Changes for page 03. Building and simulating a simple model
Last modified by adavison on 2022/10/04 13:55
Summary
-
Page properties (1 modified, 0 added, 0 removed)
Details
- Page properties
-
- Content
-
... ... @@ -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. ... ... @@ -310,120 +310,26 @@ 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 updateourfigure,byadding a secondpanelto show theesponsesofPopulation2.318 +(% class="wikigeneratedid" id="HSummary28Inthistutorial2CyouhavelearnedtodoX202629" %) 319 +(% class="small" %)**Summary (In this tutorial, you have learned to do X…)** 318 318 319 -(% 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() 321 +. 357 357 358 - **Runscriptinterminal,showfigure**359 -) ))323 +(% class="wikigeneratedid" id="HAcknowledgementsifappropriate" %) 324 +(% class="small" %)**Acknowledgements if appropriate** 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. 326 +. 363 363 364 -(% 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() 328 +(% class="wikigeneratedid" id="HReferencestowebsites28Formoreinformation2Cvisitusat202629" %) 329 +(% class="small" %)**References to websites (For more information, visit us at…)** 402 402 403 -**Run script in terminal, show figure** 404 -))) 331 +. 405 405 406 -(% class="wikigeneratedid" %) 407 - Asyou wouldhope,NESTandNEURONgiveessentiallyidenticalresults.333 +(% class="wikigeneratedid" id="HContactinformation28Forquestions2Ccontactusat202629" %) 334 +(% class="small" %)**Contact information (For questions, contact us at…)** 408 408 409 -(% class="box successmessage" %) 410 -((( 411 -**Slide** recap of learning objectives 412 -))) 413 - 414 -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. 415 - 416 -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. 417 - 418 -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. 419 - 420 -(% class="box successmessage" %) 421 -((( 422 -**Slide** acknowledgements, contact information 423 -))) 424 - 425 -(% class="wikigeneratedid" %) 426 -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. 427 - 428 -(% class="wikigeneratedid" %) 429 -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. 336 +.