Last modified by adavison on 2022/10/04 13:55
From version 11.4
edited by adavison
on 2021/09/30 14:18
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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... ... @@ -233,8 +233,7 @@
233 233   ),
234 234   title="Response of first five neurons with heterogeneous parameters",
235 235   annotations="Simulated with NEST"
236 -).show()(%%)
237 -\\**Run script in terminal, show figure**
236 +).show()
238 238  )))
239 239  
240 240  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.
... ... @@ -311,10 +311,48 @@
311 311   ),
312 312   title="Response of first five neurons with heterogeneous parameters",
313 313   annotations="Simulated with NEST"
314 -).show()(%%)
315 -\\**Run script in terminal, show figure**
313 +).show()
316 316  )))
317 317  
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 +
318 318  (% class="wikigeneratedid" id="HSummary28Inthistutorial2CyouhavelearnedtodoX202629" %)
319 319  (% class="small" %)**Summary (In this tutorial, you have learned to do X…)**
320 320