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...	...	@@ -106,36 +106,11 @@
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)(%%)
110		-\\**Run script in terminal**
	109	+sim.run(100.0)
111	111	)))
112	112
113	113	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).
114	114
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		-
139	139	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.
140	140
141	141	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.