complex analysis tasks
- know how to implement new analysis algorithms in software from a given mathematical formulation
- can interpret analysis results in a neuroscientific context
- have applied these techniques to both single channel and multi-channel neurophysiological data
++ Neurosci. knowlg.
+ Scient. literature
+ Social skills
++ Interdiscipl. knowlg.
++ Maths/Stats/Progr.
+ Data present./disc.
+ Scientific English |
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