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 11.2
edited by adavison
on 2021/09/30 13:51
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)",
... ... @@ -192,178 +192,24 @@
192 192  
193 193  Now if we run our simulation again, we can see the effect of this heterogeneity in the neuron population.
194 194  
195 -(% class="box successmessage" %)
196 -(((
197 -**Slide** showing addition of second population, and of connections between them
198 -)))
200 +TO BE COMPLETED
199 199  
200 -(% class="wikigeneratedid" %)
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.
202 +(% class="wikigeneratedid" id="HSummary28Inthistutorial2CyouhavelearnedtodoX202629" %)
203 +(% class="small" %)**Summary (In this tutorial, you have learned to do X…)**
202 202  
203 -(% class="box infomessage" %)
204 -(((
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 -)))
205 +.
215 215  
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.
207 +(% class="wikigeneratedid" id="HAcknowledgementsifappropriate" %)
208 +(% class="small" %)**Acknowledgements if appropriate**
217 217  
218 -(% class="box successmessage" %)
219 -(((
220 -**Slide** showing all-to-all connections
221 -)))
210 +.
222 222  
223 -We could connect all neurons in Population 1 to all those in Population 2.
212 +(% class="wikigeneratedid" id="HReferencestowebsites28Formoreinformation2Cvisitusat202629" %)
213 +(% class="small" %)**References to websites (For more information, visit us at…)**
224 224  
225 -(% class="box successmessage" %)
226 -(((
227 -**Slide** showing random connections
228 -)))
215 +.
229 229  
230 -We could connect the populations randomly, in several different ways.
217 +(% class="wikigeneratedid" id="HContactinformation28Forquestions2Ccontactusat202629" %)
218 +(% class="small" %)**Contact information (For questions, contact us at…)**
231 231  
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
307 -\\(% style="color:#000000" %)"""Simple network model using PyNN"""
308 -\\import pyNN.(% style="color:#e74c3c" %)neuron(% style="color:#000000" %) as sim(%%)
309 -(% style="color:#000000" %)from pyNN.utility.plotting import Figure, Panel(%%)
310 -(% style="color:#000000" %)from pyNN.random import RandomDistribution(%%)
311 -(% style="color:#000000" %)sim.setup(timestep=0.1)(%%)
312 -(% style="color:#000000" %)cell_type  = sim.IF_curr_exp(
313 - (% style="color:#e74c3c" %) (% style="color:#000000" %)v_rest=RandomDistribution('normal', {'mu': -65.0, 'sigma': 1.0}),
314 - v_thresh=RandomDistribution('normal', {'mu': -55.0, 'sigma': 1.0}),
315 - v_reset=RandomDistribution('normal', {'mu': -65.0, 'sigma': 1.0}), (%%)
316 -(% style="color:#000000" %) t_refrac=1, tau_m=10, cm=1, i_offset=0.1)(%%)
317 -(% style="color:#000000" %)population1 = sim.Population(100, cell_type, label="Population 1")(%%)
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]
328 -Figure(
329 - Panel(
330 - data1_v[:, 0:5],
331 - xticks=True,
332 - yticks=True, ylabel="Membrane potential (mV)"
333 - ),
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**
344 -)))
345 -
346 -(% class="wikigeneratedid" %)
347 -As you would hope, NEST and NEURON give essentially identical results.
348 -
349 -(% class="box successmessage" %)
350 -(((
351 -**Slide** recap of learning objectives
352 -)))
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.
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.
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.
359 -
360 -(% class="box successmessage" %)
361 -(((
362 -**Slide** acknowledgements, contact information
363 -)))
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.
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.
220 +.