Last modified by adavison on 2022/10/04 13:55
From version 28.1
edited by adavison
on 2022/10/04 13:55
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To version 15.1
edited by adavison
on 2021/09/30 15:30
Change comment: There is no comment for this version

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1 -
2 -(% class="box successmessage" %)
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3 3  (((
4 -[[https:~~/~~/www.youtube.com/watch?v=zBLNfJiEvRc>>https://www.youtube.com/watch?v=zBLNfJiEvRc]]
3 +tutorial under development
5 5  )))
6 6  
7 7  == Learning objectives ==
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10 10  
11 11  == Audience ==
12 12  
13 -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.
14 14  
15 15  == Prerequisites ==
16 16  
17 -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.
18 18  
19 19  == Format ==
20 20  
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45 45  **Slide** listing prerequisites
46 46  )))
47 47  
48 -To follow this tutorial, you need a basic knowledge of neuroscience (high-school level or higher), basic familiarity with the Python programming language, and you should have already followed our earlier tutorial video which guides you through the installation process.
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.
49 49  
50 50  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.
51 51  
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67 67  **Screencast** - blank document in editor
68 68  )))
69 69  
70 -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.
71 71  
72 -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".
73 73  
74 -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.
75 75  
76 -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.
77 77  
78 78  (% class="box infomessage" %)
79 79  (((
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85 85  
86 86  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.
87 87  
88 -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.
89 89  
90 90  (% class="box infomessage" %)
91 91  (((
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93 93  \\(% style="color:#000000" %)"""Simple network model using PyNN"""
94 94  \\import pyNN.nest as sim
95 95  sim.setup(timestep=0.1)(%%)
96 -(% 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=1.1)
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)
97 97  )))
98 98  
99 -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.
100 100  
101 101  (% class="box infomessage" %)
102 102  (((
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104 104  \\(% style="color:#000000" %)"""Simple network model using PyNN"""
105 105  \\import pyNN.nest as sim
106 106  sim.setup(timestep=0.1)(%%)
107 -(% 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=1.1)(%%)
106 +(% 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)(%%)
108 108  (% style="color:#e74c3c" %)population1 = sim.Population(100, cell_type, label="Population 1")
109 109  population1.record("v")
110 110  sim.run(100.0)(%%)
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120 120  \\import pyNN.nest as sim(%%)
121 121  (% style="color:#e74c3c" %)from pyNN.utility.plotting import Figure, Panel(%%)
122 122  (% style="color:#000000" %)sim.setup(timestep=0.1)(%%)
123 -(% 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=1.1)(%%)
122 +(% 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)(%%)
124 124  (% style="color:#000000" %)population1 = sim.Population(100, cell_type, label="Population 1")
125 125  population1.record("v")
126 126  sim.run(100.0)(%%)
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137 137  \\**Run script in terminal, show figure**
138 138  )))
139 139  
140 -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.
141 141  
142 -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.
143 143  
144 144  (% class="box infomessage" %)
145 145  (((
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159 159  \\**Run script in terminal, show figure**
160 160  )))
161 161  
162 -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.
163 163  
164 164  (% class="box infomessage" %)
165 165  (((
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167 167  \\(% style="color:#000000" %)"""Simple network model using PyNN"""
168 168  \\import pyNN.nest as sim(%%)
169 169  (% style="color:#000000" %)from pyNN.utility.plotting import Figure, Panel(%%)
170 -(% style="color:#e74c3c" %)from pyNN.random import RandomDistribution, NumpyRNG(%%)
169 +(% style="color:#e74c3c" %)from pyNN.random import RandomDistribution(%%)
171 171  (% style="color:#000000" %)sim.setup(timestep=0.1)(%%)
172 -(% style="color:#e74c3c" %)rng = NumpyRNG(seed=1)(%%)
173 173  (% style="color:#000000" %)cell_type  = sim.IF_curr_exp(
174 - (% style="color:#e74c3c" %) v_rest=RandomDistribution('normal', mu=-65.0, sigma=1.0, rng=rng),
175 - v_thresh=RandomDistribution('normal', mu=-55.0, sigma=1.0, rng=rng),
176 - v_reset=RandomDistribution('normal', mu=-65.0, sigma=1.0, rng=rng), (%%)
177 -(% 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" %) t_refrac=1, tau_m=10, cm=1, i_offset=0.1)(%%)
176 +
178 178  
179 179  **...**
180 180  
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191 191  \\**Run script in terminal, show figure**
192 192  )))
193 193  
194 -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.
195 195  
196 196  (% class="box successmessage" %)
197 197  (((
198 -**Slide** showing addition of second population and of connections between them
197 +**Slide** showing addition of second population, and of connections between them
199 199  )))
200 200  
201 201  (% class="wikigeneratedid" %)
202 -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.
203 203  
204 204  (% class="box infomessage" %)
205 205  (((
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214 214  **...**
215 215  )))
216 216  
217 -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.
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.
218 218  
219 219  (% class="box successmessage" %)
220 220  (((
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255 255  )))
256 256  
257 257  (% class="wikigeneratedid" %)
258 -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.
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.
259 259  
260 260  (% class="box infomessage" %)
261 261  (((
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264 264  
265 265  **...**
266 266  (% style="color:#000000" %)population2.record("v")(%%)
267 -(% style="color:#e74c3c" %)connection_algorithm = sim.FixedProbabilityConnector(p_connect=0.5, rng=rng)
266 +(% style="color:#e74c3c" %)connection_algorithm = sim.FixedProbabilityConnector(p=0.5)
268 268  synapse_type = sim.StaticSynapse(weight=0.5, delay=0.5)
269 269  connections = sim.Projection(population1, population2, connection_algorithm, synapse_type)(%%)
270 270  (% style="color:#000000" %)sim.run(100.0)(%%)
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290 290   (% style="color:#e74c3c" %)Panel(
291 291   data2_v[:, 0:5],
292 292   xticks=True, xlabel="Time (ms)",
293 - yticks=True
292 + yticks=True"
294 294   ),(%%)
295 295  (% style="color:#000000" %) title="Response of (% style="color:#e74c3c" %)simple network(% style="color:#000000" %)",
296 296   annotations="Simulated with NEST"
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300 300  )))
301 301  
302 302  (% class="wikigeneratedid" %)
303 -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.
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.
304 304  
305 305  (% class="box infomessage" %)
306 306  (((
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308 308  \\(% style="color:#000000" %)"""Simple network model using PyNN"""
309 309  \\import pyNN.(% style="color:#e74c3c" %)neuron(% style="color:#000000" %) as sim(%%)
310 310  (% style="color:#000000" %)from pyNN.utility.plotting import Figure, Panel(%%)
311 -(% style="color:#000000" %)from pyNN.random import RandomDistribution, NumpyRNG(%%)
310 +(% style="color:#000000" %)from pyNN.random import RandomDistribution(%%)
312 312  (% style="color:#000000" %)sim.setup(timestep=0.1)(%%)
313 -(% style="color:#000000" %)rng = NumpyRNG(seed=1)(%%)
314 314  (% style="color:#000000" %)cell_type  = sim.IF_curr_exp(
315 - v_rest=RandomDistribution('normal', mu=-65.0, sigma=1.0, rng=rng),
316 - v_thresh=RandomDistribution('normal', mu=-55.0, sigma=1.0, rng=rng),
317 - v_reset=RandomDistribution('normal', mu=-65.0, sigma=1.0, rng=rng), 
318 - tau_refrac=1, tau_m=10, cm=1, i_offset=1.1)
319 -population1 = sim.Population(100, cell_type, label="Population 1")
320 -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" %) t_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")
321 321  population2.set(i_offset=0)
322 322  population1.record("v")
323 -population2.record("v")
324 -connection_algorithm = sim.FixedProbabilityConnector(p_connect=0.5, rng=rng)
321 +population2.record("v")(%%)
322 +(% style="color:#000000" %)connection_algorithm = sim.FixedProbabilityConnector(p=0.5)
325 325  synapse_type = sim.StaticSynapse(weight=0.5, delay=0.5)
326 326  connections = sim.Projection(population1, population2, connection_algorithm, synapse_type)(%%)
327 327  (% style="color:#000000" %)sim.run(100.0)(%%)
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336 336   Panel(
337 337   data2_v[:, 0:5],
338 338   xticks=True, xlabel="Time (ms)",
339 - yticks=True
337 + yticks=True"
340 340   ),(%%)
341 341  (% style="color:#000000" %) title="Response of simple network",
342 342   annotations="Simulated with (% style="color:#e74c3c" %)NEURON(% style="color:#000000" %)"
... ... @@ -353,11 +353,11 @@
353 353  **Slide** recap of learning objectives
354 354  )))
355 355  
356 -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.
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.
357 357  
358 358  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.
359 359  
360 -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.
361 361  
362 362  (% class="box successmessage" %)
363 363  (((
... ... @@ -365,7 +365,7 @@
365 365  )))
366 366  
367 367  (% class="wikigeneratedid" %)
368 -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.
366 +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.
369 369  
370 370  (% class="wikigeneratedid" %)
371 -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.
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.