@@ -29,20 +29,23 @@ + @@ -51,103 +54,99 @@

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
- - -
-
-
-
-
-

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()
-
-
- +
+
+
+
+

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()
+
+
-
- +
@@ -189,6 +188,7 @@

Here is a sample NEST script. Click each section and disco