Last modified by adavison on 2022/10/04 13:55
From version 13.2
edited by adavison
on 2021/09/30 14:31
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)(%%)
... ... @@ -224,16 +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()
213 +**...**
237 237  )))
238 238  
239 239  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.
... ... @@ -281,36 +281,16 @@
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](%%)
... ... @@ -363,7 +363,7 @@
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(%%)
... ... @@ -406,22 +406,24 @@
406 406  (% class="wikigeneratedid" %)
407 407  As you would hope, NEST and NEURON give essentially identical results.
408 408  
409 -(% class="wikigeneratedid" id="HSummary28Inthistutorial2CyouhavelearnedtodoX202629" %)
410 -(% class="small" %)**Summary (In this tutorial, you have learned to do X…)**
349 +(% class="box successmessage" %)
350 +(((
351 +**Slide** recap of learning objectives
352 +)))
411 411  
412 -.
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.
413 413  
414 -(% class="wikigeneratedid" id="HAcknowledgementsifappropriate" %)
415 -(% class="small" %)**Acknowledgements if appropriate**
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.
416 416  
417 -.
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.
418 418  
419 -(% class="wikigeneratedid" id="HReferencestowebsites28Formoreinformation2Cvisitusat202629" %)
420 -(% class="small" %)**References to websites (For more information, visit us at…)**
360 +(% class="box successmessage" %)
361 +(((
362 +**Slide** acknowledgements, contact information
363 +)))
421 421  
422 -.
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.
423 423  
424 -(% class="wikigeneratedid" id="HContactinformation28Forquestions2Ccontactusat202629" %)
425 -(% class="small" %)**Contact information (For questions, contact us at…)**
426 -
427 -.
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.