NMSAT is a python package that provides a set of tools to build, simulate and analyse neuronal microcircuit models with any degree of complexity, as well as to probe the circuits with arbitrarily complex input stimuli / signals and to analyse the relevant functional aspects of single neuron, population and network dynamics. It provides a high-level wrapper for PyNEST (which is used as the core simulation engine). As such, the complexity of the microcircuits analysed and their building blocks (neuron and synapse models, circuit topology and connectivity, etc.), are determined by the models available in NEST. The use of NEST allows efficient and highly scalable simulations of very large and complex circuits, constrained only by the computational resources available to the user. The modular design allows the user to specify numerical experiments with varying degrees of complexity depending on concrete research objectives. The generality of some of these experiments allows the same types of measurements to be performed on a variety of different circuits, which can be useful for benchmarking and comparison purposes. Additionally, the code was designed to allow an effortless migration across computing systems, i.e. the same simulations can be executed in a local machine, in a computer cluster or a supercomputer, with straightforward resource allocation.

The authors acknowledge the computing time granted by the JARA-HPC Vergabegremium on the supercomputer JURECA at Forschungszentrum Jülich used for testing the software. We acknowledge partial support by the Erasmus Mundus Joint Doctoral Program EuroSPIN, the German Ministry for Education and Research (Bundesministerium für Bildung und Forschung) BMBF Grant 01GQ0420 to BCCN Freiburg, the Helmholtz Alliance on Systems Biology (Germany), the Initiative and Networking Fund of the Helmholtz Association, the Helmholtz Portfolio theme 'Supercomputing and Modeling for the Human Brain'.