Introduction to Computational Neuroscience
Course 0000000970 in SS 2018
General Data
| Course Type | lecture with integrated exercises |
|---|---|
| Semester Weekly Hours | 3 SWS |
| Organisational Unit | Chair of Real-Time Computer Systems (Prof. Wille komm.) |
| Lecturers | |
| Dates |
Tue, 09:45–12:30, 3999 |
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 | The lecture demonstrates the methods for analysis and modelling of neurons, neuronal systems, and behavior. Keywords: Spiking neurons, resting membrane potential, ion channels, action potential, Hodgkin-Huxley model, phase plane analysis, leaky integrate-and-fire model, synaptic transmission, synaptic plasticity. Neural networks: perceptron, Hebb's learning rule, Hopfield networks. Analysis of spike trains (reverse correlation) and firing rate (regression and system identification). Optimal estimation: minimum variance, maximum likelihood, maximum a-posteriori, mechanisms of sensory fusion. Modelling of sensorimotor systems. |
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| Links |
Course documents Additional information TUMonline entry |
Equivalent Courses (e. g. in other semesters)
| Semester | Title | Lecturers | Dates |
|---|---|---|---|
| SS 2017 | Introduction to Computational Neuroscience | ||
| SS 2016 | Introduction to Computational Neuroscience | ||
| SS 2015 | Introduction to Computational Neuroscience | ||
| SS 2014 | Introduction to Computational Neuroscience | ||
| SS 2013 | Introduction to Computational Neuroscience |