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University of Oldenburg
28.11.2021 03:44:24
neu320 - Introduction to Neurophysics (Complete module description)
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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 (Authorized examiners)
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
Reader's advisory
  • 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"
Modullevel / module level
Modulart / typ of module
Lehr-/Lernform / Teaching/Learning method Master of Science: Neuroscience
Vorkenntnisse / Previous knowledge Computer programming (preferably Matlab), basic mathematics (statistics, analysis, linear algebra)
Course type Comment SWS Frequency Workload of compulsory attendance
Lecture
WiSe 0
Seminar
WiSe 0
Exercises
WiSe 0
Total time of attendance for the module 0 h
Examination Time of examination Type of examination
Final exam of module
end of winter term
80% oral exam or written exam, 20% exercise work and presentation