NEST Simulator

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.

This is a large-scale spiking model of the vision-related areas of the macaque cortex, including over 4 million neurons (Schmidt et al., 2018). It can be simulated with NEST but requires a larger amount of compute resources. Running the simulation at full scale requires a compute cluster and an advanced workflow to generate model structure from empirical data.

NEST offers the multi-area model and its workflow. Both were implemented following a thorough review process and provide a freely available reference and basis for subsequent work. Watch a video describing the first steps in working with this large multi-scale spiking network model.

One of the long-term core developers of NEST discusses the approach to neuronal network simulation and scientific tool development that has driven NEST development throughout its 25-year history.