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...	...	@@ -106,11 +106,36 @@
106	106	(% style="color:#000000" %)cell_type  = sim.IF_curr_exp(v_rest=-65, v_thresh=-55, v_reset=-65, t_refrac=1, tau_m=10, cm=1, i_offset=0.1)(%%)
107	107	(% style="color:#e74c3c" %)population1 = sim.Population(100, cell_type, label="Population 1")
108	108	population1.record("v")
109		-sim.run(100.0)
	109	+sim.run(100.0)(%%)
	110	+\\**Run script in terminal**
110	110	)))
111	111
112	112	PyNN has some built-in tools for making simple plots, so let's import those, and plot the membrane voltage of the zeroth neuron in our population (remember Python starts counting at zero).
113	113
	115	+(% class="box infomessage" %)
	116	+(((
	117	+**Screencast** - current state of editor
	118	+\\(% style="color:#000000" %)"""Simple network model using PyNN"""
	119	+\\import pyNN.nest as sim(%%)
	120	+(% style="color:#e74c3c" %)from pyNN.utility.plotting import Figure, Panel(%%)
	121	+(% style="color:#000000" %)sim.setup(timestep=0.1)(%%)
	122	+(% style="color:#000000" %)cell_type  = sim.IF_curr_exp(v_rest=-65, v_thresh=-55, v_reset=-65, t_refrac=1, tau_m=10, cm=1, i_offset=0.1)(%%)
	123	+(% style="color:#000000" %)population1 = sim.Population(100, cell_type, label="Population 1")
	124	+population1.record("v")
	125	+sim.run(100.0)(%%)
	126	+(% style="color:#e74c3c" %)data_v = population1.get_data().segments[0].filter(name='v')[0]
	127	+Figure(
	128	+ Panel(
	129	+ data_v[:, 0],
	130	+ xticks=True, xlabel="Time (ms)",
	131	+ yticks=True, ylabel="Membrane potential (mV)"
	132	+ ),
	133	+ title="Response of neuron #0",
	134	+ annotations="Simulated with NEST"
	135	+).show()(%%)
	136	+\\**Run script in terminal**
	137	+)))
	138	+
114	114	As you'd expect, the bias current causes the membrane voltage to increase until it reaches threshold~-~--it doesn't increase in a straight line because it's a //leaky// integrate-and-fire neuron~-~--then once it hits the threshold the voltage is reset, and then stays at the same level for a short time~-~--this is the refractory period~-~--before it starts to increase again.
115	115
116	116	Now, all 100 neurons in our population are identical, so if we plotted the first neuron, the second neuron, ..., we'd get the same trace.