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Didactic concept

NEST Desktop focuses on teaching university students, for whom significant programming skills cannot be assumed. The goal is to give them an introduction to computational neuroscience, and illustrate how computer simulations are used in the field. NEST Desktop provides an intuitive graphical user interface to NEST, which is actually a script-based simulation tool widely used in research. The idea is that students approach and understand important concepts in neuroscience by means of interactive construction, simulation and analysis of neuronal network models. This functionality is enabled by visual elements that can be manipulated with the computer mouse. No script-based programming is required at this stage. Thanks to this intuitive approach, learning is fast, and students can devote their time and attention to neuroscientific content rather than code syntax and data structures.

NEST Desktop still implements the standard workflow in the computational sciences: model creation, numerical simulation, statistical analysis and visualization of the simulation outcome. This logic represents another didactic dimension of NEST Desktop, beyond lowering the threshold for novices to use complex and powerful simulation engines like NEST. In addition, it is possible to inspect the automatically generated NEST code and even change it before simulation. This way, the students get some insight into the script-based interface of NEST, which enables more complex simulations.

For a typical course in computational neuroscience, the following combination of three course elements has proven to be effective:

  1. A theoretical introduction to computational neuroscience using slide-based presentations, possibly enhanced by the interactive use of NEST Desktop as a demonstrator during the lecture.
  2. An interactive tutorial explaining how NEST Desktop can be employed to work on course assignments.
  3. Structured lab reports exploiting the capabilities of NEST Desktop with regard to creation, simulation and analysis of models, potentially prepared in small working groups.

Please refer to the various examples of specific assignments offered in this documentation.