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1		-XWiki.annedevismes
	1	+XWiki.adavison

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9	9
10	10	== Audience ==
11	11
12		-This tutorial is intended for people with at least a basic knowledge of neuroscience (high-school level or above) and basic familiarity with the Python programming language. It should also be helpful for people who already have advanced knowledge of neuroscience and neural simulation, who simply wish to learn how to use PyNN and how it differs from other simulation tools they know.
	12	+This tutorial is intended for people with at least a basic knowledge of neuroscience (high school level or above) and basic familiarity with the Python programming language. It should also be helpful for people who already have advanced knowledge of neuroscience and neural simulation, who simply wish to learn how to use PyNN, and how it differs from other simulation tools they know.
13	13
14	14	== Prerequisites ==
15	15
16		-To follow this tutorial, you need a basic knowledge of neuroscience (high-school level or greater), basic familiarity with the Python programming language, and either a computer with PyNN, NEST, NEURON, and Brian 2 installed or an EBRAINS account and basic familiarity with Jupyter notebooks. If you don't have these tools installed, see one of our previous tutorials which guide you through the installation.
	16	+To follow this tutorial, you need a basic knowledge of neuroscience (high-school level or greater), basic familiarity with the Python programming language, and either a computer with PyNN, NEST, NEURON and Brian 2 installed, or an EBRAINS account and basic familiarity with Jupyter notebooks. If you don't have these tools installed, see one of our previous tutorials which guide you through the installation.
17	17
18	18	== Format ==
19	19
...	...	@@ -66,13 +66,13 @@
66	66	**Screencast** - blank document in editor
67	67	)))
68	68
69		-In this video, you'll see my editor on the left and my terminal and my file browser on the right. I'll be writing code in the editor and then running my scripts in the terminal. You're welcome to follow along~-~--you can pause the video at any time if I'm going too fast~-~--or you can just watch.
	69	+In this video, you'll see my editor on the left, and on the right my terminal and my file browser. I'll be writing code in the editor, and then running my scripts in the terminal. You're welcome to follow along~-~--you can pause the video at any time if I'm going too fast~-~--or you can just watch.
70	70
71		-Let's start by writing a docstring "Simple network model using PyNN".
	71	+Let's start by writing a docstring, "Simple network model using PyNN".
72	72
73		-For now, we're going to use the NEST simulator to simulate this model; so, we import the PyNN-for-NEST module.
	73	+For now, we're going to use the NEST simulator to simulate this model, so we import the PyNN-for-NEST module.
74	74
75		-Like with any numerical model, we need to break time down into small steps; so let's set that up with steps of 0.1 milliseconds.
	75	+Like with any numerical model, we need to break time down into small steps, so let's set that up with steps of 0.1 milliseconds.
76	76
77	77	(% class="box infomessage" %)
78	78	(((
...	...	@@ -95,7 +95,7 @@
95	95	(% style="color:#e74c3c" %)cell_type  = sim.IF_curr_exp(v_rest=-65, v_thresh=-55, v_reset=-65, t_refrac=1, tau_m=10, cm=1, i_offset=0.1)
96	96	)))
97	97
98		-Let's create 100 of these neurons; then, we're going to record the membrane voltage and run a simulation for 100 milliseconds.
	98	+Let's create 100 of these neurons, then we're going to record the membrane voltage, and run a simulation for 100 milliseconds.
99	99
100	100	(% class="box infomessage" %)
101	101	(((
...	...	@@ -136,17 +136,23 @@
136	136	\\**Run script in terminal, show figure**
137	137	)))
138	138
139		-As you'd expect, the bias current causes the membrane voltage to increase until it reaches threshold~-~--it doesn't increase in a straight line because it's a //leaky// integrate-and-fire neuron~-~--then, once it hits the threshold, the voltage is reset and then stays at the same level for a short time~-~--this is the refractory period~-~--before it starts to increase again.
	139	+As you'd expect, the bias current causes the membrane voltage to increase until it reaches threshold~-~--it doesn't increase in a straight line because it's a //leaky// integrate-and-fire neuron~-~--then once it hits the threshold the voltage is reset, and then stays at the same level for a short time~-~--this is the refractory period~-~--before it starts to increase again.
140	140
141		-Now, all 100 neurons in our population are identical; so, if we plotted the first neuron, the second neuron, ..., we'd get the same trace.
	141	+Now, all 100 neurons in our population are identical, so if we plotted the first neuron, the second neuron, ..., we'd get the same trace.
142	142
143	143	(% class="box infomessage" %)
144	144	(((
145		-**Screencast** - changes in editor
146		-
147		-
148		-**...**
149		-(% style="color:#000000" %)Figure(
	145	+**Screencast** - current state of editor
	146	+\\(% style="color:#000000" %)"""Simple network model using PyNN"""
	147	+\\import pyNN.nest as sim(%%)
	148	+(% style="color:#000000" %)from pyNN.utility.plotting import Figure, Panel(%%)
	149	+(% style="color:#000000" %)sim.setup(timestep=0.1)(%%)
	150	+(% style="color:#000000" %)cell_type  = sim.IF_curr_exp(v_rest=-65, v_thresh=-55, v_reset=-65, t_refrac=1, tau_m=10, cm=1, i_offset=0.1)(%%)
	151	+(% style="color:#000000" %)population1 = sim.Population(100, cell_type, label="Population 1")
	152	+population1.record("v")
	153	+sim.run(100.0)(%%)
	154	+(% style="color:#000000" %)data_v = population1.get_data().segments[0].filter(name='v')[0]
	155	+Figure(
150	150	Panel(
151	151	data_v[:, (% style="color:#e74c3c" %)0:5(% style="color:#000000" %)],
152	152	xticks=True, xlabel="Time (ms)",
...	...	@@ -158,11 +158,11 @@
158	158	\\**Run script in terminal, show figure**
159	159	)))
160	160
161		-Let's change that. In nature, every neuron is a little bit different; so, let's set the resting membrane potential and the spike threshold randomly from a Gaussian distribution.
	167	+Let's change that. In nature every neuron is a little bit different, so let's set the resting membrane potential and the spike threshold randomly from a Gaussian distribution.
162	162
163	163	(% class="box infomessage" %)
164	164	(((
165		-**Screencast** - changes in editor
	171	+**Screencast** - current state of editor
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(%%)
...	...	@@ -173,12 +173,11 @@
173	173	v_thresh=RandomDistribution('normal', {'mu': -55.0, 'sigma': 1.0}),
174	174	v_reset=RandomDistribution('normal', {'mu': -65.0, 'sigma': 1.0}), (%%)
175	175	(% style="color:#000000" %) t_refrac=1, tau_m=10, cm=1, i_offset=0.1)(%%)
176		-
177		-
178		-**...**
179		-
180		-
181		-(% style="color:#000000" %)Figure(
	182	+(% style="color:#000000" %)population1 = sim.Population(100, cell_type, label="Population 1")
	183	+population1.record("v")
	184	+sim.run(100.0)(%%)
	185	+(% style="color:#000000" %)data_v = population1.get_data().segments[0].filter(name='v')[0]
	186	+Figure(
182	182	Panel(
183	183	data_v[:, 0:5],
184	184	xticks=True, xlabel="Time (ms)",
...	...	@@ -190,20 +190,29 @@
190	190	\\**Run script in terminal, show figure**
191	191	)))
192	192
193		-Now, if we run our simulation again, we can see the effect of this heterogeneity in the neuron population.
	198	+Now if we run our simulation again, we can see the effect of this heterogeneity in the neuron population.
194	194
195	195	(% class="box successmessage" %)
196	196	(((
197		-**Slide** showing addition of second population and of connections between them
	202	+**Slide** showing addition of second population, and of connections between them
198	198	)))
199	199
200	200	(% class="wikigeneratedid" %)
201		-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.
	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.
202	202
203	203	(% class="box infomessage" %)
204	204	(((
205		-**Screencast** - changes in editor
206		-\\**...**
	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)(%%)
207	207	(% style="color:#000000" %)population1 = sim.Population(100, cell_type, label="Population 1")(%%)
208	208	(% style="color:#e74c3c" %)population2 = sim.Population(100, cell_type, label="Population 2")
209	209	population2.set(i_offset=0)(%%)
...	...	@@ -210,10 +210,19 @@
210	210	(% style="color:#000000" %)population1.record("v")(%%)
211	211	(% style="color:#e74c3c" %)population2.record("v")(%%)
212	212	(% style="color:#000000" %)sim.run(100.0)(%%)
213		-**...**
	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()
214	214	)))
215	215
216		-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.
	239	+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.
217	217
218	218	(% class="box successmessage" %)
219	219	(((
...	...	@@ -254,20 +254,40 @@
254	254	)))
255	255
256	256	(% class="wikigeneratedid" %)
257		-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.
	280	+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.
258	258
259	259	(% class="box infomessage" %)
260	260	(((
261		-**Screencast** - changes in editor
262		-
263		-
264		-**...**
265		-(% style="color:#000000" %)population2.record("v")(%%)
	284	+**Screencast** - current state of editor
	285	+\\(% style="color:#000000" %)"""Simple network model using PyNN"""
	286	+\\import pyNN.nest as sim(%%)
	287	+(% style="color:#000000" %)from pyNN.utility.plotting import Figure, Panel(%%)
	288	+(% style="color:#000000" %)from pyNN.random import RandomDistribution(%%)
	289	+(% style="color:#000000" %)sim.setup(timestep=0.1)(%%)
	290	+(% style="color:#000000" %)cell_type  = sim.IF_curr_exp(
	291	+ (% style="color:#e74c3c" %) (% style="color:#000000" %)v_rest=RandomDistribution('normal', {'mu': -65.0, 'sigma': 1.0}),
	292	+ v_thresh=RandomDistribution('normal', {'mu': -55.0, 'sigma': 1.0}),
	293	+ v_reset=RandomDistribution('normal', {'mu': -65.0, 'sigma': 1.0}), (%%)
	294	+(% style="color:#000000" %) t_refrac=1, tau_m=10, cm=1, i_offset=0.1)(%%)
	295	+(% style="color:#000000" %)population1 = sim.Population(100, cell_type, label="Population 1")(%%)
	296	+(% style="color:#000000" %)population2 = sim.Population(100, cell_type, label="Population 2")
	297	+population2.set(i_offset=0)
	298	+population1.record("v")
	299	+population2.record("v")(%%)
266	266	(% style="color:#e74c3c" %)connection_algorithm = sim.FixedProbabilityConnector(p=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)(%%)
270		-**...**
	304	+(% style="color:#000000" %)data_v = population1.get_data().segments[0].filter(name='v')[0]
	305	+Figure(
	306	+ Panel(
	307	+ data_v[:, 0:5],
	308	+ xticks=True, xlabel="Time (ms)",
	309	+ yticks=True, ylabel="Membrane potential (mV)"
	310	+ ),
	311	+ title="Response of first five neurons with heterogeneous parameters",
	312	+ annotations="Simulated with NEST"
	313	+).show()
271	271	)))
272	272
273	273	(% class="wikigeneratedid" %)
...	...	@@ -275,8 +275,25 @@
275	275
276	276	(% class="box infomessage" %)
277	277	(((
278		-**Screencast** - changes in editor
279		-\\**...**
	321	+**Screencast** - current state of editor
	322	+\\(% style="color:#000000" %)"""Simple network model using PyNN"""
	323	+\\import pyNN.nest as sim(%%)
	324	+(% style="color:#000000" %)from pyNN.utility.plotting import Figure, Panel(%%)
	325	+(% style="color:#000000" %)from pyNN.random import RandomDistribution(%%)
	326	+(% style="color:#000000" %)sim.setup(timestep=0.1)(%%)
	327	+(% style="color:#000000" %)cell_type  = sim.IF_curr_exp(
	328	+ (% style="color:#e74c3c" %) (% style="color:#000000" %)v_rest=RandomDistribution('normal', {'mu': -65.0, 'sigma': 1.0}),
	329	+ v_thresh=RandomDistribution('normal', {'mu': -55.0, 'sigma': 1.0}),
	330	+ v_reset=RandomDistribution('normal', {'mu': -65.0, 'sigma': 1.0}), (%%)
	331	+(% style="color:#000000" %) t_refrac=1, tau_m=10, cm=1, i_offset=0.1)(%%)
	332	+(% style="color:#000000" %)population1 = sim.Population(100, cell_type, label="Population 1")(%%)
	333	+(% style="color:#000000" %)population2 = sim.Population(100, cell_type, label="Population 2")
	334	+population2.set(i_offset=0)
	335	+population1.record("v")
	336	+population2.record("v")(%%)
	337	+(% style="color:#000000" %)connection_algorithm = sim.FixedProbabilityConnector(p=0.5)
	338	+synapse_type = sim.StaticSynapse(weight=0.5, delay=0.5)
	339	+connections = sim.Projection(population1, population2, connection_algorithm, synapse_type)(%%)
280	280	(% style="color:#000000" %)sim.run(100.0)(%%)
281	281	(% style="color:#e74c3c" %)data1_v(% style="color:#000000" %) = population1.get_data().segments[0].filter(name='v')[0](%%)
282	282	(% style="color:#e74c3c" %)data2_v = population2.get_data().segments[0].filter(name='v')[0](%%)
...	...	@@ -299,11 +299,11 @@
299	299	)))
300	300
301	301	(% class="wikigeneratedid" %)
302		-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.
	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.
303	303
304	304	(% class="box infomessage" %)
305	305	(((
306		-**Screencast** - final state of editor
	366	+**Screencast** - current state of editor
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(%%)
...	...	@@ -351,7 +351,7 @@
351	351	**Slide** recap of learning objectives
352	352	)))
353	353
354		-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.
	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.
355	355
356	356	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.
357	357
...	...	@@ -363,7 +363,7 @@
363	363	)))
364	364
365	365	(% class="wikigeneratedid" %)
366		-PyNN has been developed by many different people, with financial support from several organisations. I'd like to mention in particular the CNRS and the European Commission, through the FACETS, BrainScaleS, and Human Brain Project grants.
	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.
367	367
368	368	(% class="wikigeneratedid" %)
369		-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.
	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.