Last modified by adavison on 2022/10/04 13:55
From version 17.1
edited by shailesh
on 2021/12/01 12:55
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To version 9.8
edited by adavison
on 2021/08/04 16:01
Change comment: There is no comment for this version

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1 -XWiki.shailesh
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  
... ... @@ -21,10 +21,8 @@
21 21  
22 22  == Script ==
23 23  
24 -(% class="box successmessage" %)
25 -(((
26 -**Slide** showing tutorial title, PyNN logo, link to PyNN service page.
27 -)))
24 +(% class="wikigeneratedid" id="HIntroduceyourself28ifvideo29" %)
25 +(% class="small" %)**Introduce yourself**
28 28  
29 29  Hello, my name is X.
30 30  
... ... @@ -32,26 +32,20 @@
32 32  
33 33  For a list of the other tutorials in this series, you can visit ebrains.eu/service/pynn, that's p-y-n-n.
34 34  
35 -(% class="box successmessage" %)
36 -(((
37 -**Slide** listing learning objectives
38 -)))
33 +(% class="wikigeneratedid" id="HStatethelearningobjectives28Inthistutorial2CyouwilllearntodoX202629" %)
34 +(% class="small" %)**State the learning objectives**
39 39  
40 40  In this tutorial, you will learn the basics of PyNN: how to build a simple network of integrate-and-fire neurons using PyNN, how to run simulation experiments with this network using different simulators, and how to visualize the data generated by these experiments.
41 41  
42 -(% class="box successmessage" %)
43 -(((
44 -**Slide** listing prerequisites
45 -)))
38 +(% class="wikigeneratedid" id="HStateprerequisites" %)
39 +(% class="small" %)**State prerequisites**
46 46  
47 47  To follow this tutorial, you need a basic knowledge of neuroscience (high-school level or greater), basic familiarity with the Python programming language, and you should have already followed our earlier tutorial video which guides you through the installation process.
48 48  
49 49  This video covers PyNN 0.10. If you've installed a more recent version of PyNN, you might want to look for an updated version of this video.
50 50  
51 -(% class="box successmessage" %)
52 -(((
53 -**Slide** showing animation of leaky integrate-and-fire model
54 -)))
45 +(% class="wikigeneratedid" id="HDescription2Cexplanation2Candpractice" %)
46 +(% class="small" %)**Description, explanation, and practice**
55 55  
56 56  PyNN is a tool for building models of nervous systems, and parts of nervous systems, at the level of individual neurons and synapses.
57 57  
... ... @@ -61,309 +61,46 @@
61 61  
62 62  At that point, the neuron produces an action potential, also called a spike, and the membrane voltage is reset.
63 63  
64 -(% class="box infomessage" %)
65 -(((
66 -**Screencast** - blank document in editor
67 -)))
56 +Let's start by writing a docstring, "Simple network model using PyNN".
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.
58 +For now, we're going to use the NEST simulator to simulate this model, so we import the PyNN-for-NEST module.
70 70  
71 -Let's start by writing a docstring "Simple network model using PyNN".
60 +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.
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.
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.
76 -
77 -(% class="box infomessage" %)
78 -(((
79 -**Screencast** - current state of editor
80 -\\(% style="color:#e74c3c" %)"""Simple network model using PyNN"""
81 -\\import pyNN.nest as sim
82 -sim.setup(timestep=0.1)
83 -)))
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 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 -(% class="box infomessage" %)
90 -(((
91 -**Screencast** - current state of editor
92 -\\(% style="color:#000000" %)"""Simple network model using PyNN"""
93 -\\import pyNN.nest as sim
94 -sim.setup(timestep=0.1)(%%)
95 -(% style="color:#e74c3c" %)cell_type  = sim.IF_curr_exp(v_rest=-65, v_thresh=-55, v_reset=-65, tau_refrac=1, tau_m=10, cm=1, i_offset=0.1)
96 -)))
66 +Let's create 100 of these neurons, then we're going to record the membrane voltage, and run a simulation for 100 milliseconds.
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.
99 -
100 -(% class="box infomessage" %)
101 -(((
102 -**Screencast** - current state of editor
103 -\\(% style="color:#000000" %)"""Simple network model using PyNN"""
104 -\\import pyNN.nest as sim
105 -sim.setup(timestep=0.1)(%%)
106 -(% style="color:#000000" %)cell_type  = sim.IF_curr_exp(v_rest=-65, v_thresh=-55, v_reset=-65, tau_refrac=1, tau_m=10, cm=1, i_offset=0.1)(%%)
107 -(% style="color:#e74c3c" %)population1 = sim.Population(100, cell_type, label="Population 1")
108 -population1.record("v")
109 -sim.run(100.0)(%%)
110 -\\**Run script in terminal**
111 -)))
112 -
113 113  PyNN has some built-in tools for making simple plots, so let's import those, and plot the membrane voltage of the zeroth neuron in our population (remember Python starts counting at zero).
114 114  
115 -(% class="box infomessage" %)
116 -(((
117 -**Screencast** - current state of editor
118 -\\(% style="color:#000000" %)"""Simple network model using PyNN"""
119 -\\import pyNN.nest as sim(%%)
120 -(% style="color:#e74c3c" %)from pyNN.utility.plotting import Figure, Panel(%%)
121 -(% style="color:#000000" %)sim.setup(timestep=0.1)(%%)
122 -(% style="color:#000000" %)cell_type  = sim.IF_curr_exp(v_rest=-65, v_thresh=-55, v_reset=-65, tau_refrac=1, tau_m=10, cm=1, i_offset=0.1)(%%)
123 -(% style="color:#000000" %)population1 = sim.Population(100, cell_type, label="Population 1")
124 -population1.record("v")
125 -sim.run(100.0)(%%)
126 -(% style="color:#e74c3c" %)data_v = population1.get_data().segments[0].filter(name='v')[0]
127 -Figure(
128 - Panel(
129 - data_v[:, 0],
130 - xticks=True, xlabel="Time (ms)",
131 - yticks=True, ylabel="Membrane potential (mV)"
132 - ),
133 - title="Response of neuron #0",
134 - annotations="Simulated with NEST"
135 -).show()(%%)
136 -\\**Run script in terminal, show figure**
137 -)))
70 +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.
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.
72 +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.
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.
74 +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, and let's plot membrane voltage from _all_ the  neurons.
142 142  
143 -(% class="box infomessage" %)
144 -(((
145 -**Screencast** - changes in editor
146 -
76 +Now if we run our simulation again, we can see the effect of this heterogeneity in the neuron population.
147 147  
148 -**...**
149 -(% style="color:#000000" %)Figure(
150 - Panel(
151 - data_v[:, (% style="color:#e74c3c" %)0:5(% style="color:#000000" %)],
152 - xticks=True, xlabel="Time (ms)",
153 - yticks=True, ylabel="Membrane potential (mV)"
154 - ),
155 - title="Response of (% style="color:#e74c3c" %)first five neurons(% style="color:#000000" %)",
156 - annotations="Simulated with NEST"
157 -).show()(%%)
158 -\\**Run script in terminal, show figure**
159 -)))
78 +TO BE COMPLETED
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.
80 +(% class="wikigeneratedid" id="HSummary28Inthistutorial2CyouhavelearnedtodoX202629" %)
81 +(% class="small" %)**Summary (In this tutorial, you have learned to do X…)**
162 162  
163 -(% class="box infomessage" %)
164 -(((
165 -**Screencast** - changes in editor
166 -\\(% style="color:#000000" %)"""Simple network model using PyNN"""
167 -\\import pyNN.nest as sim(%%)
168 -(% style="color:#000000" %)from pyNN.utility.plotting import Figure, Panel(%%)
169 -(% style="color:#e74c3c" %)from pyNN.random import RandomDistribution(%%)
170 -(% style="color:#000000" %)sim.setup(timestep=0.1)(%%)
171 -(% style="color:#000000" %)cell_type  = sim.IF_curr_exp(
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=0.1)(%%)
176 -
83 +.
177 177  
178 -**...**
179 -
85 +(% class="wikigeneratedid" id="HAcknowledgementsifappropriate" %)
86 +(% class="small" %)**Acknowledgements if appropriate**
180 180  
181 -(% style="color:#000000" %)Figure(
182 - Panel(
183 - data_v[:, 0:5],
184 - xticks=True, xlabel="Time (ms)",
185 - yticks=True, ylabel="Membrane potential (mV)"
186 - ),
187 - title="Response of first five neurons (% style="color:#e74c3c" %)with heterogeneous parameters(% style="color:#000000" %)",
188 - annotations="Simulated with NEST"
189 -).show()(%%)
190 -\\**Run script in terminal, show figure**
191 -)))
88 +.
192 192  
193 -Now, if we run our simulation again, we can see the effect of this heterogeneity in the neuron population.
90 +(% class="wikigeneratedid" id="HReferencestowebsites28Formoreinformation2Cvisitusat202629" %)
91 +(% class="small" %)**References to websites (For more information, visit us at…)**
194 194  
195 -(% class="box successmessage" %)
196 -(((
197 -**Slide** showing addition of second population and of connections between them
198 -)))
93 +.
199 199  
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.
95 +(% class="wikigeneratedid" id="HContactinformation28Forquestions2Ccontactusat202629" %)
96 +(% class="small" %)**Contact information (For questions, contact us at…)**
202 202  
203 -(% class="box infomessage" %)
204 -(((
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
307 -\\(% style="color:#000000" %)"""Simple network model using PyNN"""
308 -\\import pyNN.(% style="color:#e74c3c" %)neuron(% style="color:#000000" %) as sim(%%)
309 -(% style="color:#000000" %)from pyNN.utility.plotting import Figure, Panel(%%)
310 -(% style="color:#000000" %)from pyNN.random import RandomDistribution(%%)
311 -(% style="color:#000000" %)sim.setup(timestep=0.1)(%%)
312 -(% style="color:#000000" %)cell_type  = sim.IF_curr_exp(
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=0.1)(%%)
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")
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]
328 -Figure(
329 - Panel(
330 - data1_v[:, 0:5],
331 - xticks=True,
332 - yticks=True, ylabel="Membrane potential (mV)"
333 - ),
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**
344 -)))
345 -
346 -(% class="wikigeneratedid" %)
347 -As you would hope, NEST and NEURON give essentially identical results.
348 -
349 -(% class="box successmessage" %)
350 -(((
351 -**Slide** recap of learning objectives
352 -)))
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.
355 -
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 -
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.
359 -
360 -(% class="box successmessage" %)
361 -(((
362 -**Slide** acknowledgements, contact information
363 -)))
364 -
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.
367 -
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.
98 +.