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358	358	**Run script in terminal, show figure**
359	359	)))
360	360
361		-(% class="wikigeneratedid" id="HSummary28Inthistutorial2CyouhavelearnedtodoX202629" %)
362		-(% class="small" %)**Summary (In this tutorial, you have learned to do X…)**
	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	363
364		-.
	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" %)"
	401	+).show()
365	365
366		-(% class="wikigeneratedid" id="HAcknowledgementsifappropriate" %)
367		-(% class="small" %)**Acknowledgements if appropriate**
	403	+**Run script in terminal, show figure**
	404	+)))
368	368
369		-.
	406	+(% class="wikigeneratedid" %)
	407	+As you would hope, NEST and NEURON give essentially identical results.
370	370
371		-(% class="wikigeneratedid" id="HReferencestowebsites28Formoreinformation2Cvisitusat202629" %)
372		-(% class="small" %)**References to websites (For more information, visit us at…)**
	409	+(% class="box successmessage" %)
	410	+(((
	411	+**Slide** recap of learning objectives
	412	+)))
373	373
374		-.
	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.
375	375
376		-(% class="wikigeneratedid" id="HContactinformation28Forquestions2Ccontactusat202629" %)
377		-(% class="small" %)**Contact information (For questions, contact us at…)**
	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.
378	378
379		-.
	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.
	419	+
	420	+(% class="box successmessage" %)
	421	+(((
	422	+**Slide** acknowledgements, contact information
	423	+)))
	424	+
	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.
	427	+
	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.