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358	358	**Run script in terminal, show figure**
359	359	)))
360	360
	361	+(% class="wikigeneratedid" %)
	362	+and there we have it, our simple neuronal network of integrate-and-fire neurons, written in PyNN, simulated with NEST. If you prefer to use the NEURON simulator, PyNN makes this very simple, we import the PyNN-for-NEURON module instead.
	363	+
	364	+(% class="box infomessage" %)
	365	+(((
	366	+**Screencast** - current state of editor
	367	+\\(% style="color:#000000" %)"""Simple network model using PyNN"""
	368	+\\import pyNN.(% style="color:#e74c3c" %)neuron(% style="color:#000000" %) as sim(%%)
	369	+(% style="color:#000000" %)from pyNN.utility.plotting import Figure, Panel(%%)
	370	+(% style="color:#000000" %)from pyNN.random import RandomDistribution(%%)
	371	+(% style="color:#000000" %)sim.setup(timestep=0.1)(%%)
	372	+(% style="color:#000000" %)cell_type  = sim.IF_curr_exp(
	373	+ (% style="color:#e74c3c" %) (% style="color:#000000" %)v_rest=RandomDistribution('normal', {'mu': -65.0, 'sigma': 1.0}),
	374	+ v_thresh=RandomDistribution('normal', {'mu': -55.0, 'sigma': 1.0}),
	375	+ v_reset=RandomDistribution('normal', {'mu': -65.0, 'sigma': 1.0}), (%%)
	376	+(% style="color:#000000" %) t_refrac=1, tau_m=10, cm=1, i_offset=0.1)(%%)
	377	+(% style="color:#000000" %)population1 = sim.Population(100, cell_type, label="Population 1")(%%)
	378	+(% style="color:#000000" %)population2 = sim.Population(100, cell_type, label="Population 2")
	379	+population2.set(i_offset=0)
	380	+population1.record("v")
	381	+population2.record("v")(%%)
	382	+(% style="color:#000000" %)connection_algorithm = sim.FixedProbabilityConnector(p=0.5)
	383	+synapse_type = sim.StaticSynapse(weight=0.5, delay=0.5)
	384	+connections = sim.Projection(population1, population2, connection_algorithm, synapse_type)(%%)
	385	+(% style="color:#000000" %)sim.run(100.0)(%%)
	386	+(% style="color:#000000" %)data1_v = population1.get_data().segments[0].filter(name='v')[0]
	387	+data2_v = population2.get_data().segments[0].filter(name='v')[0]
	388	+Figure(
	389	+ Panel(
	390	+ data1_v[:, 0:5],
	391	+ xticks=True,
	392	+ yticks=True, ylabel="Membrane potential (mV)"
	393	+ ),
	394	+ Panel(
	395	+ data2_v[:, 0:5],
	396	+ xticks=True, xlabel="Time (ms)",
	397	+ yticks=True"
	398	+ ),(%%)
	399	+(% style="color:#000000" %) title="Response of simple network",
	400	+ annotations="Simulated with (% style="color:#e74c3c" %)NEURON(% style="color:#000000" %)"
	401	+).show()
	402	+
	403	+**Run script in terminal, show figure**
	404	+)))
	405	+
	406	+(% class="wikigeneratedid" %)
	407	+As you would hope, NEST and NEURON give essentially identical results.
	408	+
361	361	(% class="wikigeneratedid" id="HSummary28Inthistutorial2CyouhavelearnedtodoX202629" %)
362	362	(% class="small" %)**Summary (In this tutorial, you have learned to do X…)**
363	363