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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 27.1
edited by galluzziandrea
on 2022/01/27 17:31
Change comment: There is no comment for this version

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... ... @@ -235,9 +235,298 @@
235 235  # [.....],[],...]
236 236  {{/code}}
237 237  
238 +(% class="wikigeneratedid" %)
239 +=== [[image:image-20220127173048-1.png||height="508" width="948"]] ===
238 238  
241 +=== Defining general and nest.kernel parameters ===
239 239  
243 +{{code language="python"}}
244 +#############################------------------------------------------------------------------------
245 +#Clean the Network
246 +#############################------------------------------------------------------------------------
247 +nest.ResetKernel()
240 240  
241 -=== Results ===
249 +#############################------------------------------------------------------------------------
250 +#insert the introductory parameters of the simulation
251 +#############################------------------------------------------------------------------------
242 242  
253 +
254 +dt = 0.1 # the resolution in ms
255 +StartMisure=0. # start time of measurements
256 +simtime = int(float(InfoPerseo[3])) # Simulation time in ms (200 s)
257 +if simtime<=StartMisure: # If the simulation time is less than StartMisure, it is increased by StartMisure
258 + simtime=simtime+StartMisure
259 +start=0.0 # start time of poissonian processes
260 +origin=0.0 # temporal origin
261 +
262 +#############################------------------------------------------------------------------------
263 +# Kernel parameters
264 +#############################------------------------------------------------------------------------
265 +LNT=multiprocessing.cpu_count();
266 +nest.SetKernelStatus({"local_num_threads": LNT})
267 +nest.SetKernelStatus({"resolution": dt, "print_time": True,
268 + "overwrite_files": True})
269 +
270 +#############################------------------------------------------------------------------------
271 +#"randomize" the seeds of the random generators
272 +#############################------------------------------------------------------------------------
273 +
274 +#msd = int(math.fabs(time.process_time()*1000))
275 +#N_vp = nest.GetKernelStatus(['total_num_virtual_procs'])[0]
276 +#pyrngs = [numpy.random.RandomState(s) for s in range(msd, msd+N_vp)]
277 +#nest.SetKernelStatus({"grng_seed" : msd+N_vp})
278 +#nest.SetKernelStatus({"rng_seeds" : list(range(msd+N_vp+1, msd+2*N_vp+1))})
279 +{{/code}}
280 +
281 +=== Building the network: neuronal populations , Poisson processes and spike detectors ===
282 +
283 +{{code language="python"}}
284 +#############################------------------------------------------------------------------------
285 +print("Building network")
286 +#############################------------------------------------------------------------------------
287 +
288 +startbuild = time.time() #initialize the calculation of the time used to simulate
289 +
290 +NeuronPop=[]
291 +NoisePop=[]
292 +DetectorPop=[]
293 +
294 +#define and initialize the populations of neurons with the parameters extracted from the.ini files
295 +for i in range(1,int(InfoBuild[0])+1):
296 + if int(InfoBuild[i][7])==0:
297 + app=float(InfoBuild[i][5])
298 + else:
299 + app=0.
300 + app2= nest.Create("aeif_psc_exp", int(InfoBuild[i][0]),params={"C_m": 1.0,
301 + "g_L": 1.0/float(InfoBuild[i][3]),
302 + "t_ref": float(InfoBuild[i][6]),
303 + "E_L": 0.0,
304 + "V_reset": float(InfoBuild[i][5]),
305 + "V_m": app,
306 + "V_th": float(InfoBuild[i][4]),
307 + "Delta_T": 0.,
308 + "tau_syn_ex": 1.0,
309 + "tau_syn_in": 1.0,
310 + "a": 0.0,
311 + "b": float(InfoBuild[i][10]),
312 + "tau_w": float(InfoBuild[i][9]),
313 + "V_peak":float(InfoBuild[i][4])+10.0})
314 + NeuronPop.append(app2)
315 +
316 +#define and initialize the poisson generators and the spike detectors with the parameters extracted from the.ini files
317 +
318 +for i in range(1,int(InfoBuild[0])+1):
319 + app3= nest.Create("poisson_generator",params={"rate": float(InfoBuild[i][1]*InfoBuild[i][2]),
320 + 'origin':0.,
321 + 'start':start})
322 + NoisePop.append(app3)
323 + app4 = nest.Create("spike_recorder",params={ "start":StartMisure})
324 + DetectorPop.append(app4)
325 +
326 +endbuild = time.time()
327 +{{/code}}
328 +
329 +=== [[image:image-20220127165908-2.png||height="659" width="1149"]] ===
330 +
331 +=== Connecting the network nodes: neuronal populations, Poisson processes and spike detectors ===
332 +
333 +{{code language="python"}}
334 +#############################------------------------------------------------------------------------
335 +print("Connecting ")
336 +#############################------------------------------------------------------------------------
337 +
338 +startconnect = time.time()
339 +Connessioni=[]
340 +Medie=[]
341 +
342 +#create and define the connections between the populations of neurons and the poisson generators
343 +#and between the populations of neurons and the spike detectors with the parameters extracted from the.ini files
344 +
345 +for i in range(0,int(InfoBuild[0])):
346 + nest.Connect(NoisePop[i], NeuronPop[i], syn_spec={'synapse_model': 'static_synapse_hpc',
347 + 'delay': dt,
348 + 'weight': nest.math.redraw(nest.random.normal(mean=float(InfoConnectNoise[i+1][0]),
349 + std=(float(InfoConnectNoise[i+1][1])*float(InfoConnectNoise[i+1][0]))),
350 + min=0., max=float('Inf'))
351 + })
352 + nest.Connect(NeuronPop[i][:int(InfoBuild[i+1][0])], DetectorPop[i], syn_spec={"weight": 1.0, "delay": dt})
353 +
354 +#create and define the connections between the populations of neurons with the parameters extracted from the.ini files
355 +
356 +for i in range(0,len(InfoConnectPop[1:])):
357 +
358 + conn=nest.Connect(NeuronPop[int(InfoConnectPop[i+1][1])], NeuronPop[int(InfoConnectPop[i+1][0])],
359 + {'rule': 'pairwise_bernoulli',
360 + 'p':float(InfoConnectPop[i+1][2]) },
361 + syn_spec={'synapse_model': 'static_synapse_hpc',
362 + 'delay':nest.math.redraw(nest.random.exponential(beta=float(1./(2.99573227355/(float(InfoConnectPop[i+1][4])-float(InfoConnectPop[i+1][3]))))),
363 + min= numpy.max([dt,float(1./float(InfoConnectPop[i+1][4]))]),
364 + max= float(1./(float(InfoConnectPop[i+1][3])-dt/2))),
365 +
366 + 'weight':nest.random.normal(mean=float(InfoConnectPop[i+1][6]),
367 + std=math.fabs(float(InfoConnectPop[i+1][6])*float(InfoConnectPop[i+1][7])))})
368 +
369 +
370 +endconnect = time.time()
371 +{{/code}}
372 +
373 +=== ===
374 +
375 +=== ===
376 +
377 +=== [[image:image-20220127170722-1.png]] ===
378 +
379 +=== Simulating: neuronal time evolution. ===
380 +
381 +=== ===
382 +
383 +{{code language="python"}}
384 + #############################------------------------------------------------------------------------
385 + print("Simulating")
386 + #############################------------------------------------------------------------------------
387 + ###################################################################################################################################################################
388 + if Salva:
389 + print("I m going to save the data")
390 + #x=str(iterazioni)
391 + f = open(FileName,"w")
392 + if len(InfoProtocol):
393 + print("I m going to split the simulation")
394 + tempo=0
395 + for contatore in range(0,len(InfoProtocol)):
396 + appoggio1=int((tempo+InfoProtocol[contatore][0])/1000.)
397 + appoggio2=int(tempo/1000.)
398 + appoggio3=tempo+InfoProtocol[contatore][0]
399 + if (appoggio1-appoggio2)>=1:
400 + T1=(1+appoggio2)*1000-tempo
401 + nest.Simulate(T1)
402 + #Save the Data!!!!
403 + ###########################################################
404 + Equilibri=[]
405 + for i in range(0,int(InfoBuild[0])):
406 + Equilibri.append([])
407 + a=nest.GetStatus(DetectorPop[i])[0]["events"]["times"]
408 + if len(a)>0:
409 + Trange=(1000*int(numpy.min(a)/1000.),1000*int(numpy.min(a)/1000.)+1000)
410 + hist,Tbin=numpy.histogram(a,200,(Trange[0],Trange[1]))
411 + Equilibri[i]=hist*1000./(5.*int(InfoBuild[i+1][0]))
412 + else:
413 + Trange=(1000*int(tempo/1000.),1000*int(tempo/1000.)+1000)
414 + hist=numpy.zeros(200)
415 + Tbin=numpy.linspace(Trange[0],Trange[1],num=201)
416 + Equilibri[i]=hist
417 + nest.SetStatus(DetectorPop[i],{'n_events':0})
418 + for j in range(0,len(hist)):
419 + f.write(str(Tbin[j])+" ")
420 + for i in range(0,int(InfoBuild[0])):
421 + f.write(str(Equilibri[i][j])+" ")
422 + f.write("\n ")
423 + ###########################################################
424 + tempo=tempo+T1
425 + for contatore2 in range(1,(appoggio1-appoggio2)):
426 + nest.Simulate(1000.)
427 + #Save the Data!!!!
428 + ###########################################################
429 + Equilibri=[]
430 + for i in range(0,int(InfoBuild[0])):
431 + Equilibri.append([])
432 + a=nest.GetStatus(DetectorPop[i])[0]["events"]["times"]
433 + if len(a)>0:
434 + Trange=(1000*int(numpy.min(a)/1000.),1000*int(numpy.min(a)/1000.)+1000)
435 + hist,Tbin=numpy.histogram(a,200,(Trange[0],Trange[1]))
436 + Equilibri[i]=hist*1000./(5.*int(InfoBuild[i+1][0]))
437 + else:
438 + Trange=(1000*int(tempo/1000.),1000*int(tempo/1000.)+1000)
439 + hist=numpy.zeros(200)
440 + Tbin=numpy.linspace(Trange[0],Trange[1],num=201)
441 + Equilibri[i]=hist
442 + nest.SetStatus(DetectorPop[i],{'n_events':0})
443 + for j in range(0,len(hist)):
444 + f.write(str(Tbin[j])+" ")
445 + for i in range(0,int(InfoBuild[0])):
446 + f.write(str(Equilibri[i][j])+" ")
447 + f.write("\n ")
448 + tempo=tempo+1000.
449 + T2=appoggio3-tempo
450 + nest.Simulate(T2);
451 + tempo=tempo+T2;
452 + else:
453 + nest.Simulate(InfoProtocol[contatore][0])
454 + temp=InfoProtocol[contatore][0]
455 + tempo=tempo+temp
456 + if InfoProtocol[contatore][2]==4:
457 + nest.SetStatus(NoisePop[InfoProtocol[contatore][1]],params={"rate": float(InfoBuild[1+InfoProtocol[contatore][1]][2]*InfoProtocol[contatore][3])})
458 + if InfoProtocol[contatore][2]==12:
459 + nest.SetStatus(NeuronPop[InfoProtocol[contatore][1]], params={"b": float(InfoProtocol[contatore][3])})
460 + else:
461 + nest.Simulate(simtime)
462 + tempo=simtime
463 + if (simtime-tempo)>0.:
464 + nest.Simulate(simtime-tempo)
465 +
466 +
467 + endsimulate = time.time()
468 + f.close()
469 + else:
470 + if len(InfoProtocol):
471 + tempo=0
472 + for contatore in range(0,len(InfoProtocol)):
473 + nest.Simulate(InfoProtocol[contatore][0])
474 + temp=InfoProtocol[contatore][0]
475 + tempo=tempo+temp
476 + if InfoProtocol[contatore][2]==4:
477 + nest.SetStatus(NoisePop[InfoProtocol[contatore][1]],params={"rate": float(InfoBuild[1+InfoProtocol[contatore][1]][2]*InfoProtocol[contatore][3])})
478 + #print "Population:", InfoProtocol[contatore][1] ,";Parameter:", InfoProtocol[contatore][2] ,"; Value: ",InfoProtocol[contatore][3]
479 + if InfoProtocol[contatore][2]==12:
480 + nest.SetStatus(NeuronPop[InfoProtocol[contatore][1]], params={"b": float(InfoProtocol[contatore][3])})
481 + #print "Population:", InfoProtocol[contatore][1] ,";Parameter:", InfoProtocol[contatore][2] ,"; Value: ",InfoProtocol[contatore][3]
482 +
483 + else:
484 + nest.Simulate(simtime)
485 + tempo=simtime
486 + if (simtime-tempo)>0.:
487 + nest.Simulate(simtime-tempo)
488 + endsimulate = time.time()
489 +
490 +
491 + ###################################################################################################################################################################
492 +
493 + #############################------------------------------------------------------------------------
494 + #print some information from the simulation
495 + #############################------------------------------------------------------------------------
496 +
497 + num_synapses = nest.GetDefaults('static_synapse_hpc')["num_connections"]
498 + build_time = endbuild - startbuild
499 + connect_time = endconnect - startconnect
500 + sim_time = endsimulate - endconnect
501 +
502 + N_neurons=0
503 + for i in range(0,int(InfoBuild[0])):
504 + N_neurons=N_neurons+int(InfoBuild[i+1][0])
505 +
506 + print(" Network simulation (Python) neuron type:",InfoPerseo[0])
507 + print("Number of neurons : {0}".format(N_neurons))
508 + print("Number of synapses: {0}".format(num_synapses))
509 + print("Building time : %.2f s" % build_time)
510 + print("Connecting time : %.2f s" % connect_time)
511 + print("Simulation time : %.2f s" % sim_time)
512 +
513 +Fine=time.time()
514 +print ("Total Simulation time : %.2f s" % (Fine-Inizio))
515 +{{/code}}
516 +
517 +=== ===
518 +
519 +=== ===
520 +
521 +=== [[image:image-20220127171242-1.png]] ===
522 +
523 +=== Results: ===
524 +
525 +[[the output of this simulationo is...>>https://drive.ebrains.eu/smart-link/215f8213-17e3-468b-b573-e6eaf49d315e/]]
526 +
527 +
528 +
529 +
530 +
531 +
243 243  ==== ====
CorticalField_t=80_DeepSpontPlanar.mp4
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