Have you already installed NEST <install_nest>
?
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.
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
import matplotlib.pyplot as plt
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:
spikegenerator.set(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)
plt.show()
You should see the following image as the output:
And that's it! You have performed your first neuronal simulation in NEST!
Take a look at the :py.set_verbosity
documentation, which describes how to display fewer messages on the terminal.
- Check out our
PyNEST tutorial <tutorials>
, which provides full explanations on how to build your first neural network simulation in NEST. - We have a large collection of
Example networks <pynest_examples>
for you to explore. - Regularly used terms and default physical units in NEST are explained in the
Glossary <glossary>
.