NEST is a simulator for spiking neural network models that focuses on the dynamics, size and structure of neural systems, rather than on the exact morphology of individual neurons. It is ideal for networks of any size, including models of information processing (e.g. in the visual or auditory cortex of mammals), models of network activity dynamics (e.g. laminar cortical networks or balanced random networks) and models of learning and plasticity. NEST is openly available for download.

Our offer

  • Widely used in computational neuroscience, neurorobotics and machine learning
  • Perform simulations through PyNEST, PyNN and NEST Desktop graphical user interface
  • Extend NEST without programming experience with the NESTML modelling language
  • Receive support and inspiration from the vibrant NEST developer community and the EBRAINS High Level Support Team (HLST)
  • Runs on laptops and scales to future exascale computers, thanks to NEST parallel simulation technology advances


Perform spiking neural network simulations with NEST

NEST is a command line tool for simulating neural networks. Such simulations try to follow the logic of an electrophysiological experiment. The main difference is that it takes place inside a computer, rather than the physical world. To simulate neural networks, NEST can be used interactively from the Python prompt. NEST allows users to create neuron models and devices, connect neurons to devices, specify synapse properties and simulate the network for a given number of milliseconds.

User story

Microcircuit model

Microcircuit model (Potjans and Diesmann, 2014)

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.

User story

Multi-area model

Multi-area model (Schmidt et al., 2018)

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.


Learn about the philosophy behind NEST

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.


Get involved in the NEST community

NEST Conference 2019 at NMBU in Ås, Norway

Since its first release in 1994, NEST has gathered a large and experienced user and developer community.

The community ensures systematic code review and continuous integration testing to maintain high code quality standards. Beyond daily collaboration via GitHub, users and developers can interact via the NEST User Mailing List, NEST Hackathons and fortnightly Open NEST Developer Video Conferences, as well as the annual NEST Conference. At the conference, everyone is welcome to join talks and discussions, share success stories, exchange advice and learn about current developments in and beyond NEST spiking network simulation and its applications.


Potjans T.C., Diesmann M. (2014) The cell-type specific cortical microcircuit: relating structure and activity in a full-scale spiking network model. Cerebral Cortex 24(3):785–806.

Schmidt M., Bakker R., Shen K., Bezgin G., Diesmann M., van Albada S.J. (2018) A multi-scale layer-resolved spiking network model of resting-state dynamics in macaque visual cortical areas. PLoS Comput Biol 14(10): e1006359.

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