Welcome to the NEST Simulator documentation!

NEST is used in computational neuroscience to model and study behavior of large networks of neurons. The models describe single neuron and synapse behavior and their connections. Different mechanisms of plasticity can be used to investigate artificial learning and help to shed light on the fundamental principles of how the brain works.

NEST is ideal for networks of spiking neurons of any size, and scales flexibly from running on your laptop to high-performance computing systems involving hundreds of compute nodes.

Here is a sample NEST script. Click each section and discover related topics!

                  import nest

                  neurons = nest.Create("iaf_psc_alpha", 10000, {
                      "V_m": nest.random.normal(-5.0),
                      "I_e": 1000.0

                  input = nest.Create("noise_generator", params={
                      "amplitude": 500.0
                  nest.Connect(input, neurons, syn_spec={'synapse_model': 'stdp_synapse'})

                  spikes = nest.Create("spike_recorder", params={
                      'record_to': 'ascii',
                      'label': 'excitatory_spikes'
                  nest.Connect(neurons, spikes)

                  nest.raster_plot.from_device(spikes, hist=True)

Tutorials and guides

If you're new to NEST, check out our PyNEST tutorials, where you can learn about the NEST interface and how to build networks.
We also provide an in depth look at spatially structured networks.
Need to convert scripts written for NEST 2.x into NEST 3.x and beyond? Take a look at our reference guide.

Learning from example

Our extensive list of example scripts showcase the many models and methods you can use for your project.
We also have network models of varyinig scales like the microcircuit model and the multi-area model.

API documentation

Need to look up a command for NEST? Browse all our available functions.

Related projects

NEST is one among a set of awesome tools and resources for researchers in neuroscience, robotics, and beyond. If you're looking for ways to analyze your results, compare with other simulators, or want to use a graphical user interface, we have some ideas for you. See our list of related projects.


Did you use NEST in your research? Please cite us! You can also access logo for posters and presentations here.

Developer space

All model implementations and simulation algorithms in NEST are thoroughly tested and highly optimized. We employ a modern development process, continuous integration, and code reviews to ensure that the NEST code is rock solid at all times. If you want the gritty details and find out how it's done come to the dark side! See our developer facing documentation.