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1		-XWiki.adavison
	1	+XWiki.annedevismes

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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 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.
	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.
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	(((
...	...	@@ -136,23 +136,17 @@
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** - 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(
	145	+**Screencast** - changes in editor
	146	+
	147	+
	148	+**...**
	149	+(% style="color:#000000" %)Figure(
156	156	Panel(
157	157	data_v[:, (% style="color:#e74c3c" %)0:5(% style="color:#000000" %)],
158	158	xticks=True, xlabel="Time (ms)",
...	...	@@ -164,11 +164,11 @@
164	164	\\**Run script in terminal, show figure**
165	165	)))
166	166
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.
	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.
168	168
169	169	(% class="box infomessage" %)
170	170	(((
171		-**Screencast** - current state of editor
	165	+**Screencast** - changes in editor
172	172	\\(% style="color:#000000" %)"""Simple network model using PyNN"""
173	173	\\import pyNN.nest as sim(%%)
174	174	(% style="color:#000000" %)from pyNN.utility.plotting import Figure, Panel(%%)
...	...	@@ -179,11 +179,12 @@
179	179	v_thresh=RandomDistribution('normal', {'mu': -55.0, 'sigma': 1.0}),
180	180	v_reset=RandomDistribution('normal', {'mu': -65.0, 'sigma': 1.0}), (%%)
181	181	(% style="color:#000000" %) t_refrac=1, tau_m=10, cm=1, i_offset=0.1)(%%)
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(
	176	+
	177	+
	178	+**...**
	179	+
	180	+
	181	+(% style="color:#000000" %)Figure(
187	187	Panel(
188	188	data_v[:, 0:5],
189	189	xticks=True, xlabel="Time (ms)",
...	...	@@ -195,21 +195,122 @@
195	195	\\**Run script in terminal, show figure**
196	196	)))
197	197
198		-Now if we run our simulation again, we can see the effect of this heterogeneity in the neuron population.
	193	+Now, if we run our simulation again, we can see the effect of this heterogeneity in the neuron population.
199	199
200	200	(% class="box successmessage" %)
201	201	(((
202		-**Slide** showing addition of second population, and of connections between them
	197	+**Slide** showing addition of second population and of connections between them
203	203	)))
204	204
205	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.
	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.
207	207
208	208	(% class="box infomessage" %)
209	209	(((
210		-**Screencast** - current state of editor
	205	+**Screencast** - changes in editor
	206	+\\**...**
	207	+(% style="color:#000000" %)population1 = sim.Population(100, cell_type, label="Population 1")(%%)
	208	+(% style="color:#e74c3c" %)population2 = sim.Population(100, cell_type, label="Population 2")
	209	+population2.set(i_offset=0)(%%)
	210	+(% style="color:#000000" %)population1.record("v")(%%)
	211	+(% style="color:#e74c3c" %)population2.record("v")(%%)
	212	+(% style="color:#000000" %)sim.run(100.0)(%%)
	213	+**...**
	214	+)))
	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.
	217	+
	218	+(% class="box successmessage" %)
	219	+(((
	220	+**Slide** showing all-to-all connections
	221	+)))
	222	+
	223	+We could connect all neurons in Population 1 to all those in Population 2.
	224	+
	225	+(% class="box successmessage" %)
	226	+(((
	227	+**Slide** showing random connections
	228	+)))
	229	+
	230	+We could connect the populations randomly, in several different ways.
	231	+
	232	+(% class="box successmessage" %)
	233	+(((
	234	+**Slide** showing distance-dependent connections
	235	+)))
	236	+
	237	+(% class="wikigeneratedid" %)
	238	+We could connect the populations randomly, but with a probability of connection that depends on the distance between the neurons.
	239	+
	240	+(% class="box successmessage" %)
	241	+(((
	242	+**Slide** showing explicit lists of connections
	243	+)))
	244	+
	245	+(% class="wikigeneratedid" %)
	246	+Or we could connect the neurons in a very specific manner, based on an explicit list of connections.
	247	+
	248	+(% class="wikigeneratedid" %)
	249	+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.
	250	+
	251	+(% class="box successmessage" %)
	252	+(((
	253	+**Slide** showing addition of second population, and of connections between them, labelled as a Projection.
	254	+)))
	255	+
	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.
	258	+
	259	+(% class="box infomessage" %)
	260	+(((
	261	+**Screencast** - changes in editor
	262	+
	263	+
	264	+**...**
	265	+(% style="color:#000000" %)population2.record("v")(%%)
	266	+(% style="color:#e74c3c" %)connection_algorithm = sim.FixedProbabilityConnector(p=0.5)
	267	+synapse_type = sim.StaticSynapse(weight=0.5, delay=0.5)
	268	+connections = sim.Projection(population1, population2, connection_algorithm, synapse_type)(%%)
	269	+(% style="color:#000000" %)sim.run(100.0)(%%)
	270	+**...**
	271	+)))
	272	+
	273	+(% class="wikigeneratedid" %)
	274	+Finally, let's update our figure, by adding a second panel to show the responses of Population 2.
	275	+
	276	+(% class="box infomessage" %)
	277	+(((
	278	+**Screencast** - changes in editor
	279	+\\**...**
	280	+(% style="color:#000000" %)sim.run(100.0)(%%)
	281	+(% style="color:#e74c3c" %)data1_v(% style="color:#000000" %) = population1.get_data().segments[0].filter(name='v')[0](%%)
	282	+(% style="color:#e74c3c" %)data2_v = population2.get_data().segments[0].filter(name='v')[0](%%)
	283	+(% style="color:#000000" %)Figure(
	284	+ Panel(
	285	+ (% style="color:#e74c3c" %)data1_v(% style="color:#000000" %)[:, 0:5],
	286	+ xticks=True, (% style="color:#e74c3c" %)--xlabel="Time (ms)",--(%%)
	287	+(% style="color:#000000" %) yticks=True, ylabel="Membrane potential (mV)"
	288	+ ),
	289	+ (% style="color:#e74c3c" %)Panel(
	290	+ data2_v[:, 0:5],
	291	+ xticks=True, xlabel="Time (ms)",
	292	+ yticks=True"
	293	+ ),(%%)
	294	+(% style="color:#000000" %) title="Response of (% style="color:#e74c3c" %)simple network(% style="color:#000000" %)",
	295	+ annotations="Simulated with NEST"
	296	+).show()
	297	+
	298	+**Run script in terminal, show figure**
	299	+)))
	300	+
	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.
	303	+
	304	+(% class="box infomessage" %)
	305	+(((
	306	+**Screencast** - final state of editor
211	211	\\(% style="color:#000000" %)"""Simple network model using PyNN"""
212		-\\import pyNN.nest as sim(%%)
	308	+\\import pyNN.(% style="color:#e74c3c" %)neuron(% style="color:#000000" %) as sim(%%)
213	213	(% style="color:#000000" %)from pyNN.utility.plotting import Figure, Panel(%%)
214	214	(% style="color:#000000" %)from pyNN.random import RandomDistribution(%%)
215	215	(% style="color:#000000" %)sim.setup(timestep=0.1)(%%)
...	...	@@ -219,38 +219,55 @@
219	219	v_reset=RandomDistribution('normal', {'mu': -65.0, 'sigma': 1.0}), (%%)
220	220	(% style="color:#000000" %) t_refrac=1, tau_m=10, cm=1, i_offset=0.1)(%%)
221	221	(% style="color:#000000" %)population1 = sim.Population(100, cell_type, label="Population 1")(%%)
222		-(% style="color:#000000" %)population1.record("v")
223		-sim.run(100.0)(%%)
224		-(% style="color:#000000" %)data_v = population1.get_data().segments[0].filter(name='v')[0]
	318	+(% style="color:#000000" %)population2 = sim.Population(100, cell_type, label="Population 2")
	319	+population2.set(i_offset=0)
	320	+population1.record("v")
	321	+population2.record("v")(%%)
	322	+(% style="color:#000000" %)connection_algorithm = sim.FixedProbabilityConnector(p=0.5)
	323	+synapse_type = sim.StaticSynapse(weight=0.5, delay=0.5)
	324	+connections = sim.Projection(population1, population2, connection_algorithm, synapse_type)(%%)
	325	+(% style="color:#000000" %)sim.run(100.0)(%%)
	326	+(% style="color:#000000" %)data1_v = population1.get_data().segments[0].filter(name='v')[0]
	327	+data2_v = population2.get_data().segments[0].filter(name='v')[0]
225	225	Figure(
226	226	Panel(
227		- data_v[:, 0:5],
228		- xticks=True, xlabel="Time (ms)",
	330	+ data1_v[:, 0:5],
	331	+ xticks=True,
229	229	yticks=True, ylabel="Membrane potential (mV)"
230	230	),
231		- title="Response of first five neurons with heterogeneous parameters",
232		- annotations="Simulated with NEST"
233		-).show()(%%)
234		-\\**Run script in terminal, show figure**
	334	+ Panel(
	335	+ data2_v[:, 0:5],
	336	+ xticks=True, xlabel="Time (ms)",
	337	+ yticks=True"
	338	+ ),(%%)
	339	+(% style="color:#000000" %) title="Response of simple network",
	340	+ annotations="Simulated with (% style="color:#e74c3c" %)NEURON(% style="color:#000000" %)"
	341	+).show()
	342	+
	343	+**Run script in terminal, show figure**
235	235	)))
236	236
	346	+(% class="wikigeneratedid" %)
	347	+As you would hope, NEST and NEURON give essentially identical results.
237	237
238		-(% class="wikigeneratedid" id="HSummary28Inthistutorial2CyouhavelearnedtodoX202629" %)
239		-(% class="small" %)**Summary (In this tutorial, you have learned to do X…)**
	349	+(% class="box successmessage" %)
	350	+(((
	351	+**Slide** recap of learning objectives
	352	+)))
240	240
241		-.
	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.
242	242
243		-(% class="wikigeneratedid" id="HAcknowledgementsifappropriate" %)
244		-(% class="small" %)**Acknowledgements if appropriate**
	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.
245	245
246		-.
	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.
247	247
248		-(% class="wikigeneratedid" id="HReferencestowebsites28Formoreinformation2Cvisitusat202629" %)
249		-(% class="small" %)**References to websites (For more information, visit us at…)**
	360	+(% class="box successmessage" %)
	361	+(((
	362	+**Slide** acknowledgements, contact information
	363	+)))
250	250
251		-.
	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.
252	252
253		-(% class="wikigeneratedid" id="HContactinformation28Forquestions2Ccontactusat202629" %)
254		-(% class="small" %)**Contact information (For questions, contact us at…)**
255		-
256		-.
	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.