Introduction to Computational Neuroscience
Module EI7322
This module handbook serves to describe contents, learning outcome, methods and examination type as well as linking to current dates for courses and module examination in the respective sections.
Module version of WS 2017/8 (current)
There are historic module descriptions of this module. A module description is valid until replaced by a newer one.
Whether the module’s courses are offered during a specific semester is listed in the section Courses, Learning and Teaching Methods and Literature below.
| available module versions | ||
|---|---|---|
| WS 2017/8 | SS 2017 | SS 2014 |
Basic Information
EI7322 is a semester module in English language at Master’s level which is offered in summer semester.
This module description is valid from SS 2014 to WS 2018/9.
| Total workload | Contact hours | Credits (ECTS) |
|---|---|---|
| 150 h | 45 h | 5 CP |
Content, Learning Outcome and Preconditions
Content
The course demonstrates computational methods for analysis and modelling of neurons, neuronal systems, and behavior. On the neuron level it covers different mathematical abstractions and computational implementations of spiking neurons like the Hodgkin-Huxley, Izhikevich, leaky integrate-and-fire, or multi-compartmental models, as well as models of synaptic transmission and -plasticity. On the systems level (i.e. network networks of neurons) it covers the Perceptron, Hopfield networks, Hebb’s learning rule as well as selected network topologies from neurobiology.
Further topics include the analysis of spike trains (reverse correlation) and firing rate (regression and system identification). On a behavioral level students gain knowledge about optimal estimation: minimum variance, maximum likelihood, maximum a-posteriori, and mechanisms of sensory fusion. In the lab course students will utilize and apply the gained knowledge in a multi-level example: the modelling of sensorimotor systems.
Further topics include the analysis of spike trains (reverse correlation) and firing rate (regression and system identification). On a behavioral level students gain knowledge about optimal estimation: minimum variance, maximum likelihood, maximum a-posteriori, and mechanisms of sensory fusion. In the lab course students will utilize and apply the gained knowledge in a multi-level example: the modelling of sensorimotor systems.
Learning Outcome
After completion of the module students understand simple models of neuronal functions such as “leaky integrate and fire-models”; and know how to apply larger models of computational neuroscience to describe observed neuronal behavior. They are able to describe complex neuronal functionality in terms of models of simple neural circuitry. They know how to transfer principles of neural computation to technical applications such as motor control.
Preconditions
Basics of System Theory
Basic of Computer Science
Basic of Computer Science
Courses, Learning and Teaching Methods and Literature
Learning and Teaching Methods
Neuronal models and components are introduced during the lectures. They are transferrred into case-studies during the exercises to introduce and combine examples and to allow students to further investigate while experimenting during group work to provide hands-on experience with computer models to build own functions like motor control.
Media
- Powerpoint Presentations
- Script
- Exercises with Programming examples on the computer
- Script
- Exercises with Programming examples on the computer
Literature
- P. Dayan, L. Abbott, Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems (2005),
MIT Press, ISBN 978-0262541855
- T. Trappenberg, Fundamentals of Computational Neuroscience (2010), Oxford UP, ISBN 978-0199568413
MIT Press, ISBN 978-0262541855
- T. Trappenberg, Fundamentals of Computational Neuroscience (2010), Oxford UP, ISBN 978-0199568413
Module Exam
Description of exams and course work
During a written exam (90 min) students need to reproduce computational models of brain function and assess the
suitability of different principles for modeling tasks. Students need to demonstrate simple transfer and extension of
models to possibly new sensory or motor modalities.
suitability of different principles for modeling tasks. Students need to demonstrate simple transfer and extension of
models to possibly new sensory or motor modalities.
Exam Repetition
There is a possibility to take the exam in the following semester.