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...	...	@@ -133,7 +133,7 @@
133	133	title="Response of neuron #0",
134	134	annotations="Simulated with NEST"
135	135	).show()(%%)
136		-\\**Run script in terminal**
	136	+\\**Run script in terminal, show figure**
137	137	)))
138	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.
...	...	@@ -140,8 +140,61 @@
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.
142	142
143		-Let's change that. In nature every neuron is a little bit different, so let's set the resting membrane potential and the spike threshold randomly from a Gaussian distribution, and let's plot membrane voltage from _all_ the  neurons.
	143	+(% class="box infomessage" %)
	144	+(((
	145	+**Screencast** - current state of editor
	146	+\\(% style="color:#000000" %)"""Simple network model using PyNN"""
	147	+\\import pyNN.nest as sim(%%)
	148	+(% style="color:#000000" %)from pyNN.utility.plotting import Figure, Panel(%%)
	149	+(% style="color:#000000" %)sim.setup(timestep=0.1)(%%)
	150	+(% 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)(%%)
	151	+(% style="color:#000000" %)population1 = sim.Population(100, cell_type, label="Population 1")
	152	+population1.record("v")
	153	+sim.run(100.0)(%%)
	154	+(% style="color:#000000" %)data_v = population1.get_data().segments[0].filter(name='v')[0]
	155	+Figure(
	156	+ Panel(
	157	+ data_v[:, (% style="color:#e74c3c" %)0:5(% style="color:#000000" %)],
	158	+ xticks=True, xlabel="Time (ms)",
	159	+ yticks=True, ylabel="Membrane potential (mV)"
	160	+ ),
	161	+ title="Response of (% style="color:#e74c3c" %)first five neurons(% style="color:#000000" %)",
	162	+ annotations="Simulated with NEST"
	163	+).show()(%%)
	164	+\\**Run script in terminal, show figure**
	165	+)))
144	144
	167	+Let's change that. In nature every neuron is a little bit different, so let's set the resting membrane potential and the spike threshold randomly from a Gaussian distribution.
	168	+
	169	+(% class="box infomessage" %)
	170	+(((
	171	+**Screencast** - current state of editor
	172	+\\(% style="color:#000000" %)"""Simple network model using PyNN"""
	173	+\\import pyNN.nest as sim(%%)
	174	+(% style="color:#000000" %)from pyNN.utility.plotting import Figure, Panel(%%)
	175	+(% style="color:#e74c3c" %)from pyNN.random import RandomDistribution(%%)
	176	+(% style="color:#000000" %)sim.setup(timestep=0.1)(%%)
	177	+(% style="color:#000000" %)cell_type  = sim.IF_curr_exp(
	178	+ (% style="color:#e74c3c" %) v_rest=RandomDistribution('normal', {'mu': -65.0, 'sigma': 1.0}),
	179	+ v_thresh=RandomDistribution('normal', {'mu': -55.0, 'sigma': 1.0}),
	180	+ v_reset=RandomDistribution('normal', {'mu': -65.0, 'sigma': 1.0}), (%%)
	181	+(% style="color:#000000" %) t_refrac=1, tau_m=10, cm=1, i_offset=0.1)(%%)
	182	+(% style="color:#000000" %)population1 = sim.Population(100, cell_type, label="Population 1")
	183	+population1.record("v")
	184	+sim.run(100.0)(%%)
	185	+(% style="color:#000000" %)data_v = population1.get_data().segments[0].filter(name='v')[0]
	186	+Figure(
	187	+ Panel(
	188	+ data_v[:, 0:5],
	189	+ xticks=True, xlabel="Time (ms)",
	190	+ yticks=True, ylabel="Membrane potential (mV)"
	191	+ ),
	192	+ title="Response of first five neurons (% style="color:#e74c3c" %)with heterogeneous parameters(% style="color:#000000" %)",
	193	+ annotations="Simulated with NEST"
	194	+).show()(%%)
	195	+\\**Run script in terminal, show figure**
	196	+)))
	197	+
145	145	Now if we run our simulation again, we can see the effect of this heterogeneity in the neuron population.
146	146
147	147	TO BE COMPLETED