Last modified by adavison on 2022/10/04 13:55
From version 13.2
edited by adavison
on 2021/09/30 14:31
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To version 13.1
edited by adavison
on 2021/09/30 14:24
Change comment: There is no comment for this version

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... ... @@ -358,54 +358,6 @@
358 358  **Run script in terminal, show figure**
359 359  )))
360 360  
361 -(% class="wikigeneratedid" %)
362 -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.
363 -
364 -(% class="box infomessage" %)
365 -(((
366 -**Screencast** - current state of editor
367 -\\(% style="color:#000000" %)"""Simple network model using PyNN"""
368 -\\import pyNN.(% style="color:#e74c3c" %)neuron(% style="color:#000000" %) as sim(%%)
369 -(% style="color:#000000" %)from pyNN.utility.plotting import Figure, Panel(%%)
370 -(% style="color:#000000" %)from pyNN.random import RandomDistribution(%%)
371 -(% style="color:#000000" %)sim.setup(timestep=0.1)(%%)
372 -(% style="color:#000000" %)cell_type  = sim.IF_curr_exp(
373 - (% style="color:#e74c3c" %) (% style="color:#000000" %)v_rest=RandomDistribution('normal', {'mu': -65.0, 'sigma': 1.0}),
374 - v_thresh=RandomDistribution('normal', {'mu': -55.0, 'sigma': 1.0}),
375 - v_reset=RandomDistribution('normal', {'mu': -65.0, 'sigma': 1.0}), (%%)
376 -(% style="color:#000000" %) t_refrac=1, tau_m=10, cm=1, i_offset=0.1)(%%)
377 -(% style="color:#000000" %)population1 = sim.Population(100, cell_type, label="Population 1")(%%)
378 -(% style="color:#000000" %)population2 = sim.Population(100, cell_type, label="Population 2")
379 -population2.set(i_offset=0)
380 -population1.record("v")
381 -population2.record("v")(%%)
382 -(% style="color:#000000" %)connection_algorithm = sim.FixedProbabilityConnector(p=0.5)
383 -synapse_type = sim.StaticSynapse(weight=0.5, delay=0.5)
384 -connections = sim.Projection(population1, population2, connection_algorithm, synapse_type)(%%)
385 -(% style="color:#000000" %)sim.run(100.0)(%%)
386 -(% style="color:#000000" %)data1_v = population1.get_data().segments[0].filter(name='v')[0]
387 -data2_v = population2.get_data().segments[0].filter(name='v')[0]
388 -Figure(
389 - Panel(
390 - data1_v[:, 0:5],
391 - xticks=True,
392 - yticks=True, ylabel="Membrane potential (mV)"
393 - ),
394 - Panel(
395 - data2_v[:, 0:5],
396 - xticks=True, xlabel="Time (ms)",
397 - yticks=True"
398 - ),(%%)
399 -(% style="color:#000000" %) title="Response of simple network",
400 - annotations="Simulated with (% style="color:#e74c3c" %)NEURON(% style="color:#000000" %)"
401 -).show()
402 -
403 -**Run script in terminal, show figure**
404 -)))
405 -
406 -(% class="wikigeneratedid" %)
407 -As you would hope, NEST and NEURON give essentially identical results.
408 -
409 409  (% class="wikigeneratedid" id="HSummary28Inthistutorial2CyouhavelearnedtodoX202629" %)
410 410  (% class="small" %)**Summary (In this tutorial, you have learned to do X…)**
411 411