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...	...	@@ -106,17 +106,95 @@
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, show figure**
	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.
117	117
	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	+)))
	166	+
118	118	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.
119	119
	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	+
120	120	Now if we run our simulation again, we can see the effect of this heterogeneity in the neuron population.
121	121
122	122	TO BE COMPLETED