1. NEST provides over 50 neuron models many of which have been published. Choose from simple integrate-and-fire neurons with current or conductance based synapses, over the Izhikevich or AdEx models, to Hodgkin-Huxley models.

  2. NEST provides over 10 synapse models, including short-term plasticity (Tsodyks & Markram) and different variants of spike-timing dependent plasticity (STDP).

  3. NEST provides the possibility to create spatially-structured networks. (Guide to spatially-structured networks)

  4. NEST provides many examples that help you getting started with your own simulation project.

  5. NEST offers convenient and efficient commands to define and connect large networks, ranging from algorithmically determined connections to data-driven connectivity.

  6. NEST lets you inspect and modify the state of each neuron and each connection at any time during a simulation.

  7. NEST is fast and memory efficient. It makes best use of your multi-core computer and compute clusters with minimal user intervention.

  8. NEST runs on a wide range of UNIX-like systems, from MacBooks to supercomputers.

  9. NEST has minimal dependencies. All it really needs is a C++ compiler. Everything else is optional.

  10. NEST developers are using agile continuous integration-based workflows in order to maintain high code quality standards for correct and reproducible simulations.

  11. NEST has one of the largest and most experienced developer communities of all neural simulators. NEST was first released in 1994 under the name SYNOD and has been extended and improved ever since.

  12. NEST is open source software and is licensed under the GNU General Public License v2 or later.

General Features

  • Python based user interface (PyNEST)

  • Built-in simulation language interpreter (SLI)

  • Multi-threading to use multi-processor machines efficiently

  • MPI-parallelism to use computer clusters and super computers

Neuron models

  • Integrate and fire (IAF) neuron models with current based synapses (delta-, exponential- and alpha-function shaped)

  • Integrate and fire neuron models with conductance-based synapses

  • Adaptive-exponential integrate and fire neuron model (AdEx) (Brette & Gerstner, 2005)- the standard in the FACETS EU project ([1])

  • MAT2 neuron model (Kobayashi et al. 2009)

  • Hodgkin-Huxley type models with one compartment

  • Neuron models with few compartments

Synapse models

  • Static synapses

  • Spike-timing dependent plasticity (STDP)

  • Short-term plasticity (Tsodyks et al. 2000)

  • Neuromodulatory synapses using dopamine

Spatially structured networks


  • Interface to the Multi Simulator Coordinator MUSIC

  • Backend for the simulator-independent modeling tool PyNN



  • After installation NEST can be verified by an automatic testsuite

  • The testsuite is automatically run after each modification of the NEST sources. You can watch the current status on our Continuous Integration system.

Supported platforms

  • Linux

  • Mac OS X

  • Virtual machines for use under Windows

By support we mean that we regularly test and use NEST on recent versions of these systems and that NEST therefore should work on those systems. It should not be construed as any warranty that NEST will run on any particular system.