Changes for page Code description
Last modified by galluzziandrea on 2022/06/20 12:33
From version 27.1
edited by galluzziandrea
on 2022/01/27 17:31
on 2022/01/27 17:31
Change comment:
There is no comment for this version
To version 7.1
edited by galluzziandrea
on 2021/12/09 14:49
on 2021/12/09 14:49
Change comment:
There is no comment for this version
Summary
-
Page properties (1 modified, 0 added, 0 removed)
-
Attachments (0 modified, 0 added, 8 removed)
Details
- Page properties
-
- Content
-
... ... @@ -235,298 +235,9 @@ 235 235 # [.....],[],...] 236 236 {{/code}} 237 237 238 -(% class="wikigeneratedid" %) 239 -=== [[image:image-20220127173048-1.png||height="508" width="948"]] === 240 240 241 -=== Defining general and nest.kernel parameters === 242 242 243 -{{code language="python"}} 244 -#############################------------------------------------------------------------------------ 245 -#Clean the Network 246 -#############################------------------------------------------------------------------------ 247 -nest.ResetKernel() 248 248 249 -#############################------------------------------------------------------------------------ 250 -#insert the introductory parameters of the simulation 251 -#############################------------------------------------------------------------------------ 241 +=== Results === 252 252 253 - 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 - 532 532 ==== ====
- CorticalField_t=80_DeepSpontPlanar.mp4
-
- Author
-
... ... @@ -1,1 +1,0 @@ 1 -XWiki.galluzziandrea - Size
-
... ... @@ -1,1 +1,0 @@ 1 -3.4 MB - Content
- image-20220127165822-1.png
-
- Author
-
... ... @@ -1,1 +1,0 @@ 1 -XWiki.galluzziandrea - Size
-
... ... @@ -1,1 +1,0 @@ 1 -237.6 KB - Content
- image-20220127165908-2.png
-
- Author
-
... ... @@ -1,1 +1,0 @@ 1 -XWiki.galluzziandrea - Size
-
... ... @@ -1,1 +1,0 @@ 1 -106.6 KB - Content
- image-20220127170104-1.png
-
- Author
-
... ... @@ -1,1 +1,0 @@ 1 -XWiki.galluzziandrea - Size
-
... ... @@ -1,1 +1,0 @@ 1 -149.6 KB - Content
- image-20220127170155-2.png
-
- Author
-
... ... @@ -1,1 +1,0 @@ 1 -XWiki.galluzziandrea - Size
-
... ... @@ -1,1 +1,0 @@ 1 -280.0 KB - Content
- image-20220127170722-1.png
-
- Author
-
... ... @@ -1,1 +1,0 @@ 1 -XWiki.galluzziandrea - Size
-
... ... @@ -1,1 +1,0 @@ 1 -220.5 KB - Content
- image-20220127171242-1.png
-
- Author
-
... ... @@ -1,1 +1,0 @@ 1 -XWiki.galluzziandrea - Size
-
... ... @@ -1,1 +1,0 @@ 1 -301.3 KB - Content
- image-20220127173048-1.png
-
- Author
-
... ... @@ -1,1 +1,0 @@ 1 -XWiki.galluzziandrea - Size
-
... ... @@ -1,1 +1,0 @@ 1 -71.5 KB - Content
- Introduction (path and modules):
- Check where I am and place myself in the right folder:
- Import the modules necessary for the simulation:
- Define necessary classes to import the Initialization Files:
- Import the initialization files:
- Defining general and nest.kernel parameters
- Building the network: neuronal populations , Poisson processes and spike detectors
- Connecting the network nodes: neuronal populations, Poisson processes and spike detectors
- Simulating: neuronal time evolution.
- Results: