NEST is built for simulations of large networks composed of interconnected simple neuron models. An example of such a model that can be simulated with NEST is the cortical microcircuit. Originally described by Potjans and Diesmann in 2014, the microcircuit model is a building block for larger networks and is of neuroscientific relevance because of the realistic proximity of neurons and synapses. It is a data-driven, full-scale spiking network model of 1 mm² of cortex which relates structure and activity. The model comprises four cortical layers, each containing an excitatory and an inhibitory population, with some 77,000 neurons in total.
A reference implementation is written in PyNEST, the easy Python interface to NEST with commands like Create(), Connect() and Simulate(). As a reference, it has been widely cited and the implementation has been reused in a considerable number of peer-reviewed papers since its original publication.