neu241 - Computational Neuroscience - Introduction (Vollständige Modulbeschreibung)

Modulbezeichnung	Computational Neuroscience - Introduction
Modulkürzel	neu241
Kreditpunkte	12.0 KP
Workload	360 h ( 360 h 2 SWS Lecture Total workload 60h: 30h contact/30h individual revision of lecture contents, test preparation 1 SWS Seminar Total workload 45h: 15h contact/30h individual reading and test preparation 10.5 SWS Supervised exercise Total workload 255h: 145h contact/110h individual work on portfolio tasks (programming, interpretation of simulation results) )
Einrichtungsverzeichnis	Department für Neurowissenschaften
Verwendbarkeit des Moduls	Master Neuroscience (Master) > Background Modules
Zuständige Personen	Kretzberg, Jutta (Modulverantwortung) Kretzberg, Jutta (Modulberatung) Kretzberg, Jutta (Prüfungsberechtigt) Greschner, Martin (Prüfungsberechtigt) Ashida, Go (Prüfungsberechtigt)
Teilnahmevoraussetzungen	Programming experience in Matlab (e.g. acquired by a 6 ECTS programming course)
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
Modulinhalte	This course consists of six weeks with different topics, which are introduced in lectures, discussed in depth using selected literature in the seminar and consolidated in computer-based hands-on exercises (in Matlab). Portfolio tasks, mainly interpretation of programming results are given every day. Weeks 1 and 2: Spike train analysis response tuning, spike triggered average, receptive fields, linear-nonlinear model, spike correlation, linear reconstruction, classification Weeks 3 and 4: Neuron models Conductance-based single cell models using differerential equations (passive membrane equation, integrate and fire, Hodgkin Huxley, alpha synapses) Weeks 5 and 6: Small network models Feed-forward and feed-back networks, lateral inhibition, central pattern generator, spike-timing dependent plasticity, multi-compartment models
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).
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Unterrichtssprache	Englisch
Dauer in Semestern	1 Semester
Angebotsrhythmus Modul	annually
Aufnahmekapazität Modul	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) )
Modulart	Pflicht o. Wahlpflicht / compulsory or optional
Lehr-/Lernform	Master of Science: Neuroscience
Vorkenntnisse	Programming experience, preferably in Matlab (e.g. acquired by a 6 ECTS programming course)

Lehrveranstaltungsform	SWS	Angebotsrhythmus	Workload Präsenz
Vorlesung	2	WiSe	28
Seminar	1	WiSe	14
Übung	10	WiSe	147
Präsenzzeit Modul insgesamt			189 h

Prüfung	Prüfungszeiten	Prüfungsform
Gesamtmodul	during the course	Portfolio, consisting of daily short tests, programming exercises, short reports