Version 28.1 by adavison on 2022/10/04 13:55
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4 [[https:~~/~~/www.youtube.com/watch?v=zBLNfJiEvRc>>https://www.youtube.com/watch?v=zBLNfJiEvRc]]
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7 == Learning objectives ==
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9 In this tutorial, you will learn 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.
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11 == Audience ==
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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.
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15 == Prerequisites ==
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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.
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19 == Format ==
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21 This tutorial will be a video combining slides, animations, and screencast elements. The intended duration is 10 minutes.
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23 == Script ==
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27 **Slide** showing tutorial title, PyNN logo, link to PyNN service page.
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30 Hello, my name is X.
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32 This video is one of a series of tutorials for PyNN, which is Python software for modelling and simulating spiking neural networks.
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34 For a list of the other tutorials in this series, you can visit ebrains.eu/service/pynn, that's p-y-n-n.
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38 **Slide** listing learning objectives
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41 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.
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45 **Slide** listing prerequisites
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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.
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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.
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54 **Slide** showing animation of leaky integrate-and-fire model
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57 PyNN is a tool for building models of nervous systems, and parts of nervous systems, at the level of individual neurons and synapses.
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59 We'll start off creating a group of 100 neurons, using a really simple model of a neuron, the leaky integrate-and-fire model.
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61 When we inject positive current into this model, either from an electrode or from an excitatory synapse, it increases the voltage across the cell membrane, until the voltage reaches a certain threshold.
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63 At that point, the neuron produces an action potential, also called a spike, and the membrane voltage is reset.
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67 **Screencast** - blank document in editor
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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.
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72 Let's start by writing a docstring "Simple network model using PyNN".
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74 For now, we're going to use the NEST simulator to simulate this model; so, we import the PyNN-for-NEST module.
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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.
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79 (((
80 **Screencast** - current state of editor
81 \\(% style="color:#e74c3c" %)"""Simple network model using PyNN"""
82 \\import pyNN.nest as sim
83 sim.setup(timestep=0.1)
84 )))
85
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.
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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.
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91 (((
92 **Screencast** - current state of editor
93 \\(% style="color:#000000" %)"""Simple network model using PyNN"""
94 \\import pyNN.nest as sim
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)
97 )))
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.
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102 (((
103 **Screencast** - current state of editor
104 \\(% style="color:#000000" %)"""Simple network model using PyNN"""
105 \\import pyNN.nest as sim
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)(%%)
108 (% style="color:#e74c3c" %)population1 = sim.Population(100, cell_type, label="Population 1")
109 population1.record("v")
110 sim.run(100.0)(%%)
111 \\**Run script in terminal**
112 )))
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114 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).
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117 (((
118 **Screencast** - current state of editor
119 \\(% style="color:#000000" %)"""Simple network model using PyNN"""
120 \\import pyNN.nest as sim(%%)
121 (% style="color:#e74c3c" %)from pyNN.utility.plotting import Figure, Panel(%%)
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)(%%)
124 (% style="color:#000000" %)population1 = sim.Population(100, cell_type, label="Population 1")
125 population1.record("v")
126 sim.run(100.0)(%%)
127 (% style="color:#e74c3c" %)data_v = population1.get_data().segments[0].filter(name='v')[0]
128 Figure(
129 Panel(
130 data_v[:, 0],
131 xticks=True, xlabel="Time (ms)",
132 yticks=True, ylabel="Membrane potential (mV)"
133 ),
134 title="Response of neuron #0",
135 annotations="Simulated with NEST"
136 ).show()(%%)
137 \\**Run script in terminal, show figure**
138 )))
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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.
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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.
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145 (((
146 **Screencast** - changes in editor
147
148
149 **...**
150 (% style="color:#000000" %)Figure(
151 Panel(
152 data_v[:, (% style="color:#e74c3c" %)0:5(% style="color:#000000" %)],
153 xticks=True, xlabel="Time (ms)",
154 yticks=True, ylabel="Membrane potential (mV)"
155 ),
156 title="Response of (% style="color:#e74c3c" %)first five neurons(% style="color:#000000" %)",
157 annotations="Simulated with NEST"
158 ).show()(%%)
159 \\**Run script in terminal, show figure**
160 )))
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.
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165 (((
166 **Screencast** - changes in editor
167 \\(% style="color:#000000" %)"""Simple network model using PyNN"""
168 \\import pyNN.nest as sim(%%)
169 (% style="color:#000000" %)from pyNN.utility.plotting import Figure, Panel(%%)
170 (% style="color:#e74c3c" %)from pyNN.random import RandomDistribution, NumpyRNG(%%)
171 (% style="color:#000000" %)sim.setup(timestep=0.1)(%%)
172 (% style="color:#e74c3c" %)rng = NumpyRNG(seed=1)(%%)
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)
178
179 **...**
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181
182 (% style="color:#000000" %)Figure(
183 Panel(
184 data_v[:, 0:5],
185 xticks=True, xlabel="Time (ms)",
186 yticks=True, ylabel="Membrane potential (mV)"
187 ),
188 title="Response of first five neurons (% style="color:#e74c3c" %)with heterogeneous parameters(% style="color:#000000" %)",
189 annotations="Simulated with NEST"
190 ).show()(%%)
191 \\**Run script in terminal, show figure**
192 )))
193
194 Now, if we run our simulation again, we can see the effect of this heterogeneity in the neuron population.
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197 (((
198 **Slide** showing addition of second population and of connections between them
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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.
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205 (((
206 **Screencast** - changes in editor
207 \\**...**
208 (% style="color:#000000" %)population1 = sim.Population(100, cell_type, label="Population 1")(%%)
209 (% style="color:#e74c3c" %)population2 = sim.Population(100, cell_type, label="Population 2")
210 population2.set(i_offset=0)(%%)
211 (% style="color:#000000" %)population1.record("v")(%%)
212 (% style="color:#e74c3c" %)population2.record("v")(%%)
213 (% style="color:#000000" %)sim.run(100.0)(%%)
214 **...**
215 )))
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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.
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220 (((
221 **Slide** showing all-to-all connections
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224 We could connect all neurons in Population 1 to all those in Population 2.
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228 **Slide** showing random connections
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231 We could connect the populations randomly, in several different ways.
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235 **Slide** showing distance-dependent connections
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239 We could connect the populations randomly, but with a probability of connection that depends on the distance between the neurons.
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243 **Slide** showing explicit lists of connections
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247 Or we could connect the neurons in a very specific manner, based on an explicit list of connections.
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250 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.
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253 (((
254 **Slide** showing addition of second population, and of connections between them, labelled as a Projection.
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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.
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261 (((
262 **Screencast** - changes in editor
263
264
265 **...**
266 (% style="color:#000000" %)population2.record("v")(%%)
267 (% style="color:#e74c3c" %)connection_algorithm = sim.FixedProbabilityConnector(p_connect=0.5, rng=rng)
268 synapse_type = sim.StaticSynapse(weight=0.5, delay=0.5)
269 connections = sim.Projection(population1, population2, connection_algorithm, synapse_type)(%%)
270 (% style="color:#000000" %)sim.run(100.0)(%%)
271 **...**
272 )))
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275 Finally, let's update our figure, by adding a second panel to show the responses of Population 2.
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278 (((
279 **Screencast** - changes in editor
280 \\**...**
281 (% style="color:#000000" %)sim.run(100.0)(%%)
282 (% style="color:#e74c3c" %)data1_v(% style="color:#000000" %) = population1.get_data().segments[0].filter(name='v')[0](%%)
283 (% style="color:#e74c3c" %)data2_v = population2.get_data().segments[0].filter(name='v')[0](%%)
284 (% style="color:#000000" %)Figure(
285 Panel(
286 (% style="color:#e74c3c" %)data1_v(% style="color:#000000" %)[:, 0:5],
287 xticks=True, (% style="color:#e74c3c" %)--xlabel="Time (ms)",--(%%)
288 (% style="color:#000000" %) yticks=True, ylabel="Membrane potential (mV)"
289 ),
290 (% style="color:#e74c3c" %)Panel(
291 data2_v[:, 0:5],
292 xticks=True, xlabel="Time (ms)",
293 yticks=True
294 ),(%%)
295 (% style="color:#000000" %) title="Response of (% style="color:#e74c3c" %)simple network(% style="color:#000000" %)",
296 annotations="Simulated with NEST"
297 ).show()
298
299 **Run script in terminal, show figure**
300 )))
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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.
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306 (((
307 **Screencast** - final state of editor
308 \\(% style="color:#000000" %)"""Simple network model using PyNN"""
309 \\import pyNN.(% style="color:#e74c3c" %)neuron(% style="color:#000000" %) as sim(%%)
310 (% style="color:#000000" %)from pyNN.utility.plotting import Figure, Panel(%%)
311 (% style="color:#000000" %)from pyNN.random import RandomDistribution, NumpyRNG(%%)
312 (% style="color:#000000" %)sim.setup(timestep=0.1)(%%)
313 (% style="color:#000000" %)rng = NumpyRNG(seed=1)(%%)
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")
321 population2.set(i_offset=0)
322 population1.record("v")
323 population2.record("v")
324 connection_algorithm = sim.FixedProbabilityConnector(p_connect=0.5, rng=rng)
325 synapse_type = sim.StaticSynapse(weight=0.5, delay=0.5)
326 connections = sim.Projection(population1, population2, connection_algorithm, synapse_type)(%%)
327 (% style="color:#000000" %)sim.run(100.0)(%%)
328 (% style="color:#000000" %)data1_v = population1.get_data().segments[0].filter(name='v')[0]
329 data2_v = population2.get_data().segments[0].filter(name='v')[0]
330 Figure(
331 Panel(
332 data1_v[:, 0:5],
333 xticks=True,
334 yticks=True, ylabel="Membrane potential (mV)"
335 ),
336 Panel(
337 data2_v[:, 0:5],
338 xticks=True, xlabel="Time (ms)",
339 yticks=True
340 ),(%%)
341 (% style="color:#000000" %) title="Response of simple network",
342 annotations="Simulated with (% style="color:#e74c3c" %)NEURON(% style="color:#000000" %)"
343 ).show()
344
345 **Run script in terminal, show figure**
346 )))
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349 As you would hope, NEST and NEURON give essentially identical results.
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352 (((
353 **Slide** recap of learning objectives
354 )))
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.
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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.
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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.
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363 (((
364 **Slide** acknowledgements, contact information
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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.
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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.