neu240 - Computational Neuroscience - Introduction (Vollständige Modulbeschreibung)
Modulbezeichnung | Computational Neuroscience - Introduction |
Modulkürzel | neu240 |
Kreditpunkte | 9.0 KP |
Workload | 270 h |
Einrichtungsverzeichnis | Department für Neurowissenschaften |
Verwendbarkeit des Moduls |
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Zuständige Personen |
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Teilnahmevoraussetzungen | attendance in pre-meeting |
Kompetenzziele | Neurosci. knowlg. Expt. methods Independent research + Scient. literature + Social skills Interdiscipl. knowlg. ++ Maths/Stats/Progr. + Data present./disc. + Scientific English Ethics Upon successful completion of this course, students have acquired good programming skills (in Matlab) are able to implement and apply algorithms 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 four 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. Week 1: Background and Matlab preparation week practice of programming principles (functions,scripts, if, loops, structures, cell arrays) revision of neuroscience backgrounds (neuron, membrane, spike) Week 2: Spike train analysis response tuning, spike triggered average, receptive fields, linear-nonlinear model, spike correlation, linear reconstruction, classification Week 3: Neuron models Conductance-based single cell models using differerential equations (passive membrane equation, integrate and fire, Hodgkin Huxley, alpha synapses) Week 4: Network models small networks (lateral inhibition, central pattern generator) larger networks (Integrate and fire networks, rate models, inhibition-excitation balance, learning) |
Literaturempfehlungen | Dayan / Abbott: Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems. MIT Press (More text books will be suggested prior to the course). Scripts for each course day will be provided prior to / during the course Copies of scientific articles for the seminar will be provided prior to the course |
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Unterrichtssprache | Englisch |
Dauer in Semestern | 1 Semester |
Angebotsrhythmus Modul | jährlich |
Aufnahmekapazität Modul | unbegrenzt |
Hinweise | Course in the first half of the semester |
Lehrveranstaltungsform | Kommentar | SWS | Angebotsrhythmus | Workload Präsenz |
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Vorlesung | 1 | 14 | ||
Übung | 4 | 56 | ||
Seminar | 1 | 14 | ||
Präsenzzeit Modul insgesamt | 84 h |
Prüfung | Prüfungszeiten | Prüfungsform |
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Gesamtmodul | during the course |
Portfolio, consisting of daily short tests, programming exercises and short reports |