Computational Neuroscience: Basics (4 C, 2 SWS) [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

Admission requirements

Recommended previous knowledge

Number of repeat examinations permitted

Course frequency
each summer semester

Recommended semester
2 - 6

1 semester


Maximum number of students

Person responsible for module
Prof. Dr. Florentin Andreas Wörgötter