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.Simulate(100.0)
nest.raster_plot.from_device(spikes, hist=True)
plt.show()
Tutorials and guides

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

We also have network models of varyinig scales like the microcircuit model and the multi-area model.
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.
Cite NEST
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.