neu320 - Introduction to Neurophysics (Complete module description)

Module label	Introduction to Neurophysics
Module code	neu320
Credit points	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 )
Institute directory	Department of Neurosciences
Applicability of the module	Master's Programme Neuroscience (Master) > Background Modules
Responsible persons	Anemüller, Jörn (module responsibility) Anemüller, Jörn (authorised to take exams) Dietz, Mathias (authorised to take exams)
Prerequisites	recommended in semester: 3 (with Matlab prereq.: 1)
Skills to be acquired in this module	++ 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.
Module contents	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
Recommended reading	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
Language of instruction	English
Duration (semesters)	1 Semester
Module frequency	winter term / annually
Module capacity	30 ( Registration procedure / selection criteria: StudIP )
Reference text	Recommended in combination with: 5.04.4012 Informationsverarbeitung und Kommunikation (phy350) Will also be offered in "M.Sc. Physik, Technik, Medizin"

Type of course	Frequency	Workload of compulsory attendance
Lecture	WiSe	0
Seminar	WiSe	0
Exercises	WiSe	0
Total module attendance time		0 h

Examination	Prüfungszeiten	Type of examination
Final exam of module	end of winter term	80% oral exam or written exam, 20% exercise work and presentation