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We are entering an age of `big' computational neuroscience, in which neuralnetwork models are increasing in size and in numbers of underlying data sets.Consolidating the zoo of models into large-scale models simultaneouslyconsistent with a wide range of data is only possible through the effort oflarge teams, which can be spread across multiple research institutions. Toensure that computational neuroscientists can build on each other's work, it isimportant to make models publicly available as well-documented code. Thischapter describes such an open-source model, which relates the connectivitystructure of all vision-related cortical areas of the macaque monkey with theirresting-state dynamics. We give a brief overview of how to use the executablemodel specification, which employs NEST as simulation engine, and show itsruntime scaling. The solutions found serve as an example for organizing theworkflow of future models from the raw experimental data to the visualizationof the results, expose the challenges, and give guidance for the constructionof ICT infrastructure for neuroscience.

Usage and Scaling of an Open-Source Spiking Multi-Area Model of Monkey Cortex