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...	...	@@ -334,40 +334,96 @@
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" %)data_v = population1.get_data().segments[0].filter(name='v')[0]
342		-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(
343	343	Panel(
344		- data_v[:, 0:5],
	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	+ ),
	349	+ (% style="color:#e74c3c" %)Panel(
	350	+ data2_v[:, 0:5],
345	345	xticks=True, xlabel="Time (ms)",
	352	+ yticks=True"
	353	+ ),(%%)
	354	+(% style="color:#000000" %) title="Response of (% style="color:#e74c3c" %)simple network(% style="color:#000000" %)",
	355	+ annotations="Simulated with NEST"
	356	+).show()
	357	+
	358	+**Run script in terminal, show figure**
	359	+)))
	360	+
	361	+(% class="wikigeneratedid" %)
	362	+and there we have it, our simple neuronal network of integrate-and-fire neurons, written in PyNN, simulated with NEST. If you prefer to use the NEURON simulator, PyNN makes this very simple, we import the PyNN-for-NEURON module instead.
	363	+
	364	+(% class="box infomessage" %)
	365	+(((
	366	+**Screencast** - current state of editor
	367	+\\(% style="color:#000000" %)"""Simple network model using PyNN"""
	368	+\\import pyNN.(% style="color:#e74c3c" %)neuron(% style="color:#000000" %) as sim(%%)
	369	+(% style="color:#000000" %)from pyNN.utility.plotting import Figure, Panel(%%)
	370	+(% style="color:#000000" %)from pyNN.random import RandomDistribution(%%)
	371	+(% style="color:#000000" %)sim.setup(timestep=0.1)(%%)
	372	+(% style="color:#000000" %)cell_type  = sim.IF_curr_exp(
	373	+ (% style="color:#e74c3c" %) (% style="color:#000000" %)v_rest=RandomDistribution('normal', {'mu': -65.0, 'sigma': 1.0}),
	374	+ v_thresh=RandomDistribution('normal', {'mu': -55.0, 'sigma': 1.0}),
	375	+ v_reset=RandomDistribution('normal', {'mu': -65.0, 'sigma': 1.0}), (%%)
	376	+(% style="color:#000000" %) t_refrac=1, tau_m=10, cm=1, i_offset=0.1)(%%)
	377	+(% style="color:#000000" %)population1 = sim.Population(100, cell_type, label="Population 1")(%%)
	378	+(% style="color:#000000" %)population2 = sim.Population(100, cell_type, label="Population 2")
	379	+population2.set(i_offset=0)
	380	+population1.record("v")
	381	+population2.record("v")(%%)
	382	+(% style="color:#000000" %)connection_algorithm = sim.FixedProbabilityConnector(p=0.5)
	383	+synapse_type = sim.StaticSynapse(weight=0.5, delay=0.5)
	384	+connections = sim.Projection(population1, population2, connection_algorithm, synapse_type)(%%)
	385	+(% style="color:#000000" %)sim.run(100.0)(%%)
	386	+(% style="color:#000000" %)data1_v = population1.get_data().segments[0].filter(name='v')[0]
	387	+data2_v = population2.get_data().segments[0].filter(name='v')[0]
	388	+Figure(
	389	+ Panel(
	390	+ data1_v[:, 0:5],
	391	+ xticks=True,
346	346	yticks=True, ylabel="Membrane potential (mV)"
347	347	),
348		- title="Response of first five neurons with heterogeneous parameters",
349		- annotations="Simulated with NEST"
	394	+ Panel(
	395	+ data2_v[:, 0:5],
	396	+ xticks=True, xlabel="Time (ms)",
	397	+ yticks=True"
	398	+ ),(%%)
	399	+(% style="color:#000000" %) title="Response of simple network",
	400	+ annotations="Simulated with (% style="color:#e74c3c" %)NEURON(% style="color:#000000" %)"
350	350	).show()
351	351
352		-Run script in terminal, show figure
	403	+**Run script in terminal, show figure**
353	353	)))
354	354
355		-(% class="wikigeneratedid" id="HSummary28Inthistutorial2CyouhavelearnedtodoX202629" %)
356		-(% class="small" %)**Summary (In this tutorial, you have learned to do X…)**
	406	+(% class="wikigeneratedid" %)
	407	+As you would hope, NEST and NEURON give essentially identical results.
357	357
358		-.
	409	+(% class="box successmessage" %)
	410	+(((
	411	+**Slide** recap of learning objectives
	412	+)))
359	359
360		-(% class="wikigeneratedid" id="HAcknowledgementsifappropriate" %)
361		-(% class="small" %)**Acknowledgements if appropriate**
	414	+That is the end of this tutorial, in which I've demonstrated how to build a simple network using PyNN, and to simulate it using two different simulators, NEST and NEURON.
362	362
363		-.
	416	+Of course, PyNN allows you to create much more complex networks than this, with more realistic neuron models, synaptic plasticity, spatial structure, and so on. You can also use other simulators, such as Brian or SpiNNaker, and you can run simulations in parallel on clusters or supercomputers.
364	364
365		-(% class="wikigeneratedid" id="HReferencestowebsites28Formoreinformation2Cvisitusat202629" %)
366		-(% class="small" %)**References to websites (For more information, visit us at…)**
	418	+We will be releasing a series of tutorials, throughout the rest of 2021 and 2022, to introduce these more advanced features of PyNN, so keep an eye on the EBRAINS website.
367	367
368		-.
	420	+(% class="box successmessage" %)
	421	+(((
	422	+**Slide** acknowledgements, contact information
	423	+)))
369	369
370		-(% class="wikigeneratedid" id="HContactinformation28Forquestions2Ccontactusat202629" %)
371		-(% class="small" %)**Contact information (For questions, contact us at…)**
	425	+(% class="wikigeneratedid" %)
	426	+PyNN has been developed by many different people, with financial support from several different organisations. I'd like to mention in particular the CNRS and the European Commission, through the FACETS, BrainScaleS and Human Brain Project grants.
372	372
373		-.
	428	+(% class="wikigeneratedid" %)
	429	+For more information visit neuralensemble.org/PyNN. If you have questions you can contact us through the PyNN Github project, the NeuralEnsemble forum, EBRAINS support, or the EBRAINS Community.