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Last modified by galluzziandrea on 2022/06/20 12:33
From version 6.1
edited by galluzziandrea
on 2021/12/09 14:47
Change comment: There is no comment for this version
To version 18.1
edited by galluzziandrea
on 2022/01/27 17:07
Change comment: There is no comment for this version

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... ... @@ -36,8 +36,7 @@
36 36  
37 37  === Define necessary classes to import the Initialization Files: ===
38 38  
39 -{{code language="python" width="90%" title="
40 -120%"}}
39 +{{code language="python" title=" "}}
41 41  class ImportIniLIFCA():
42 42   #initialize the information to look for in perseo.ini
43 43   inf=["NeuronType", #still fixed value
... ... @@ -236,9 +236,300 @@
236 236  # [.....],[],...]
237 237  {{/code}}
238 238  
238 +=== Defining general and nest.kernel parameters ===
239 239  
240 +{{code language="python"}}
241 +#############################------------------------------------------------------------------------
242 +#Clean the Network
243 +#############################------------------------------------------------------------------------
244 +nest.ResetKernel()
240 240  
246 +#############################------------------------------------------------------------------------
247 +#insert the introductory parameters of the simulation
248 +#############################------------------------------------------------------------------------
241 241  
242 -=== Results ===
243 243  
251 +dt = 0.1 # the resolution in ms
252 +StartMisure=0. # start time of measurements
253 +simtime = int(float(InfoPerseo[3])) # Simulation time in ms (200 s)
254 +if simtime<=StartMisure: # If the simulation time is less than StartMisure, it is increased by StartMisure
255 + simtime=simtime+StartMisure
256 +start=0.0 # start time of poissonian processes
257 +origin=0.0 # temporal origin
258 +
259 +#############################------------------------------------------------------------------------
260 +# Kernel parameters
261 +#############################------------------------------------------------------------------------
262 +LNT=multiprocessing.cpu_count();
263 +nest.SetKernelStatus({"local_num_threads": LNT})
264 +nest.SetKernelStatus({"resolution": dt, "print_time": True,
265 + "overwrite_files": True})
266 +
267 +#############################------------------------------------------------------------------------
268 +#"randomize" the seeds of the random generators
269 +#############################------------------------------------------------------------------------
270 +
271 +#msd = int(math.fabs(time.process_time()*1000))
272 +#N_vp = nest.GetKernelStatus(['total_num_virtual_procs'])[0]
273 +#pyrngs = [numpy.random.RandomState(s) for s in range(msd, msd+N_vp)]
274 +#nest.SetKernelStatus({"grng_seed" : msd+N_vp})
275 +#nest.SetKernelStatus({"rng_seeds" : list(range(msd+N_vp+1, msd+2*N_vp+1))})
276 +{{/code}}
277 +
278 +=== Building the network: neuronal populations , Poisson processes and spike detectors ===
279 +
280 +{{code language="python"}}
281 +#############################------------------------------------------------------------------------
282 +print("Building network")
283 +#############################------------------------------------------------------------------------
284 +
285 +startbuild = time.time() #initialize the calculation of the time used to simulate
286 +
287 +NeuronPop=[]
288 +NoisePop=[]
289 +DetectorPop=[]
290 +
291 +#define and initialize the populations of neurons with the parameters extracted from the.ini files
292 +for i in range(1,int(InfoBuild[0])+1):
293 + if int(InfoBuild[i][7])==0:
294 + app=float(InfoBuild[i][5])
295 + else:
296 + app=0.
297 + app2= nest.Create("aeif_psc_exp", int(InfoBuild[i][0]),params={"C_m": 1.0,
298 + "g_L": 1.0/float(InfoBuild[i][3]),
299 + "t_ref": float(InfoBuild[i][6]),
300 + "E_L": 0.0,
301 + "V_reset": float(InfoBuild[i][5]),
302 + "V_m": app,
303 + "V_th": float(InfoBuild[i][4]),
304 + "Delta_T": 0.,
305 + "tau_syn_ex": 1.0,
306 + "tau_syn_in": 1.0,
307 + "a": 0.0,
308 + "b": float(InfoBuild[i][10]),
309 + "tau_w": float(InfoBuild[i][9]),
310 + "V_peak":float(InfoBuild[i][4])+10.0})
311 + NeuronPop.append(app2)
312 +
313 +#define and initialize the poisson generators and the spike detectors with the parameters extracted from the.ini files
314 +
315 +for i in range(1,int(InfoBuild[0])+1):
316 + app3= nest.Create("poisson_generator",params={"rate": float(InfoBuild[i][1]*InfoBuild[i][2]),
317 + 'origin':0.,
318 + 'start':start})
319 + NoisePop.append(app3)
320 + app4 = nest.Create("spike_recorder",params={ "start":StartMisure})
321 + DetectorPop.append(app4)
322 +
323 +endbuild = time.time()
324 +{{/code}}
325 +
326 +=== [[image:image-20220127165908-2.png||height="659" width="1149"]] ===
327 +
328 +=== Connecting the network nodes: neuronal populations, Poisson processes and spike detectors ===
329 +
330 +{{code language="python"}}
331 +#############################------------------------------------------------------------------------
332 +print("Connecting ")
333 +#############################------------------------------------------------------------------------
334 +
335 +startconnect = time.time()
336 +Connessioni=[]
337 +Medie=[]
338 +
339 +#create and define the connections between the populations of neurons and the poisson generators
340 +#and between the populations of neurons and the spike detectors with the parameters extracted from the.ini files
341 +
342 +for i in range(0,int(InfoBuild[0])):
343 + nest.Connect(NoisePop[i], NeuronPop[i], syn_spec={'synapse_model': 'static_synapse_hpc',
344 + 'delay': dt,
345 + 'weight': nest.math.redraw(nest.random.normal(mean=float(InfoConnectNoise[i+1][0]),
346 + std=(float(InfoConnectNoise[i+1][1])*float(InfoConnectNoise[i+1][0]))),
347 + min=0., max=float('Inf'))
348 + })
349 + nest.Connect(NeuronPop[i][:int(InfoBuild[i+1][0])], DetectorPop[i], syn_spec={"weight": 1.0, "delay": dt})
350 +
351 +#create and define the connections between the populations of neurons with the parameters extracted from the.ini files
352 +
353 +for i in range(0,len(InfoConnectPop[1:])):
354 +
355 + conn=nest.Connect(NeuronPop[int(InfoConnectPop[i+1][1])], NeuronPop[int(InfoConnectPop[i+1][0])],
356 + {'rule': 'pairwise_bernoulli',
357 + 'p':float(InfoConnectPop[i+1][2]) },
358 + syn_spec={'synapse_model': 'static_synapse_hpc',
359 + 'delay':nest.math.redraw(nest.random.exponential(beta=float(1./(2.99573227355/(float(InfoConnectPop[i+1][4])-float(InfoConnectPop[i+1][3]))))),
360 + min= numpy.max([dt,float(1./float(InfoConnectPop[i+1][4]))]),
361 + max= float(1./(float(InfoConnectPop[i+1][3])-dt/2))),
362 +
363 + 'weight':nest.random.normal(mean=float(InfoConnectPop[i+1][6]),
364 + std=math.fabs(float(InfoConnectPop[i+1][6])*float(InfoConnectPop[i+1][7])))})
365 +
366 +
367 +endconnect = time.time()
368 +{{/code}}
369 +
370 +=== ===
371 +
372 +=== ===
373 +
374 +=== ===
375 +
376 +=== ===
377 +
378 +=== ===
379 +
380 +(% class="wikigeneratedid" %)
381 +=== [[image:image-20220127170722-1.png]] ===
382 +
383 +=== Simulating: neuronal time evolution. ===
384 +
385 +=== ===
386 +
387 +{{code language="python"}}
388 + #############################------------------------------------------------------------------------
389 + print("Simulating")
390 + #############################------------------------------------------------------------------------
391 + ###################################################################################################################################################################
392 + if Salva:
393 + print("I m going to save the data")
394 + #x=str(iterazioni)
395 + f = open(FileName,"w")
396 + if len(InfoProtocol):
397 + print("I m going to split the simulation")
398 + tempo=0
399 + for contatore in range(0,len(InfoProtocol)):
400 + appoggio1=int((tempo+InfoProtocol[contatore][0])/1000.)
401 + appoggio2=int(tempo/1000.)
402 + appoggio3=tempo+InfoProtocol[contatore][0]
403 + if (appoggio1-appoggio2)>=1:
404 + T1=(1+appoggio2)*1000-tempo
405 + nest.Simulate(T1)
406 + #Save the Data!!!!
407 + ###########################################################
408 + Equilibri=[]
409 + for i in range(0,int(InfoBuild[0])):
410 + Equilibri.append([])
411 + a=nest.GetStatus(DetectorPop[i])[0]["events"]["times"]
412 + if len(a)>0:
413 + Trange=(1000*int(numpy.min(a)/1000.),1000*int(numpy.min(a)/1000.)+1000)
414 + hist,Tbin=numpy.histogram(a,200,(Trange[0],Trange[1]))
415 + Equilibri[i]=hist*1000./(5.*int(InfoBuild[i+1][0]))
416 + else:
417 + Trange=(1000*int(tempo/1000.),1000*int(tempo/1000.)+1000)
418 + hist=numpy.zeros(200)
419 + Tbin=numpy.linspace(Trange[0],Trange[1],num=201)
420 + Equilibri[i]=hist
421 + nest.SetStatus(DetectorPop[i],{'n_events':0})
422 + for j in range(0,len(hist)):
423 + f.write(str(Tbin[j])+" ")
424 + for i in range(0,int(InfoBuild[0])):
425 + f.write(str(Equilibri[i][j])+" ")
426 + f.write("\n ")
427 + ###########################################################
428 + tempo=tempo+T1
429 + for contatore2 in range(1,(appoggio1-appoggio2)):
430 + nest.Simulate(1000.)
431 + #Save the Data!!!!
432 + ###########################################################
433 + Equilibri=[]
434 + for i in range(0,int(InfoBuild[0])):
435 + Equilibri.append([])
436 + a=nest.GetStatus(DetectorPop[i])[0]["events"]["times"]
437 + if len(a)>0:
438 + Trange=(1000*int(numpy.min(a)/1000.),1000*int(numpy.min(a)/1000.)+1000)
439 + hist,Tbin=numpy.histogram(a,200,(Trange[0],Trange[1]))
440 + Equilibri[i]=hist*1000./(5.*int(InfoBuild[i+1][0]))
441 + else:
442 + Trange=(1000*int(tempo/1000.),1000*int(tempo/1000.)+1000)
443 + hist=numpy.zeros(200)
444 + Tbin=numpy.linspace(Trange[0],Trange[1],num=201)
445 + Equilibri[i]=hist
446 + nest.SetStatus(DetectorPop[i],{'n_events':0})
447 + for j in range(0,len(hist)):
448 + f.write(str(Tbin[j])+" ")
449 + for i in range(0,int(InfoBuild[0])):
450 + f.write(str(Equilibri[i][j])+" ")
451 + f.write("\n ")
452 + tempo=tempo+1000.
453 + T2=appoggio3-tempo
454 + nest.Simulate(T2);
455 + tempo=tempo+T2;
456 + else:
457 + nest.Simulate(InfoProtocol[contatore][0])
458 + temp=InfoProtocol[contatore][0]
459 + tempo=tempo+temp
460 + if InfoProtocol[contatore][2]==4:
461 + nest.SetStatus(NoisePop[InfoProtocol[contatore][1]],params={"rate": float(InfoBuild[1+InfoProtocol[contatore][1]][2]*InfoProtocol[contatore][3])})
462 + if InfoProtocol[contatore][2]==12:
463 + nest.SetStatus(NeuronPop[InfoProtocol[contatore][1]], params={"b": float(InfoProtocol[contatore][3])})
464 + else:
465 + nest.Simulate(simtime)
466 + tempo=simtime
467 + if (simtime-tempo)>0.:
468 + nest.Simulate(simtime-tempo)
469 +
470 +
471 + endsimulate = time.time()
472 + f.close()
473 + else:
474 + if len(InfoProtocol):
475 + tempo=0
476 + for contatore in range(0,len(InfoProtocol)):
477 + nest.Simulate(InfoProtocol[contatore][0])
478 + temp=InfoProtocol[contatore][0]
479 + tempo=tempo+temp
480 + if InfoProtocol[contatore][2]==4:
481 + nest.SetStatus(NoisePop[InfoProtocol[contatore][1]],params={"rate": float(InfoBuild[1+InfoProtocol[contatore][1]][2]*InfoProtocol[contatore][3])})
482 + #print "Population:", InfoProtocol[contatore][1] ,";Parameter:", InfoProtocol[contatore][2] ,"; Value: ",InfoProtocol[contatore][3]
483 + if InfoProtocol[contatore][2]==12:
484 + nest.SetStatus(NeuronPop[InfoProtocol[contatore][1]], params={"b": float(InfoProtocol[contatore][3])})
485 + #print "Population:", InfoProtocol[contatore][1] ,";Parameter:", InfoProtocol[contatore][2] ,"; Value: ",InfoProtocol[contatore][3]
486 +
487 + else:
488 + nest.Simulate(simtime)
489 + tempo=simtime
490 + if (simtime-tempo)>0.:
491 + nest.Simulate(simtime-tempo)
492 + endsimulate = time.time()
493 +
494 +
495 + ###################################################################################################################################################################
496 +
497 + #############################------------------------------------------------------------------------
498 + #print some information from the simulation
499 + #############################------------------------------------------------------------------------
500 +
501 + num_synapses = nest.GetDefaults('static_synapse_hpc')["num_connections"]
502 + build_time = endbuild - startbuild
503 + connect_time = endconnect - startconnect
504 + sim_time = endsimulate - endconnect
505 +
506 + N_neurons=0
507 + for i in range(0,int(InfoBuild[0])):
508 + N_neurons=N_neurons+int(InfoBuild[i+1][0])
509 +
510 + print(" Network simulation (Python) neuron type:",InfoPerseo[0])
511 + print("Number of neurons : {0}".format(N_neurons))
512 + print("Number of synapses: {0}".format(num_synapses))
513 + print("Building time : %.2f s" % build_time)
514 + print("Connecting time : %.2f s" % connect_time)
515 + print("Simulation time : %.2f s" % sim_time)
516 +
517 +Fine=time.time()
518 +print ("Total Simulation time : %.2f s" % (Fine-Inizio))
519 +{{/code}}
520 +
521 +=== ===
522 +
523 +=== [[image:image-20220127170155-2.png||height="682" width="1439"]] ===
524 +
525 +=== Results: ===
526 +
527 +the output of this simulationo is...
528 +
529 +
530 +
531 +
532 +
533 +
244 244  ==== ====
image-20220127165822-1.png
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