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197	197
198	198	Now if we run our simulation again, we can see the effect of this heterogeneity in the neuron population.
199	199
200		-TO BE COMPLETED
	200	+(% class="box successmessage" %)
	201	+(((
	202	+**Slide** showing addition of second population, and of connections between them
	203	+)))
201	201
	205	+(% class="wikigeneratedid" %)
	206	+So far we have a population of neurons, but there are no connections between them, we don't have a network. Let's add a second population of the same size as the first, but we'll set the offset current to zero, so they don't fire action potentials spontaneously.
	207	+
	208	+(% class="box infomessage" %)
	209	+(((
	210	+**Screencast** - current state of editor
	211	+\\(% style="color:#000000" %)"""Simple network model using PyNN"""
	212	+\\import pyNN.nest as sim(%%)
	213	+(% style="color:#000000" %)from pyNN.utility.plotting import Figure, Panel(%%)
	214	+(% style="color:#000000" %)from pyNN.random import RandomDistribution(%%)
	215	+(% style="color:#000000" %)sim.setup(timestep=0.1)(%%)
	216	+(% style="color:#000000" %)cell_type  = sim.IF_curr_exp(
	217	+ (% style="color:#e74c3c" %) (% style="color:#000000" %)v_rest=RandomDistribution('normal', {'mu': -65.0, 'sigma': 1.0}),
	218	+ v_thresh=RandomDistribution('normal', {'mu': -55.0, 'sigma': 1.0}),
	219	+ v_reset=RandomDistribution('normal', {'mu': -65.0, 'sigma': 1.0}), (%%)
	220	+(% style="color:#000000" %) t_refrac=1, tau_m=10, cm=1, i_offset=0.1)(%%)
	221	+(% style="color:#000000" %)population1 = sim.Population(100, cell_type, label="Population 1")(%%)
	222	+(% style="color:#e74c3c" %)population2 = sim.Population(100, cell_type, label="Population 2")
	223	+population2.set(i_offset=0)(%%)
	224	+(% style="color:#000000" %)population1.record("v")(%%)
	225	+(% style="color:#e74c3c" %)population2.record("v")(%%)
	226	+(% style="color:#000000" %)sim.run(100.0)(%%)
	227	+(% style="color:#000000" %)data_v = population1.get_data().segments[0].filter(name='v')[0]
	228	+Figure(
	229	+ Panel(
	230	+ data_v[:, 0:5],
	231	+ xticks=True, xlabel="Time (ms)",
	232	+ yticks=True, ylabel="Membrane potential (mV)"
	233	+ ),
	234	+ title="Response of first five neurons with heterogeneous parameters",
	235	+ annotations="Simulated with NEST"
	236	+).show()(%%)
	237	+\\**Run script in terminal, show figure**
	238	+)))
	239	+
	240	+Now we want to create synaptic connections between the neurons in Population 1 and those in Population 2. There are lots of different ways these could be connected.
	241	+
	242	+(% class="box successmessage" %)
	243	+(((
	244	+**Slide** showing all-to-all connections
	245	+)))
	246	+
	247	+We could connect all neurons in Population 1 to all those in Population 2.
	248	+
	249	+(% class="box successmessage" %)
	250	+(((
	251	+**Slide** showing random connections
	252	+)))
	253	+
	254	+We could connect the populations randomly, in several different ways.
	255	+
	256	+(% class="box successmessage" %)
	257	+(((
	258	+**Slide** showing distance-dependent connections
	259	+)))
	260	+
	261	+(% class="wikigeneratedid" %)
	262	+We could connect the populations randomly, but with a probability of connection that depends on the distance between the neurons.
	263	+
	264	+(% class="box successmessage" %)
	265	+(((
	266	+**Slide** showing explicit lists of connections
	267	+)))
	268	+
	269	+(% class="wikigeneratedid" %)
	270	+Or we could connect the neurons in a very specific manner, based on an explicit list of connections.
	271	+
	272	+(% class="wikigeneratedid" %)
	273	+Just as PyNN provides a variety of neuron models, so it comes with a range of connection algorithms built in. You can also add your own connection methods.
	274	+
	275	+(% class="box successmessage" %)
	276	+(((
	277	+**Slide** showing addition of second population, and of connections between them, labelled as a Projection.
	278	+)))
	279	+
	280	+(% class="wikigeneratedid" %)
	281	+In PyNN, we call a group of connections between two populations a _Projection_. To create a Projection, we need to specify the presynaptic population, the postsynaptic population, the connection algorithm, and the synapse model. Here we're using the simplest synapse model available in PyNN, for which the synaptic weight is constant over time, there is no plasticity.
	282	+
	283	+(% class="box infomessage" %)
	284	+(((
	285	+**Screencast** - current state of editor
	286	+\\(% style="color:#000000" %)"""Simple network model using PyNN"""
	287	+\\import pyNN.nest as sim(%%)
	288	+(% style="color:#000000" %)from pyNN.utility.plotting import Figure, Panel(%%)
	289	+(% style="color:#000000" %)from pyNN.random import RandomDistribution(%%)
	290	+(% style="color:#000000" %)sim.setup(timestep=0.1)(%%)
	291	+(% style="color:#000000" %)cell_type  = sim.IF_curr_exp(
	292	+ (% style="color:#e74c3c" %) (% style="color:#000000" %)v_rest=RandomDistribution('normal', {'mu': -65.0, 'sigma': 1.0}),
	293	+ v_thresh=RandomDistribution('normal', {'mu': -55.0, 'sigma': 1.0}),
	294	+ v_reset=RandomDistribution('normal', {'mu': -65.0, 'sigma': 1.0}), (%%)
	295	+(% style="color:#000000" %) t_refrac=1, tau_m=10, cm=1, i_offset=0.1)(%%)
	296	+(% style="color:#000000" %)population1 = sim.Population(100, cell_type, label="Population 1")(%%)
	297	+(% style="color:#000000" %)population2 = sim.Population(100, cell_type, label="Population 2")
	298	+population2.set(i_offset=0)
	299	+population1.record("v")
	300	+population2.record("v")(%%)
	301	+(% style="color:#e74c3c" %)connection_algorithm = sim.FixedProbabilityConnector(p=0.5)
	302	+synapse_type = sim.StaticSynapse(weight=0.5, delay=0.5)
	303	+connections = sim.Projection(population1, population2, connection_algorithm, synapse_type)(%%)
	304	+(% style="color:#000000" %)sim.run(100.0)(%%)
	305	+(% style="color:#000000" %)data_v = population1.get_data().segments[0].filter(name='v')[0]
	306	+Figure(
	307	+ Panel(
	308	+ data_v[:, 0:5],
	309	+ xticks=True, xlabel="Time (ms)",
	310	+ yticks=True, ylabel="Membrane potential (mV)"
	311	+ ),
	312	+ title="Response of first five neurons with heterogeneous parameters",
	313	+ annotations="Simulated with NEST"
	314	+).show()(%%)
	315	+\\**Run script in terminal, show figure**
	316	+)))
	317	+
202	202	(% class="wikigeneratedid" id="HSummary28Inthistutorial2CyouhavelearnedtodoX202629" %)
203	203	(% class="small" %)**Summary (In this tutorial, you have learned to do X…)**
204	204