Last modified by adavison on 2022/10/04 13:55
From version 14.1
edited by adavison
on 2021/09/30 15:27
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To version 16.1
edited by annedevismes
on 2021/10/18 10:26
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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)",
... ... @@ -195,29 +195,20 @@
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
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)(%%)
205 +**Screencast** - changes in editor
206 +\\**...**
221 221  (% style="color:#000000" %)population1 = sim.Population(100, cell_type, label="Population 1")(%%)
222 222  (% style="color:#e74c3c" %)population2 = sim.Population(100, cell_type, label="Population 2")
223 223  population2.set(i_offset=0)(%%)
... ... @@ -224,19 +224,10 @@
224 224  (% style="color:#000000" %)population1.record("v")(%%)
225 225  (% style="color:#e74c3c" %)population2.record("v")(%%)
226 226  (% style="color:#000000" %)sim.run(100.0)(%%)
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()
213 +**...**
237 237  )))
238 238  
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.
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.
240 240  
241 241  (% class="box successmessage" %)
242 242  (((
... ... @@ -277,40 +277,20 @@
277 277  )))
278 278  
279 279  (% class="wikigeneratedid" %)
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.
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.
281 281  
282 282  (% class="box infomessage" %)
283 283  (((
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")(%%)
261 +**Screencast** - changes in editor
262 +
263 +
264 +**...**
265 +(% style="color:#000000" %)population2.record("v")(%%)
300 300  (% style="color:#e74c3c" %)connection_algorithm = sim.FixedProbabilityConnector(p=0.5)
301 301  synapse_type = sim.StaticSynapse(weight=0.5, delay=0.5)
302 302  connections = sim.Projection(population1, population2, connection_algorithm, synapse_type)(%%)
303 303  (% style="color:#000000" %)sim.run(100.0)(%%)
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()
270 +**...**
314 314  )))
315 315  
316 316  (% class="wikigeneratedid" %)
... ... @@ -318,25 +318,8 @@
318 318  
319 319  (% class="box infomessage" %)
320 320  (((
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)(%%)
278 +**Screencast** - changes in editor
279 +\\**...**
340 340  (% style="color:#000000" %)sim.run(100.0)(%%)
341 341  (% style="color:#e74c3c" %)data1_v(% style="color:#000000" %) = population1.get_data().segments[0].filter(name='v')[0](%%)
342 342  (% style="color:#e74c3c" %)data2_v = population2.get_data().segments[0].filter(name='v')[0](%%)
... ... @@ -359,11 +359,11 @@
359 359  )))
360 360  
361 361  (% class="wikigeneratedid" %)
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.
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.
363 363  
364 364  (% class="box infomessage" %)
365 365  (((
366 -**Screencast** - current state of editor
306 +**Screencast** - final state of editor
367 367  \\(% style="color:#000000" %)"""Simple network model using PyNN"""
368 368  \\import pyNN.(% style="color:#e74c3c" %)neuron(% style="color:#000000" %) as sim(%%)
369 369  (% style="color:#000000" %)from pyNN.utility.plotting import Figure, Panel(%%)
... ... @@ -411,7 +411,7 @@
411 411  **Slide** recap of learning objectives
412 412  )))
413 413  
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.
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.
415 415  
416 416  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.
417 417  
... ... @@ -423,7 +423,7 @@
423 423  )))
424 424  
425 425  (% class="wikigeneratedid" %)
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.
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.
427 427  
428 428  (% class="wikigeneratedid" %)
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.
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.