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...	...	@@ -84,7 +84,7 @@
84	84
85	85	PyNN comes with a selection of integrate-and-fire models. We're going to use the IF_curr_exp model, where "IF" is for integrate-and-fire, "curr" means that synaptic responses are changes in current, and "exp" means that the shape of the current is a decaying exponential function.
86	86
87		-This is where we set the parameters of the model: the resting membrane potential is -65 millivolts, the spike threshold is -55 millivolts, the reset voltage after a spike is again -65 millivolts, the refractory period after a spike is one millisecond, the membrane time constant is 10 milliseconds, and the membrane capacitance is 1 nanofarad. We're also going to inject a constant bias current of 1.1 nanoamps into these neurons, so that we get some action potentials.
	87	+This is where we set the parameters of the model: the resting membrane potential is -65 millivolts, the spike threshold is -55 millivolts, the reset voltage after a spike is again -65 millivolts, the refractory period after a spike is one millisecond, the membrane time constant is 10 milliseconds, and the membrane capacitance is 1 nanofarad. We're also going to inject a constant bias current of 0.1 nanoamps into these neurons, so that we get some action potentials.
88	88
89	89	(% class="box infomessage" %)
90	90	(((
...	...	@@ -166,14 +166,14 @@
166	166	\\(% style="color:#000000" %)"""Simple network model using PyNN"""
167	167	\\import pyNN.nest as sim(%%)
168	168	(% style="color:#000000" %)from pyNN.utility.plotting import Figure, Panel(%%)
169		-(% style="color:#e74c3c" %)from pyNN.random import RandomDistribution, NumpyRNG(%%)
	169	+(% style="color:#e74c3c" %)from pyNN.random import RandomDistribution(%%)
170	170	(% style="color:#000000" %)sim.setup(timestep=0.1)(%%)
171		-(% style="color:#e74c3c" %)rng = NumpyRNG(seed=1)(%%)
172	172	(% style="color:#000000" %)cell_type  = sim.IF_curr_exp(
173		- (% style="color:#e74c3c" %) v_rest=RandomDistribution('normal', mu=-65.0, sigma=1.0, rng=rng),
174		- v_thresh=RandomDistribution('normal', mu=-55.0, sigma=1.0, rng=rng),
175		- v_reset=RandomDistribution('normal', mu=-65.0, sigma=1.0, rng=rng), (%%)
176		-(% style="color:#000000" %) tau_refrac=1, tau_m=10, cm=1, i_offset=1.1)
	172	+ (% style="color:#e74c3c" %) v_rest=RandomDistribution('normal', mu=-65.0, sigma=1.0),
	173	+ v_thresh=RandomDistribution('normal', mu=-55.0, sigma=1.0),
	174	+ v_reset=RandomDistribution('normal', mu=-65.0, sigma=1.0), (%%)
	175	+(% style="color:#000000" %) tau_refrac=1, tau_m=10, cm=1, i_offset=1.1)(%%)
	176	+
177	177
178	178	**...**
179	179
...	...	@@ -263,7 +263,7 @@
263	263
264	264	**...**
265	265	(% style="color:#000000" %)population2.record("v")(%%)
266		-(% style="color:#e74c3c" %)connection_algorithm = sim.FixedProbabilityConnector(p_connect=0.5, rng=rng)
	266	+(% style="color:#e74c3c" %)connection_algorithm = sim.FixedProbabilityConnector(p_connect=0.5)
267	267	synapse_type = sim.StaticSynapse(weight=0.5, delay=0.5)
268	268	connections = sim.Projection(population1, population2, connection_algorithm, synapse_type)(%%)
269	269	(% style="color:#000000" %)sim.run(100.0)(%%)
...	...	@@ -307,20 +307,19 @@
307	307	\\(% style="color:#000000" %)"""Simple network model using PyNN"""
308	308	\\import pyNN.(% style="color:#e74c3c" %)neuron(% style="color:#000000" %) as sim(%%)
309	309	(% style="color:#000000" %)from pyNN.utility.plotting import Figure, Panel(%%)
310		-(% style="color:#000000" %)from pyNN.random import RandomDistribution, NumpyRNG(%%)
	310	+(% style="color:#000000" %)from pyNN.random import RandomDistribution(%%)
311	311	(% style="color:#000000" %)sim.setup(timestep=0.1)(%%)
312		-(% style="color:#000000" %)rng = NumpyRNG(seed=1)(%%)
313	313	(% style="color:#000000" %)cell_type  = sim.IF_curr_exp(
314		- v_rest=RandomDistribution('normal', mu=-65.0, sigma=1.0, rng=rng),
315		- v_thresh=RandomDistribution('normal', mu=-55.0, sigma=1.0, rng=rng),
316		- v_reset=RandomDistribution('normal', mu=-65.0, sigma=1.0, rng=rng),
317		- tau_refrac=1, tau_m=10, cm=1, i_offset=1.1)
318		-population1 = sim.Population(100, cell_type, label="Population 1")
319		-population2 = sim.Population(100, cell_type, label="Population 2")
	313	+ (% style="color:#e74c3c" %) (% style="color:#000000" %)v_rest=RandomDistribution('normal', mu=-65.0, sigma=1.0),
	314	+ v_thresh=RandomDistribution('normal', mu=-55.0, sigma=1.0),
	315	+ v_reset=RandomDistribution('normal', mu=-65.0, sigma=1.0), (%%)
	316	+(% style="color:#000000" %) tau_refrac=1, tau_m=10, cm=1, i_offset=1.1)(%%)
	317	+(% style="color:#000000" %)population1 = sim.Population(100, cell_type, label="Population 1")(%%)
	318	+(% style="color:#000000" %)population2 = sim.Population(100, cell_type, label="Population 2")
320	320	population2.set(i_offset=0)
321	321	population1.record("v")
322		-population2.record("v")
323		-connection_algorithm = sim.FixedProbabilityConnector(p_connect=0.5, rng=rng)
	321	+population2.record("v")(%%)
	322	+(% style="color:#000000" %)connection_algorithm = sim.FixedProbabilityConnector(p_connect=0.5)
324	324	synapse_type = sim.StaticSynapse(weight=0.5, delay=0.5)
325	325	connections = sim.Projection(population1, population2, connection_algorithm, synapse_type)(%%)
326	326	(% style="color:#000000" %)sim.run(100.0)(%%)
...	...	@@ -356,7 +356,7 @@
356	356
357	357	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.
358	358
359		-We will be releasing a series of tutorials, throughout this year, to introduce these more advanced features of PyNN, so keep an eye on the EBRAINS website.
	358	+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.
360	360
361	361	(% class="box successmessage" %)
362	362	(((