Last modified by adavison on 2022/10/04 13:55
From version 15.1
edited by adavison
on 2021/09/30 15:30
Change comment: There is no comment for this version
To version 13.1
edited by adavison
on 2021/09/30 14:24
Change comment: There is no comment for this version

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... ... @@ -142,11 +142,17 @@
142 142  
143 143  (% class="box infomessage" %)
144 144  (((
145 -**Screencast** - changes in editor
146 -
147 -
148 -**...**
149 -(% style="color:#000000" %)Figure(
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(
150 150   Panel(
151 151   data_v[:, (% style="color:#e74c3c" %)0:5(% style="color:#000000" %)],
152 152   xticks=True, xlabel="Time (ms)",
... ... @@ -162,7 +162,7 @@
162 162  
163 163  (% class="box infomessage" %)
164 164  (((
165 -**Screencast** - changes in editor
171 +**Screencast** - current state of editor
166 166  \\(% style="color:#000000" %)"""Simple network model using PyNN"""
167 167  \\import pyNN.nest as sim(%%)
168 168  (% style="color:#000000" %)from pyNN.utility.plotting import Figure, Panel(%%)
... ... @@ -173,12 +173,11 @@
173 173   v_thresh=RandomDistribution('normal', {'mu': -55.0, 'sigma': 1.0}),
174 174   v_reset=RandomDistribution('normal', {'mu': -65.0, 'sigma': 1.0}), (%%)
175 175  (% style="color:#000000" %) t_refrac=1, tau_m=10, cm=1, i_offset=0.1)(%%)
176 -
177 -
178 -**...**
179 -
180 -
181 -(% style="color:#000000" %)Figure(
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(
182 182   Panel(
183 183   data_v[:, 0:5],
184 184   xticks=True, xlabel="Time (ms)",
... ... @@ -202,8 +202,17 @@
202 202  
203 203  (% class="box infomessage" %)
204 204  (((
205 -**Screencast** - changes in editor
206 -\\**...**
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)(%%)
207 207  (% style="color:#000000" %)population1 = sim.Population(100, cell_type, label="Population 1")(%%)
208 208  (% style="color:#e74c3c" %)population2 = sim.Population(100, cell_type, label="Population 2")
209 209  population2.set(i_offset=0)(%%)
... ... @@ -210,7 +210,16 @@
210 210  (% style="color:#000000" %)population1.record("v")(%%)
211 211  (% style="color:#e74c3c" %)population2.record("v")(%%)
212 212  (% style="color:#000000" %)sim.run(100.0)(%%)
213 -**...**
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()
214 214  )))
215 215  
216 216  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.
... ... @@ -258,54 +258,46 @@
258 258  
259 259  (% class="box infomessage" %)
260 260  (((
261 -**Screencast** - changes in editor
262 -
263 -
264 -**...**
265 -(% style="color:#000000" %)population2.record("v")(%%)
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")(%%)
266 266  (% style="color:#e74c3c" %)connection_algorithm = sim.FixedProbabilityConnector(p=0.5)
267 267  synapse_type = sim.StaticSynapse(weight=0.5, delay=0.5)
268 268  connections = sim.Projection(population1, population2, connection_algorithm, synapse_type)(%%)
269 269  (% 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(
304 +(% style="color:#000000" %)data_v = population1.get_data().segments[0].filter(name='v')[0]
305 +Figure(
284 284   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],
307 + data_v[:, 0:5],
291 291   xticks=True, xlabel="Time (ms)",
292 - yticks=True"
293 - ),(%%)
294 -(% style="color:#000000" %) title="Response of (% style="color:#e74c3c" %)simple network(% style="color:#000000" %)",
309 + yticks=True, ylabel="Membrane potential (mV)"
310 + ),
311 + title="Response of first five neurons with heterogeneous parameters",
295 295   annotations="Simulated with NEST"
296 296  ).show()
297 -
298 -**Run script in terminal, show figure**
299 299  )))
300 300  
301 301  (% 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.
317 +Finally, let's update our figure, by adding a second panel to show the responses of Population 2.
303 303  
304 304  (% class="box infomessage" %)
305 305  (((
306 -**Screencast** - final state of editor
321 +**Screencast** - current state of editor
307 307  \\(% style="color:#000000" %)"""Simple network model using PyNN"""
308 -\\import pyNN.(% style="color:#e74c3c" %)neuron(% style="color:#000000" %) as sim(%%)
323 +\\import pyNN.nest as sim(%%)
309 309  (% style="color:#000000" %)from pyNN.utility.plotting import Figure, Panel(%%)
310 310  (% style="color:#000000" %)from pyNN.random import RandomDistribution(%%)
311 311  (% style="color:#000000" %)sim.setup(timestep=0.1)(%%)
... ... @@ -323,47 +323,42 @@
323 323  synapse_type = sim.StaticSynapse(weight=0.5, delay=0.5)
324 324  connections = sim.Projection(population1, population2, connection_algorithm, synapse_type)(%%)
325 325  (% 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(
341 +(% style="color:#e74c3c" %)data1_v(% style="color:#000000" %) = population1.get_data().segments[0].filter(name='v')[0](%%)
342 +(% style="color:#e74c3c" %)data2_v = population2.get_data().segments[0].filter(name='v')[0](%%)
343 +(% style="color:#000000" %)Figure(
329 329   Panel(
330 - data1_v[:, 0:5],
331 - xticks=True,
332 - yticks=True, ylabel="Membrane potential (mV)"
345 + (% style="color:#e74c3c" %)data1_v(% style="color:#000000" %)[:, 0:5],
346 + xticks=True, (% style="color:#e74c3c" %)--xlabel="Time (ms)",--(%%)
347 +(% style="color:#000000" %) yticks=True, ylabel="Membrane potential (mV)"
333 333   ),
334 - Panel(
349 + (% style="color:#e74c3c" %)Panel(
335 335   data2_v[:, 0:5],
336 336   xticks=True, xlabel="Time (ms)",
337 337   yticks=True"
338 338   ),(%%)
339 -(% style="color:#000000" %) title="Response of simple network",
340 - annotations="Simulated with (% style="color:#e74c3c" %)NEURON(% style="color:#000000" %)"
354 +(% style="color:#000000" %) title="Response of (% style="color:#e74c3c" %)simple network(% style="color:#000000" %)",
355 + annotations="Simulated with NEST"
341 341  ).show()
342 342  
343 343  **Run script in terminal, show figure**
344 344  )))
345 345  
346 -(% class="wikigeneratedid" %)
347 -As you would hope, NEST and NEURON give essentially identical results.
361 +(% class="wikigeneratedid" id="HSummary28Inthistutorial2CyouhavelearnedtodoX202629" %)
362 +(% class="small" %)**Summary (In this tutorial, you have learned to do X…)**
348 348  
349 -(% class="box successmessage" %)
350 -(((
351 -**Slide** recap of learning objectives
352 -)))
364 +.
353 353  
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.
366 +(% class="wikigeneratedid" id="HAcknowledgementsifappropriate" %)
367 +(% class="small" %)**Acknowledgements if appropriate**
355 355  
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.
369 +.
357 357  
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.
371 +(% class="wikigeneratedid" id="HReferencestowebsites28Formoreinformation2Cvisitusat202629" %)
372 +(% class="small" %)**References to websites (For more information, visit us at…)**
359 359  
360 -(% class="box successmessage" %)
361 -(((
362 -**Slide** acknowledgements, contact information
363 -)))
374 +.
364 364  
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.
376 +(% class="wikigeneratedid" id="HContactinformation28Forquestions2Ccontactusat202629" %)
377 +(% class="small" %)**Contact information (For questions, contact us at…)**
367 367  
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.
379 +.