# Getting Started¶

Then let’s look at how to create a neural network simulation!

NEST is a command line tool for simulating neural networks. A NEST simulation tries to follow the logic of an electrophysiological experiment - the difference being it takes place inside the computer rather than in the physical world.

You can use NEST interactively from the Python prompt, from within IPython or in a Jupyter Notebook. The latter is helpful when you are exploring PyNEST, trying to learn a new functionality or debugging a routine.

## How does it work?¶

Let’s start with a basic script to simulate a simple neural network.

• Run Python or a Jupyter Notebook and try out this example simulation in NEST:

Import required packages:

import nest
import nest.voltage_trace
nest.ResetKernel()


Create the neuron models you want to simulate:

neuron = nest.Create('iaf_psc_exp')


Create the devices to stimulate or observe the neurons in the simulation:

spikegenerator = nest.Create('spike_generator')
voltmeter = nest.Create('voltmeter')


Modify properties of the device:

nest.SetStatus(spikegenerator, {'spike_times': [10.0, 50.0]})


Connect neurons to devices and specify synapse (connection) properties:

nest.Connect(spikegenerator, neuron, syn_spec={'weight': 1e3})
nest.Connect(voltmeter, neuron)


Simulate the network for the given time in miliseconds:

nest.Simulate(100.0)


Display the voltage graph from the voltmeter:

nest.voltage_trace.from_device(voltmeter)
nest.voltage_trace.show()


You should see the following image as the output:

And that’s it! You have performed your first neuronal simulation in NEST!

## Want to know more?¶

• Check out our PyNEST tutorial, which provides full explanations on how to build your first neural network simulation in NEST.

• We have a large collection of Example networks for you to explore.

• Regularly used terms and default physical units in NEST are explained in the Glossary.