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Last modified by galluzziandrea on 2022/06/20 12:33
From version 7.1
edited by galluzziandrea
on 2021/12/09 14:49
Change comment: There is no comment for this version
To version 28.1
edited by mattia
on 2022/01/28 13:39
Change comment: There is no comment for this version

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1 -XWiki.galluzziandrea
1 +XWiki.mattia
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235 235  # [.....],[],...]
236 236  {{/code}}
237 237  
238 +=== [[image:image-20220127173048-1.png||height="508" width="948"]] ===
238 238  
240 +=== Defining general and nest.kernel parameters ===
239 239  
242 +{{code language="python"}}
243 +#############################------------------------------------------------------------------------
244 +#Clean the Network
245 +#############################------------------------------------------------------------------------
246 +nest.ResetKernel()
240 240  
241 -=== Results ===
248 +#############################------------------------------------------------------------------------
249 +#insert the introductory parameters of the simulation
250 +#############################------------------------------------------------------------------------
242 242  
252 +
253 +dt = 0.1 # the resolution in ms
254 +StartMisure=0. # start time of measurements
255 +simtime = int(float(InfoPerseo[3])) # Simulation time in ms (200 s)
256 +if simtime<=StartMisure: # If the simulation time is less than StartMisure, it is increased by StartMisure
257 + simtime=simtime+StartMisure
258 +start=0.0 # start time of poissonian processes
259 +origin=0.0 # temporal origin
260 +
261 +#############################------------------------------------------------------------------------
262 +# Kernel parameters
263 +#############################------------------------------------------------------------------------
264 +LNT=multiprocessing.cpu_count();
265 +nest.SetKernelStatus({"local_num_threads": LNT})
266 +nest.SetKernelStatus({"resolution": dt, "print_time": True,
267 + "overwrite_files": True})
268 +
269 +#############################------------------------------------------------------------------------
270 +#"randomize" the seeds of the random generators
271 +#############################------------------------------------------------------------------------
272 +
273 +#msd = int(math.fabs(time.process_time()*1000))
274 +#N_vp = nest.GetKernelStatus(['total_num_virtual_procs'])[0]
275 +#pyrngs = [numpy.random.RandomState(s) for s in range(msd, msd+N_vp)]
276 +#nest.SetKernelStatus({"grng_seed" : msd+N_vp})
277 +#nest.SetKernelStatus({"rng_seeds" : list(range(msd+N_vp+1, msd+2*N_vp+1))})
278 +{{/code}}
279 +
280 +=== Building the network: neuronal populations , Poisson processes and spike detectors ===
281 +
282 +{{code language="python"}}
283 +#############################------------------------------------------------------------------------
284 +print("Building network")
285 +#############################------------------------------------------------------------------------
286 +
287 +startbuild = time.time() #initialize the calculation of the time used to simulate
288 +
289 +NeuronPop=[]
290 +NoisePop=[]
291 +DetectorPop=[]
292 +
293 +#define and initialize the populations of neurons with the parameters extracted from the.ini files
294 +for i in range(1,int(InfoBuild[0])+1):
295 + if int(InfoBuild[i][7])==0:
296 + app=float(InfoBuild[i][5])
297 + else:
298 + app=0.
299 + app2= nest.Create("aeif_psc_exp", int(InfoBuild[i][0]),params={"C_m": 1.0,
300 + "g_L": 1.0/float(InfoBuild[i][3]),
301 + "t_ref": float(InfoBuild[i][6]),
302 + "E_L": 0.0,
303 + "V_reset": float(InfoBuild[i][5]),
304 + "V_m": app,
305 + "V_th": float(InfoBuild[i][4]),
306 + "Delta_T": 0.,
307 + "tau_syn_ex": 1.0,
308 + "tau_syn_in": 1.0,
309 + "a": 0.0,
310 + "b": float(InfoBuild[i][10]),
311 + "tau_w": float(InfoBuild[i][9]),
312 + "V_peak":float(InfoBuild[i][4])+10.0})
313 + NeuronPop.append(app2)
314 +
315 +#define and initialize the poisson generators and the spike detectors with the parameters extracted from the.ini files
316 +
317 +for i in range(1,int(InfoBuild[0])+1):
318 + app3= nest.Create("poisson_generator",params={"rate": float(InfoBuild[i][1]*InfoBuild[i][2]),
319 + 'origin':0.,
320 + 'start':start})
321 + NoisePop.append(app3)
322 + app4 = nest.Create("spike_recorder",params={ "start":StartMisure})
323 + DetectorPop.append(app4)
324 +
325 +endbuild = time.time()
326 +{{/code}}
327 +
328 +=== [[image:image-20220127165908-2.png||height="659" width="1149"]] ===
329 +
330 +=== Connecting the network nodes: neuronal populations, Poisson processes and spike detectors ===
331 +
332 +{{code language="python"}}
333 +#############################------------------------------------------------------------------------
334 +print("Connecting ")
335 +#############################------------------------------------------------------------------------
336 +
337 +startconnect = time.time()
338 +Connessioni=[]
339 +Medie=[]
340 +
341 +#create and define the connections between the populations of neurons and the poisson generators
342 +#and between the populations of neurons and the spike detectors with the parameters extracted from the.ini files
343 +
344 +for i in range(0,int(InfoBuild[0])):
345 + nest.Connect(NoisePop[i], NeuronPop[i], syn_spec={'synapse_model': 'static_synapse_hpc',
346 + 'delay': dt,
347 + 'weight': nest.math.redraw(nest.random.normal(mean=float(InfoConnectNoise[i+1][0]),
348 + std=(float(InfoConnectNoise[i+1][1])*float(InfoConnectNoise[i+1][0]))),
349 + min=0., max=float('Inf'))
350 + })
351 + nest.Connect(NeuronPop[i][:int(InfoBuild[i+1][0])], DetectorPop[i], syn_spec={"weight": 1.0, "delay": dt})
352 +
353 +#create and define the connections between the populations of neurons with the parameters extracted from the.ini files
354 +
355 +for i in range(0,len(InfoConnectPop[1:])):
356 +
357 + conn=nest.Connect(NeuronPop[int(InfoConnectPop[i+1][1])], NeuronPop[int(InfoConnectPop[i+1][0])],
358 + {'rule': 'pairwise_bernoulli',
359 + 'p':float(InfoConnectPop[i+1][2]) },
360 + syn_spec={'synapse_model': 'static_synapse_hpc',
361 + 'delay':nest.math.redraw(nest.random.exponential(beta=float(1./(2.99573227355/(float(InfoConnectPop[i+1][4])-float(InfoConnectPop[i+1][3]))))),
362 + min= numpy.max([dt,float(1./float(InfoConnectPop[i+1][4]))]),
363 + max= float(1./(float(InfoConnectPop[i+1][3])-dt/2))),
364 +
365 + 'weight':nest.random.normal(mean=float(InfoConnectPop[i+1][6]),
366 + std=math.fabs(float(InfoConnectPop[i+1][6])*float(InfoConnectPop[i+1][7])))})
367 +
368 +
369 +endconnect = time.time()
370 +{{/code}}
371 +
372 +=== ===
373 +
374 +=== ===
375 +
376 +=== [[image:image-20220127170722-1.png]] ===
377 +
378 +=== Simulating: neuronal time evolution. ===
379 +
380 +=== ===
381 +
382 +{{code language="python"}}
383 + #############################------------------------------------------------------------------------
384 + print("Simulating")
385 + #############################------------------------------------------------------------------------
386 + ###################################################################################################################################################################
387 + if Salva:
388 + print("I m going to save the data")
389 + #x=str(iterazioni)
390 + f = open(FileName,"w")
391 + if len(InfoProtocol):
392 + print("I m going to split the simulation")
393 + tempo=0
394 + for contatore in range(0,len(InfoProtocol)):
395 + appoggio1=int((tempo+InfoProtocol[contatore][0])/1000.)
396 + appoggio2=int(tempo/1000.)
397 + appoggio3=tempo+InfoProtocol[contatore][0]
398 + if (appoggio1-appoggio2)>=1:
399 + T1=(1+appoggio2)*1000-tempo
400 + nest.Simulate(T1)
401 + #Save the Data!!!!
402 + ###########################################################
403 + Equilibri=[]
404 + for i in range(0,int(InfoBuild[0])):
405 + Equilibri.append([])
406 + a=nest.GetStatus(DetectorPop[i])[0]["events"]["times"]
407 + if len(a)>0:
408 + Trange=(1000*int(numpy.min(a)/1000.),1000*int(numpy.min(a)/1000.)+1000)
409 + hist,Tbin=numpy.histogram(a,200,(Trange[0],Trange[1]))
410 + Equilibri[i]=hist*1000./(5.*int(InfoBuild[i+1][0]))
411 + else:
412 + Trange=(1000*int(tempo/1000.),1000*int(tempo/1000.)+1000)
413 + hist=numpy.zeros(200)
414 + Tbin=numpy.linspace(Trange[0],Trange[1],num=201)
415 + Equilibri[i]=hist
416 + nest.SetStatus(DetectorPop[i],{'n_events':0})
417 + for j in range(0,len(hist)):
418 + f.write(str(Tbin[j])+" ")
419 + for i in range(0,int(InfoBuild[0])):
420 + f.write(str(Equilibri[i][j])+" ")
421 + f.write("\n ")
422 + ###########################################################
423 + tempo=tempo+T1
424 + for contatore2 in range(1,(appoggio1-appoggio2)):
425 + nest.Simulate(1000.)
426 + #Save the Data!!!!
427 + ###########################################################
428 + Equilibri=[]
429 + for i in range(0,int(InfoBuild[0])):
430 + Equilibri.append([])
431 + a=nest.GetStatus(DetectorPop[i])[0]["events"]["times"]
432 + if len(a)>0:
433 + Trange=(1000*int(numpy.min(a)/1000.),1000*int(numpy.min(a)/1000.)+1000)
434 + hist,Tbin=numpy.histogram(a,200,(Trange[0],Trange[1]))
435 + Equilibri[i]=hist*1000./(5.*int(InfoBuild[i+1][0]))
436 + else:
437 + Trange=(1000*int(tempo/1000.),1000*int(tempo/1000.)+1000)
438 + hist=numpy.zeros(200)
439 + Tbin=numpy.linspace(Trange[0],Trange[1],num=201)
440 + Equilibri[i]=hist
441 + nest.SetStatus(DetectorPop[i],{'n_events':0})
442 + for j in range(0,len(hist)):
443 + f.write(str(Tbin[j])+" ")
444 + for i in range(0,int(InfoBuild[0])):
445 + f.write(str(Equilibri[i][j])+" ")
446 + f.write("\n ")
447 + tempo=tempo+1000.
448 + T2=appoggio3-tempo
449 + nest.Simulate(T2);
450 + tempo=tempo+T2;
451 + else:
452 + nest.Simulate(InfoProtocol[contatore][0])
453 + temp=InfoProtocol[contatore][0]
454 + tempo=tempo+temp
455 + if InfoProtocol[contatore][2]==4:
456 + nest.SetStatus(NoisePop[InfoProtocol[contatore][1]],params={"rate": float(InfoBuild[1+InfoProtocol[contatore][1]][2]*InfoProtocol[contatore][3])})
457 + if InfoProtocol[contatore][2]==12:
458 + nest.SetStatus(NeuronPop[InfoProtocol[contatore][1]], params={"b": float(InfoProtocol[contatore][3])})
459 + else:
460 + nest.Simulate(simtime)
461 + tempo=simtime
462 + if (simtime-tempo)>0.:
463 + nest.Simulate(simtime-tempo)
464 +
465 +
466 + endsimulate = time.time()
467 + f.close()
468 + else:
469 + if len(InfoProtocol):
470 + tempo=0
471 + for contatore in range(0,len(InfoProtocol)):
472 + nest.Simulate(InfoProtocol[contatore][0])
473 + temp=InfoProtocol[contatore][0]
474 + tempo=tempo+temp
475 + if InfoProtocol[contatore][2]==4:
476 + nest.SetStatus(NoisePop[InfoProtocol[contatore][1]],params={"rate": float(InfoBuild[1+InfoProtocol[contatore][1]][2]*InfoProtocol[contatore][3])})
477 + #print "Population:", InfoProtocol[contatore][1] ,";Parameter:", InfoProtocol[contatore][2] ,"; Value: ",InfoProtocol[contatore][3]
478 + if InfoProtocol[contatore][2]==12:
479 + nest.SetStatus(NeuronPop[InfoProtocol[contatore][1]], params={"b": float(InfoProtocol[contatore][3])})
480 + #print "Population:", InfoProtocol[contatore][1] ,";Parameter:", InfoProtocol[contatore][2] ,"; Value: ",InfoProtocol[contatore][3]
481 +
482 + else:
483 + nest.Simulate(simtime)
484 + tempo=simtime
485 + if (simtime-tempo)>0.:
486 + nest.Simulate(simtime-tempo)
487 + endsimulate = time.time()
488 +
489 +
490 + ###################################################################################################################################################################
491 +
492 + #############################------------------------------------------------------------------------
493 + #print some information from the simulation
494 + #############################------------------------------------------------------------------------
495 +
496 + num_synapses = nest.GetDefaults('static_synapse_hpc')["num_connections"]
497 + build_time = endbuild - startbuild
498 + connect_time = endconnect - startconnect
499 + sim_time = endsimulate - endconnect
500 +
501 + N_neurons=0
502 + for i in range(0,int(InfoBuild[0])):
503 + N_neurons=N_neurons+int(InfoBuild[i+1][0])
504 +
505 + print(" Network simulation (Python) neuron type:",InfoPerseo[0])
506 + print("Number of neurons : {0}".format(N_neurons))
507 + print("Number of synapses: {0}".format(num_synapses))
508 + print("Building time : %.2f s" % build_time)
509 + print("Connecting time : %.2f s" % connect_time)
510 + print("Simulation time : %.2f s" % sim_time)
511 +
512 +Fine=time.time()
513 +print ("Total Simulation time : %.2f s" % (Fine-Inizio))
514 +{{/code}}
515 +
516 +=== ===
517 +
518 +=== ===
519 +
520 +=== [[image:image-20220127171242-1.png]] ===
521 +
522 +=== Results: ===
523 +
524 +[[the output of this simulation is...>>https://drive.ebrains.eu/smart-link/215f8213-17e3-468b-b573-e6eaf49d315e/]]
525 +
526 +
527 +
528 +
529 +
530 +
243 243  ==== ====
CorticalField_t=80_DeepSpontPlanar.mp4
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