NEST Simulator documentation¶
Welcome!¶
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 offers convenient and efficient commands to define and connect large networks, ranging from algorithmically determined connections to data-driven connectivity. Create connections between neurons using numerous synapse models from STDP to gap junctions.
Get started with NEST
Play PONG with NEST
Create 3D spatially structured networks
Investigate neuron and astrocyte interactions
Conceptual approach¶
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init:{
"theme": "base",
"themeVariables": {
"lineColor": "#f63",
"fontSize" : "18px"
}
}
}%%
flowchart LR
classDef nodeStyle color:#fff, stroke:#fff0, fill:#0E6A93;
classDef nodeStyle2 color:#000, stroke:#fff0, fill:#072f4200;
classDef nodeStyle3 color:#222, stroke:#fff0, fill:#bbb0;
exp --> nest-simulator
models -->nest-simulator
nest-simulator --> act
subgraph exp [Experimental protocols]
ir:::nodeStyle3
end
subgraph nest-simulator [nest-simulator]
direction TB
stimulating_devices:::nodeStyle2 --> simulate
simulate:::nodeStyle2 --> recording_devices:::nodeStyle2
end
subgraph models [Built-in or user provided models]
direction LR
neuron_models:::nodeStyle --> network:::nodeStyle
synapse_models:::nodeStyle --> network
end
subgraph act [Activity data]
smp:::nodeStyle3
end
class act sg
class exp sg
class models bg_models
class nest-simulator bg_devices
classDef sg fill:#bbb4, stroke:#4441, color:#111;
classDef bg_models fill:#bbb7, stroke:#3330, color:#000, font-weight: bold;
classDef bg_devices fill:#bbb7, stroke:#3330, color:#000, font-weight: bold;
%% Note that the spaces used in the "Simulation" text are the copy pasted unicode space character.
%% HTML spaces like %emsp; do not work
simulate(<img src="_static/img/nest_logo.png"/> Simulation \n)
neuron_models(<img src="_static/img/neuron.svg" /> \n Neuron Models)
synapse_models(<img src="_static/img/synapse.svg" /> \n Synapse Models )
stimulating_devices(<img src="_static/img/stimulatelight.svg" /> Stimulating Devices)
recording_devices(<img src="_static/img/recordinglight.svg" /> Recording Devices )
network(<img src="_static/img/networkbrainlight.svg" /> \n Network Models )
smp(<img src="_static/img/pynest/mc_neuron_sm.png" /> \n spike membrane potential, \n synaptic weights, etc.)
ir(<img src="_static/img/experiment.png"/> \n Input rates, input currents, \n timed sequences, etc.)
click network href "./networks/index.html"
click neuron_models href "./models/index_neuron.html"
click synapse_models href "./models/index_synapse.html"
click stimulating_devices href "./models/index_generator.html"
click recording_devices href "./models/index_device.html"
click simulate href "./nest_behavior/running_simulations.html"