PyNEST - First Steps

Modeling networks of spiking neurons using NEST

Python Module of the Week, 03.05.2019

This notebook guides through your first steps using NEST. It shows

how to get help
how to create and simulate a single neuron
how to visualize the output

Essentially, this is a reproduction of the 'Hello World!' notebook with added explanations.

For more details see part 1 of the official PyNEST tutorial.



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# populate namespace with pylab functions and stuff
%pylab inline



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import nest # import NEST module

Getting help



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# information about functions with Python's help() ...
help(nest.Models)



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# ... or IPython's question mark
nest.Models?



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# list neuron models
nest.Models()



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# choose LIF neuron with exponential synaptic currents: 'iaf_psc_exp'
# look in documentation for model description
# or (if not compiled with MPI)
nest.help('iaf_psc_exp')

Creating a neuron



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# before creating a new network,
# reset the simulation kernel / remove all nodes
nest.ResetKernel()



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# create the neuron
neuron = nest.Create('iaf_psc_exp')



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# investigate the neuron

# Create() just returns a list (tuple) with handles to the new nodes
# (handles = integer numbers called ids)
neuron



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# current dynamical state/parameters of the neuron
# note that the membrane voltage is at -70 mV
nest.GetStatus(neuron)

Creating a spikegenerator



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# create a spike generator
spikegenerator = nest.Create('spike_generator')



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# check out 'spike_times' in its parameters
nest.GetStatus(spikegenerator)



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# set the spike times at 10 and 50 ms
nest.SetStatus(spikegenerator, {'spike_times': [10., 50.]})

Creating a voltmeter



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# create a voltmeter for recording
voltmeter = nest.Create('voltmeter')



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# investigate the voltmeter
voltmeter



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# see that it records membrane voltage, senders, times
nest.GetStatus(voltmeter)

Connecting



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# investigate Connect() function
nest.Connect?



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# connect spike generator and voltmeter to the neuron
nest.Connect(spikegenerator, neuron, syn_spec={'weight': 1e3})



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nest.Connect(voltmeter, neuron)

Simulating



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# run simulation for 100 ms
nest.Simulate(100.)



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# look at nest's KernelStatus:
# network_size (root node, neuron, spike generator, voltmeter)
# num_connections
# time (simulation duration)
nest.GetKernelStatus()



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# note that voltmeter has recorded 99 events
nest.GetStatus(voltmeter)



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# read out recording time and voltage from voltmeter
times = nest.GetStatus(voltmeter)[0]['events']['times']
voltages = nest.GetStatus(voltmeter)[0]['events']['V_m']

Plotting



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# plot results
# units can be found in documentation
pylab.plot(times, voltages, label='Neuron 1')
pylab.xlabel('Time (ms)')
pylab.ylabel('Membrane potential (mV)')
pylab.title('Membrane potential')
pylab.legend()



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# create the same plot with NEST's build-in plotting function
import nest.voltage_trace



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nest.voltage_trace.from_device(voltmeter)

Bored?

Try to make the neuron spike (maybe use 0_hello_world.ipynb)
Connect another neuron to the first neuron recieving that spike
Check out the official PyNEST tutorials, in particular
- part 1: Neurons and simple neural networks
- part 2: Populations of neurons