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 |
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Zuständige Personen |
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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 Skills to be acquired/ competencies: ++ Neuroscience knowledge |
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 |
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Vorlesung | 2 | WiSe | 28 | |
Übung | 4 | WiSe | 56 | |
Präsenzzeit Modul insgesamt | 84 h |
Prüfung | Prüfungszeiten | Prüfungsform |
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Gesamtmodul | During the course (assignment tasks) |
Portfolio, consisting of short tests, programming tasks, and interpretation of modeling / data analysis results. |