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 11.5
edited by adavison
on 2021/09/30 14:20
Change comment: There is no comment for this version

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... ... @@ -219,8 +219,11 @@
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)(%%)
222 +(% style="color:#e74c3c" %)population2 = sim.Population(100, cell_type, label="Population 2")
223 +population2.set(i_offset=0)(%%)
224 +(% style="color:#000000" %)population1.record("v")(%%)
225 +(% style="color:#e74c3c" %)population2.record("v")(%%)
226 +(% style="color:#000000" %)sim.run(100.0)(%%)
224 224  (% style="color:#000000" %)data_v = population1.get_data().segments[0].filter(name='v')[0]
225 225  Figure(
226 226   Panel(
... ... @@ -230,11 +230,125 @@
230 230   ),
231 231   title="Response of first five neurons with heterogeneous parameters",
232 232   annotations="Simulated with NEST"
233 -).show()(%%)
234 -\\**Run script in terminal, show figure**
236 +).show()
235 235  )))
236 236  
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.
237 237  
241 +(% class="box successmessage" %)
242 +(((
243 +**Slide** showing all-to-all connections
244 +)))
245 +
246 +We could connect all neurons in Population 1 to all those in Population 2.
247 +
248 +(% class="box successmessage" %)
249 +(((
250 +**Slide** showing random connections
251 +)))
252 +
253 +We could connect the populations randomly, in several different ways.
254 +
255 +(% class="box successmessage" %)
256 +(((
257 +**Slide** showing distance-dependent connections
258 +)))
259 +
260 +(% class="wikigeneratedid" %)
261 +We could connect the populations randomly, but with a probability of connection that depends on the distance between the neurons.
262 +
263 +(% class="box successmessage" %)
264 +(((
265 +**Slide** showing explicit lists of connections
266 +)))
267 +
268 +(% class="wikigeneratedid" %)
269 +Or we could connect the neurons in a very specific manner, based on an explicit list of connections.
270 +
271 +(% class="wikigeneratedid" %)
272 +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.
273 +
274 +(% class="box successmessage" %)
275 +(((
276 +**Slide** showing addition of second population, and of connections between them, labelled as a Projection.
277 +)))
278 +
279 +(% class="wikigeneratedid" %)
280 +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.
281 +
282 +(% class="box infomessage" %)
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")(%%)
300 +(% style="color:#e74c3c" %)connection_algorithm = sim.FixedProbabilityConnector(p=0.5)
301 +synapse_type = sim.StaticSynapse(weight=0.5, delay=0.5)
302 +connections = sim.Projection(population1, population2, connection_algorithm, synapse_type)(%%)
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()
314 +)))
315 +
316 +(% class="wikigeneratedid" %)
317 +Finally, let's update our figure, by adding a second panel to show the responses of Population 2.
318 +
319 +(% class="box infomessage" %)
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:#e74c3c" %)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)(%%)
340 +(% style="color:#000000" %)sim.run(100.0)(%%)
341 +(% style="color:#000000" %)data_v = population1.get_data().segments[0].filter(name='v')[0]
342 +Figure(
343 + Panel(
344 + data_v[:, 0:5],
345 + xticks=True, xlabel="Time (ms)",
346 + yticks=True, ylabel="Membrane potential (mV)"
347 + ),
348 + title="Response of first five neurons with heterogeneous parameters",
349 + annotations="Simulated with NEST"
350 +).show()
351 +
352 +Run script in terminal, show figure
353 +)))
354 +
238 238  (% class="wikigeneratedid" id="HSummary28Inthistutorial2CyouhavelearnedtodoX202629" %)
239 239  (% class="small" %)**Summary (In this tutorial, you have learned to do X…)**
240 240