 }}){kind=icon}
-  }})
- 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
- import matplotlib.pyplot as plt
-
- 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 varying scales like the microcircuit model - and the multi-area model. -
-
API documentation
-
- 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. -
-
+  }})