Last modified by adavison on 2022/10/04 13:55
From version 11.4
edited by adavison
on 2021/09/30 14:18
Change comment: There is no comment for this version
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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... ... @@ -142,17 +142,11 @@
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)",
... ... @@ -168,7 +168,7 @@
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)",
... ... @@ -207,17 +207,8 @@
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)(%%)
... ... @@ -224,17 +224,7 @@
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()(%%)
237 -\\**Run script in terminal, show figure**
213 +**...**
238 238  )))
239 239  
240 240  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.
... ... @@ -282,9 +282,54 @@
282 282  
283 283  (% class="box infomessage" %)
284 284  (((
285 -**Screencast** - current state of editor
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
286 286  \\(% style="color:#000000" %)"""Simple network model using PyNN"""
287 -\\import pyNN.nest as sim(%%)
308 +\\import pyNN.(% style="color:#e74c3c" %)neuron(% style="color:#000000" %) as sim(%%)
288 288  (% style="color:#000000" %)from pyNN.utility.plotting import Figure, Panel(%%)
289 289  (% style="color:#000000" %)from pyNN.random import RandomDistribution(%%)
290 290  (% style="color:#000000" %)sim.setup(timestep=0.1)(%%)
... ... @@ -298,39 +298,51 @@
298 298  population2.set(i_offset=0)
299 299  population1.record("v")
300 300  population2.record("v")(%%)
301 -(% style="color:#e74c3c" %)connection_algorithm = sim.FixedProbabilityConnector(p=0.5)
322 +(% style="color:#000000" %)connection_algorithm = sim.FixedProbabilityConnector(p=0.5)
302 302  synapse_type = sim.StaticSynapse(weight=0.5, delay=0.5)
303 303  connections = sim.Projection(population1, population2, connection_algorithm, synapse_type)(%%)
304 304  (% style="color:#000000" %)sim.run(100.0)(%%)
305 -(% style="color:#000000" %)data_v = population1.get_data().segments[0].filter(name='v')[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]
306 306  Figure(
307 307   Panel(
308 - data_v[:, 0:5],
309 - xticks=True, xlabel="Time (ms)",
330 + data1_v[:, 0:5],
331 + xticks=True,
310 310   yticks=True, ylabel="Membrane potential (mV)"
311 311   ),
312 - title="Response of first five neurons with heterogeneous parameters",
313 - annotations="Simulated with NEST"
314 -).show()(%%)
315 -\\**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**
316 316  )))
317 317  
318 -(% class="wikigeneratedid" id="HSummary28Inthistutorial2CyouhavelearnedtodoX202629" %)
319 -(% class="small" %)**Summary (In this tutorial, you have learned to do X…)**
346 +(% class="wikigeneratedid" %)
347 +As you would hope, NEST and NEURON give essentially identical results.
320 320  
321 -.
349 +(% class="box successmessage" %)
350 +(((
351 +**Slide** recap of learning objectives
352 +)))
322 322  
323 -(% class="wikigeneratedid" id="HAcknowledgementsifappropriate" %)
324 -(% class="small" %)**Acknowledgements if appropriate**
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.
325 325  
326 -.
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.
327 327  
328 -(% class="wikigeneratedid" id="HReferencestowebsites28Formoreinformation2Cvisitusat202629" %)
329 -(% class="small" %)**References to websites (For more information, visit us at…)**
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.
330 330  
331 -.
360 +(% class="box successmessage" %)
361 +(((
362 +**Slide** acknowledgements, contact information
363 +)))
332 332  
333 -(% class="wikigeneratedid" id="HContactinformation28Forquestions2Ccontactusat202629" %)
334 -(% class="small" %)**Contact information (For questions, contact us at…)**
365 +(% class="wikigeneratedid" %)
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.
335 335  
336 -.
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.