Computational Neuroscience: Basics (4 C, 2 WLH) [B.Phy.7601(Bio)]
Learning outcome: Introduction to the different fields of Computational Neuroscience:
- Models of single neurons,
- Small networks,
- Implementation of all simple as well as more complex numerical computations with few neurons.
- Aspects of sensory signal processing (neurons as ?filters?),
- Development of topographic maps of sensory modalities (e.g. visual, auditory) in the brain,
- First models of brain development,
- Basics of adaptivity and learning,
- Basic models of cognitive processing.
Core skills: On completion the students will have gained...
- ...overview over the different sub-fields of Computational Neuroscience;
- ...first insights and comprehension of the complexity of brain function ranging across all sub-fields;
- ...knowledge of the interrelations between mathematical/modelling methods and the to-be-modelled substrate (synapse, neuron, network, etc.);
- ...access to the different possible model level in Computational Neuroscience.
Lehrveranstaltungen und Prüfungen
Lecture: Computational Neuroscience: Basics (2 WLH)
Examination: written examination (45 minutes)
Examination requirements: Having gained overview across the different sub-fields of Computational Neuroscience;
having acquired first insights into the complexity of across the whole bandwidth of brain
having learned the interrelations between mathematical/modelling methods and the tobe-
modelled substrate (synapse, neuron, network, etc.);
being able to realize different level of modelling in Computational Neuroscience.
28 h attendance time
92 h self-study time
Recommended previous knowledge
Number of repeat examinations permitted
each summer semester
2 - 6
Maximum number of students
Person responsible for module
Prof. Dr. Florentin Andreas Wörgötter