neu242 - Computational Neuroscience - Encoding and Decoding (Vollständige Modulbeschreibung)

Modulbezeichnung	Computational Neuroscience - Encoding and Decoding
Modulkürzel	neu242
Kreditpunkte	6.0 KP
Workload	180 h
Einrichtungsverzeichnis	Department für Neurowissenschaften
Verwendbarkeit des Moduls	Master Neuroscience (Master) > 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	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)	Portfolio, consisting of short tests, programming tasks, and interpretation of modeling / data analysis results.