Last modified by adavison on 2022/10/04 13:55
From version 11.3
edited by adavison
on 2021/09/30 14:01
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)",
... ... @@ -207,9 +207,110 @@
207 207  
208 208  (% class="box infomessage" %)
209 209  (((
210 -**Screencast** - current state of editor
205 +**Screencast** - changes in editor
206 +\\**...**
207 +(% style="color:#000000" %)population1 = sim.Population(100, cell_type, label="Population 1")(%%)
208 +(% style="color:#e74c3c" %)population2 = sim.Population(100, cell_type, label="Population 2")
209 +population2.set(i_offset=0)(%%)
210 +(% style="color:#000000" %)population1.record("v")(%%)
211 +(% style="color:#e74c3c" %)population2.record("v")(%%)
212 +(% style="color:#000000" %)sim.run(100.0)(%%)
213 +**...**
214 +)))
215 +
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.
217 +
218 +(% class="box successmessage" %)
219 +(((
220 +**Slide** showing all-to-all connections
221 +)))
222 +
223 +We could connect all neurons in Population 1 to all those in Population 2.
224 +
225 +(% class="box successmessage" %)
226 +(((
227 +**Slide** showing random connections
228 +)))
229 +
230 +We could connect the populations randomly, in several different ways.
231 +
232 +(% class="box successmessage" %)
233 +(((
234 +**Slide** showing distance-dependent connections
235 +)))
236 +
237 +(% class="wikigeneratedid" %)
238 +We could connect the populations randomly, but with a probability of connection that depends on the distance between the neurons.
239 +
240 +(% class="box successmessage" %)
241 +(((
242 +**Slide** showing explicit lists of connections
243 +)))
244 +
245 +(% class="wikigeneratedid" %)
246 +Or we could connect the neurons in a very specific manner, based on an explicit list of connections.
247 +
248 +(% class="wikigeneratedid" %)
249 +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.
250 +
251 +(% class="box successmessage" %)
252 +(((
253 +**Slide** showing addition of second population, and of connections between them, labelled as a Projection.
254 +)))
255 +
256 +(% class="wikigeneratedid" %)
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.
258 +
259 +(% class="box infomessage" %)
260 +(((
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
211 211  \\(% style="color:#000000" %)"""Simple network model using PyNN"""
212 -\\import pyNN.nest as sim(%%)
308 +\\import pyNN.(% style="color:#e74c3c" %)neuron(% style="color:#000000" %) as sim(%%)
213 213  (% style="color:#000000" %)from pyNN.utility.plotting import Figure, Panel(%%)
214 214  (% style="color:#000000" %)from pyNN.random import RandomDistribution(%%)
215 215  (% style="color:#000000" %)sim.setup(timestep=0.1)(%%)
... ... @@ -219,38 +219,55 @@
219 219   v_reset=RandomDistribution('normal', {'mu': -65.0, 'sigma': 1.0}), (%%)
220 220  (% style="color:#000000" %) t_refrac=1, tau_m=10, cm=1, i_offset=0.1)(%%)
221 221  (% style="color:#000000" %)population1 = sim.Population(100, cell_type, label="Population 1")(%%)
222 -(% style="color:#000000" %)population1.record("v")
223 -sim.run(100.0)(%%)
224 -(% style="color:#000000" %)data_v = population1.get_data().segments[0].filter(name='v')[0]
318 +(% style="color:#000000" %)population2 = sim.Population(100, cell_type, label="Population 2")
319 +population2.set(i_offset=0)
320 +population1.record("v")
321 +population2.record("v")(%%)
322 +(% style="color:#000000" %)connection_algorithm = sim.FixedProbabilityConnector(p=0.5)
323 +synapse_type = sim.StaticSynapse(weight=0.5, delay=0.5)
324 +connections = sim.Projection(population1, population2, connection_algorithm, synapse_type)(%%)
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]
225 225  Figure(
226 226   Panel(
227 - data_v[:, 0:5],
228 - xticks=True, xlabel="Time (ms)",
330 + data1_v[:, 0:5],
331 + xticks=True,
229 229   yticks=True, ylabel="Membrane potential (mV)"
230 230   ),
231 - title="Response of first five neurons with heterogeneous parameters",
232 - annotations="Simulated with NEST"
233 -).show()(%%)
234 -\\**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**
235 235  )))
236 236  
346 +(% class="wikigeneratedid" %)
347 +As you would hope, NEST and NEURON give essentially identical results.
237 237  
238 -(% class="wikigeneratedid" id="HSummary28Inthistutorial2CyouhavelearnedtodoX202629" %)
239 -(% class="small" %)**Summary (In this tutorial, you have learned to do X…)**
349 +(% class="box successmessage" %)
350 +(((
351 +**Slide** recap of learning objectives
352 +)))
240 240  
241 -.
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.
242 242  
243 -(% class="wikigeneratedid" id="HAcknowledgementsifappropriate" %)
244 -(% 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.
245 245  
246 -.
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.
247 247  
248 -(% class="wikigeneratedid" id="HReferencestowebsites28Formoreinformation2Cvisitusat202629" %)
249 -(% class="small" %)**References to websites (For more information, visit us at…)**
360 +(% class="box successmessage" %)
361 +(((
362 +**Slide** acknowledgements, contact information
363 +)))
250 250  
251 -.
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.
252 252  
253 -(% class="wikigeneratedid" id="HContactinformation28Forquestions2Ccontactusat202629" %)
254 -(% class="small" %)**Contact information (For questions, contact us at…)**
255 -
256 -.
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.