Computational Neuroscience: A Lecture Series from Models to Applications
Course 0000002916 in WS 2016/7
General Data
| Course Type | lecture |
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| Semester Weekly Hours | 2 SWS |
| Organisational Unit | Associate Professorship of Audio Information Processing (Prof. Seeber) |
| Lecturers | |
| Dates |
Assignment to Modules
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EI7646: Computational Neuroscience: Eine Ringvorlesung von Modellen bis zu Anwendungen / Computational Neuroscience: A Lecture Series from Models to Applications
This module is included in the following catalogs:- Catalogue of non-physics elective courses
Further Information
Courses are together with exams the building blocks for modules. Please keep in mind that information on the contents, learning outcomes and, especially examination conditions are given on the module level only – see section "Assignment to Modules" above.
| additional remarks | General overview: Anatomical and physiological basis of neuroscience (2 lectures, Luksch): - Neuroscience in general, evolution, model systems, general function (sensory organ → CNS → motor response), anatomy (vertebrate/human), general overview of the auditory and visual system and their most important elements. - neural transmission: resting and action potential, synaptic transmission, neuronal morphology, processing in dendrites, small networks, in vitro electrophysiology Modeling: Neural dynamics and coding (4 lectures, Herz, Leibold) - Modeling single neurons (classical computational neuroscience, spiking models, current models, firing rate models), or what can math/physics tell us about neurons? - Populations of neurons; (Sparse) coding, theory of neural networks, associative learning (Hebbian, STDP), reinforcement learning, supervised vs. unsupervised learning - fundamentals of neuronal signal processing and its modelling; neural encoding/decoding; correlations, reverse correlations, receptive fields; information theory Towards integration in the nervous system (4 lectures, Flanagin, Glasauer, MacNeilage, Sirota) - Learning and memory: Hippocampal formation, episodic memory and space representation - Spatial perception and Navigation - Psychophysics, perceptual decision making (human/animal, Diffusion models, Bayesian models) - fMRI (+ Modeling connections between brain regions, connectome) Engineering for Neuroscience and Neuroprothetics (3-4 lectures, Kleinsteuber, Seeber, Sirota) - Recording neural activity in vivo, multichannel electrophysiology, data acquisition, analysis and interpretation - Machine learning and information retrieval in high dimensional data - Engineering models of the brain - Application to hearing aids and Neuroprosthetics (cochlear implants) An overview of current research at the Bernstein Center for Computational Neuroscience Munich |
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| Links |
E-Learning course (e. g. Moodle) TUMonline entry |