neu242 Computational Neuroscience - Encoding and Decoding (Vollständige Modulbeschreibung)

neu242 Computational Neuroscience - Encoding and Decoding (Vollständige Modulbeschreibung)

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Modulbezeichnung Computational Neuroscience - Encoding and Decoding
Modulkürzel neu242
Kreditpunkte 6,0 KP
Workload 180 h
Verwendbarkeit des Moduls
  • Master Neuroscience > Background Modules
Zuständige Personen
  • Greschner, Martin (Modulverantwortung)
  • Clemens, Jan (Prüfungsberechtigt)
  • Greschner, Martin (Prüfungsberechtigt)
  • Greschner, Martin (Modulberatung)
Teilnahmevoraussetzungen

Enrolment in Master program Neuroscience; Students from other study programs are welcome if space is available.This module requires good programming skills! (As taught in neu710 or neu715.)
Kompetenzziele
Upon completion of this module, students

- are able to implement and apply algorithms in Matlab or Python
- have learned to handle scientific data independently
- have acquired theoretical and practical knowledge of advanced data analysis techniques- can interpret simulation results in a neuroscientific context

Skills to be acquired/ competencies:

++          Neuroscience knowledge
+            Scientific Literature
+            Social skills
++          Maths/Stats/Programming
+            Data presentation/discussion
+            Scientific English
Modulinhalte

This course consists of three weeks full-time work on the topics encoding and decoding of spike trains, which are introduced in lectures, discussed in depth using selected literature in the seminar and consolidated in computer-based hands-on exercises (in Matlab or Python). Portfolio tasks consists of programming and the interpretation of the analyses.

Specific topics: response tuning, spike triggered average, receptive fields, linear-nonlinear model, spike correlation, linear reconstruction, classification
Literaturempfehlungen
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).
 
Links
Unterrichtssprache Englisch
Dauer in Semestern 1 Semester
Angebotsrhythmus Modul Annualy, second half of winter term (December to early January)
Aufnahmekapazität Modul 18
Modulart Wahlpflicht / Elective
Modullevel EB (Ergänzungsbereich / Complementary)
Vorkenntnisse This module requires good programming skills in Matlab and/or Python (As taught in neu710 or neu715.)
Lehrveranstaltungsform Kommentar SWS Angebotsrhythmus Workload Präsenz
Vorlesung 2 WiSe 28
Contact (hours): 28
Self-study and preparation for exam (hours): 32
Total Workload (hours): 60
Übung 4 WiSe 56
Contact (hours): 56
Self-study and preparation for exam (hours): 64
Total workload (hours): 120
Präsenzzeit Modul insgesamt 84 h
Prüfung Prüfungszeiten Prüfungsform
Gesamtmodul
During the course (assignment tasks)