Last modified by adavison on 2022/10/04 13:55
From version 13.1
edited by adavison
on 2021/09/30 14:24
Change comment: There is no comment for this version
To version 11.4
edited by adavison
on 2021/09/30 14:18
Change comment: There is no comment for this version

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... ... @@ -233,7 +233,8 @@
233 233   ),
234 234   title="Response of first five neurons with heterogeneous parameters",
235 235   annotations="Simulated with NEST"
236 -).show()
236 +).show()(%%)
237 +\\**Run script in terminal, show figure**
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.
... ... @@ -310,54 +310,10 @@
310 310   ),
311 311   title="Response of first five neurons with heterogeneous parameters",
312 312   annotations="Simulated with NEST"
313 -).show()
314 +).show()(%%)
315 +\\**Run script in terminal, show figure**
314 314  )))
315 315  
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:#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)(%%)
340 -(% style="color:#000000" %)sim.run(100.0)(%%)
341 -(% style="color:#e74c3c" %)data1_v(% style="color:#000000" %) = population1.get_data().segments[0].filter(name='v')[0](%%)
342 -(% style="color:#e74c3c" %)data2_v = population2.get_data().segments[0].filter(name='v')[0](%%)
343 -(% style="color:#000000" %)Figure(
344 - Panel(
345 - (% style="color:#e74c3c" %)data1_v(% style="color:#000000" %)[:, 0:5],
346 - xticks=True, (% style="color:#e74c3c" %)--xlabel="Time (ms)",--(%%)
347 -(% style="color:#000000" %) yticks=True, ylabel="Membrane potential (mV)"
348 - ),
349 - (% style="color:#e74c3c" %)Panel(
350 - data2_v[:, 0:5],
351 - xticks=True, xlabel="Time (ms)",
352 - yticks=True"
353 - ),(%%)
354 -(% style="color:#000000" %) title="Response of (% style="color:#e74c3c" %)simple network(% style="color:#000000" %)",
355 - annotations="Simulated with NEST"
356 -).show()
357 -
358 -**Run script in terminal, show figure**
359 -)))
360 -
361 361  (% class="wikigeneratedid" id="HSummary28Inthistutorial2CyouhavelearnedtodoX202629" %)
362 362  (% class="small" %)**Summary (In this tutorial, you have learned to do X…)**
363 363