neu320 - Introduction to Neurophysics (Vollständige Modulbeschreibung)

Modulbezeichnung	Introduction to Neurophysics
Modulkürzel	neu320
Kreditpunkte	6.0 KP
Workload	180 h ( 2 SWS Lecture total workload 90h: 28h contact / 62h background reading/exam preparation 2 SWS Supervised exercise total workload 90h: 28h contact / 62h self-conducted exercise work/literature reading )
Einrichtungsverzeichnis	Department für Neurowissenschaften
Verwendbarkeit des Moduls	Master Neuroscience (Master) > Background Modules
Zuständige Personen	Anemüller, Jörn (Modulverantwortung) Anemüller, Jörn (Prüfungsberechtigt) Dietz, Mathias (Prüfungsberechtigt)
Teilnahmevoraussetzungen	recommended in semester: 3 (with Matlab prereq.: 1)
Kompetenzziele	++ Neurosci. knowlg. + Independent research + Scient. Literature ++ Interdiscipl. knowlg. ++ Maths/Stats/Progr. + Data present./disc. Students will learn to recognize the dynamics in neuronal networks as the result of an interplay of physical, chemical and biological processes. Overview over major physical measurement procedures for the quantification of structure and function in neuronal systems. Using the language of mathematics as a fundamental tool for the description of underlying biophysical processes with stochastics, linear algebra, differential equations. Information as represented on different length- and timescales: From microscopic processes to macroscopic functional models. Learning and adaptation as adjustment of a biophysical system to its environment.
Modulinhalte	Biophysics of synaptic and neuronal transmission Single neuron models: Hodgkin Huxley model, integrate and fire model, firing rate model Biophysics of sensory systems in the auditory, visual and mechano-sensory modality Description of neuronal dynamics: Theory of dynamical systems, from microscopic to macroscopic activity Principles of neuronal activity measurments: from single-cell recordings to EEG, MEG and fMRI Functional description of small neuronal networks: Receptive fields and their description with linear and non-linear models The neuronal code: Spikes, spike trains, population coding, time- vs. rate-code Decoding neuronal activity and its applications Simulation of artificial neural networks as a functional model, Hopfield network, Boltzmann machine, Perceptron and deep networks Informationtheoretic approaches, stimulus statistics, entropy, mutual information Learning and plasticity, conditioning and reinforcement learning, Hebbian learning, long-term potentiation and long-term depression
Literaturempfehlungen	Chow, Gutkin, Hansel, Meunier, Dalibard (Eds.): Methods and Models in Neurophysics (2003) Dayan, Abbott: Theoretical Neuroscience (2005) Galizia, Lledo (Eds.): Neurosciences, from molecule to behauvior (2013) Gerstner, Kistler, Naud, Paninski: Neuronal Dynamics - From single neurons to networks and models of Cognition (2014) Rieke, Warland, de Ruyter van Steveninck, Bialek: Spikes - Exploring the neural code (1999) Schnupp, Nelken, King: Auditory Neuroscience (2010)
Links
Unterrichtssprache	Englisch
Dauer in Semestern	1 Semester
Angebotsrhythmus Modul	winter term / annually
Aufnahmekapazität Modul	30 ( Registration procedure / selection criteria: StudIP )
Hinweise	Recommended in combination with: 5.04.4012 Informationsverarbeitung und Kommunikation (phy350) Will also be offered in "M.Sc. Physik, Technik, Medizin"
Lehr-/Lernform	Master of Science: Neuroscience
Vorkenntnisse	Computer programming (preferably Matlab), basic mathematics (statistics, analysis, linear algebra)

Lehrveranstaltungsform	Angebotsrhythmus	Workload Präsenz
Vorlesung	WiSe	0
Seminar	WiSe	0
Übung	WiSe	0
Präsenzzeit Modul insgesamt		0 h

Prüfung	Prüfungszeiten	Prüfungsform
Gesamtmodul	end of winter term	80% oral exam or written exam, 20% exercise work and presentation