-{% include "sphinx_material/header.html" %} + {% include "sphinx_material/header.html" %} - + + @@ -51,197 +56,219 @@

Welcome to the NEST Simulator documentation!

- Install NEST - +

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.

- Tutorials and examples - PyNEST API - Built-in Models - Custom models - Further topics -
-
- Contact us + Install NEST + Tutorials and examples + PyNEST API + Available models + How NEST works
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Here is a sample NEST script. Click each section and discover related topics!

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

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-
- 

-            import nest
-
- +
+ 

+                  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
+                  })
+
+ - 

-            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'})
-
- + 

+                  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)
-
- + 

+                  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()
-
-
- + 

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

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- 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. +
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+
+
+

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

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+ 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. +
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+

API documentation

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

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. -
-
-
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-

Related projects

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- 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

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

-

Developer space

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- 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. -

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