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...	...	@@ -334,43 +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" %)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	+ (% 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" %)",
	355	+ annotations="Simulated with NEST"
	356	+).show()
349	349
	358	+**Run script in terminal, show figure**
	359	+)))
350	350
351		-(% style="color:#000000" %) title="Response of first five neurons with heterogeneous parameters",
352		- annotations="Simulated with NEST"
	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,
	392	+ yticks=True, ylabel="Membrane potential (mV)"
	393	+ ),
	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" %)"
353	353	).show()
354	354
355	355	**Run script in terminal, show figure**
356	356	)))
357	357
358		-(% class="wikigeneratedid" id="HSummary28Inthistutorial2CyouhavelearnedtodoX202629" %)
359		-(% 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.
360	360
361		-.
	409	+(% class="box successmessage" %)
	410	+(((
	411	+**Slide** recap of learning objectives
	412	+)))
362	362
363		-(% class="wikigeneratedid" id="HAcknowledgementsifappropriate" %)
364		-(% 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.
365	365
366		-.
	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.
367	367
368		-(% class="wikigeneratedid" id="HReferencestowebsites28Formoreinformation2Cvisitusat202629" %)
369		-(% 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.
370	370
371		-.
	420	+(% class="box successmessage" %)
	421	+(((
	422	+**Slide** acknowledgements, contact information
	423	+)))
372	372
373		-(% class="wikigeneratedid" id="HContactinformation28Forquestions2Ccontactusat202629" %)
374		-(% 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.
375	375
376		-.
	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.