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...	...	@@ -334,21 +334,24 @@
334	334	population2.set(i_offset=0)
335	335	population1.record("v")
336	336	population2.record("v")(%%)
337		-(% style="color:#e74c3c" %)connection_algorithm = sim.FixedProbabilityConnector(p=0.5)
	337	+(% style="color:#000000" %)connection_algorithm = sim.FixedProbabilityConnector(p=0.5)
338	338	synapse_type = sim.StaticSynapse(weight=0.5, delay=0.5)
339	339	connections = sim.Projection(population1, population2, connection_algorithm, synapse_type)(%%)
340	340	(% style="color:#000000" %)sim.run(100.0)(%%)
341		-(% style="color:#000000" %)data1_v = population1.get_data().segments[0].filter(name='v')[0]
342		-data2_v = population1.get_data().segments[0].filter(name='v')[0]
343		-Figure(
	341	+(% style="color:#e74c3c" %)data1_v(% style="color:#000000" %) = population1.get_data().segments[0].filter(name='v')[0](%%)
	342	+(% style="color:#e74c3c" %)data2_v = population2.get_data().segments[0].filter(name='v')[0](%%)
	343	+(% style="color:#000000" %)Figure(
344	344	Panel(
345		- data_v[:, 0:5],
346		- xticks=True, xlabel="Time (ms)",
347		- yticks=True, ylabel="Membrane potential (mV)"
	345	+ (% style="color:#e74c3c" %)data1_v(% style="color:#000000" %)[:, 0:5],
	346	+ xticks=True, (% style="color:#e74c3c" %)--xlabel="Time (ms)",--(%%)
	347	+(% style="color:#000000" %) yticks=True, ylabel="Membrane potential (mV)"
348	348	),
349		-
350		-
351		-(% style="color:#000000" %) title="Response of first five neurons with heterogeneous parameters",
	349	+ (% style="color:#e74c3c" %)Panel(
	350	+ data2_v[:, 0:5],
	351	+ xticks=True, xlabel="Time (ms)",
	352	+ yticks=True"
	353	+ ),(%%)
	354	+(% style="color:#000000" %) title="Response of (% style="color:#e74c3c" %)simple network(% style="color:#000000" %)",
352	352	annotations="Simulated with NEST"
353	353	).show()
354	354