Example #1
0
def RunColumnSimulation(net_id="TestRunColumn",
                        nml2_source_dir="../../../neuroConstruct/generatedNeuroML2/",
                        sim_config="TempSimConfig",
                        scale_cortex=0.1,
                        scale_thalamus=0.1,
                        cell_bodies_overlap=True,
                        cylindrical=True,
                        default_synaptic_delay=0.05,
                        gaba_scaling=1.0,
                        l4ss_ampa_scaling=1.0,
                        l5pyr_gap_scaling =1.0,
                        in_nrt_tcr_nmda_scaling =1.0,
                        pyr_ss_nmda_scaling=1.0,
                        deep_bias_current=-1,
                        include_gap_junctions=True,
                        which_cell_types_to_include='all',
                        dir_nml2="../../",
                        backgroundL5Rate=30, # Hz
                        backgroundL23Rate=30, # Hz
                        duration=300,
                        dt=0.025,
                        max_memory='1000M',
                        seed=1234,
                        simulator=None,
                        num_of_cylinder_sides=None):
              
                        
    popDictFull = {}
    
    ##############   Full model ##################################
    
    popDictFull['CG3D_L23PyrRS'] = (1000, 'L23','L23PyrRS','multi', occ.L23_PRINCIPAL_CELL)
    popDictFull['CG3D_L23PyrFRB']= (50,'L23','L23PyrFRB_varInit','multi', occ.L23_PRINCIPAL_CELL_2)
    
    popDictFull['CG3D_SupBask'] = (90, 'L23','SupBasket','multi', occ.L23_INTERNEURON)  # over both l23 & l4
    popDictFull['CG3D_SupAxAx'] = (90, 'L23','SupAxAx','multi', occ.L23_INTERNEURON_2)  # over both l23 & l4
    popDictFull['CG3D_SupLTS']= (90,'L23','SupLTSInter','multi', occ.L4_INTERNEURON)    # over both l23 & l4
    
    popDictFull['CG3D_L4SpinStell']= (240,'L4','L4SpinyStellate','multi', occ.L4_PRINCIPAL_CELL)
    
    popDictFull['CG3D_L5TuftIB'] = (800, 'L5','L5TuftedPyrIB','multi', occ.L5_PRINCIPAL_CELL)
    popDictFull['CG3D_L5TuftRS']= (200,'L5','L5TuftedPyrRS','multi', occ.L5_PRINCIPAL_CELL_2)     
    
    popDictFull['CG3D_L6NonTuftRS']= (500,'L6','L6NonTuftedPyrRS','multi', occ.L6_PRINCIPAL_CELL)
    
    popDictFull['CG3D_DeepAxAx']= (100,'L6','DeepAxAx','multi', occ.L5_INTERNEURON)     # over both l5 & l6
    popDictFull['CG3D_DeepBask']= (100,'L6','DeepBasket','multi', occ.L5_INTERNEURON_2)   # over both l5 & l6
    popDictFull['CG3D_DeepLTS']= (100,'L6','DeepLTSInter','multi', occ.L6_INTERNEURON)  # over both l5 & l6
    
    popDictFull['CG3D_nRT']= (100,'Thalamus','nRT','multi', occ.THALAMUS_1)
    popDictFull['CG3D_TCR']= (100,'Thalamus','TCR','multi', occ.THALAMUS_2)
    
    ###############################################################
    
    dir_to_cells=os.path.join(dir_nml2,"cells")
    
    dir_to_synapses=os.path.join(dir_nml2,"synapses")
    
    dir_to_gap_junctions=os.path.join(dir_nml2,"gapJunctions")
    
    popDict={}
    
    cell_model_list=[]
    
    cell_diameter_dict={}
    
    nml_doc, network = oc.generate_network(net_id,seed)
    
    for cell_population in popDictFull.keys():
    
        include_cell_population=False
        
        cell_model=popDictFull[cell_population][2]
    
        if which_cell_types_to_include=='all' or cell_model in which_cell_types_to_include:
        
           popDict[cell_population]=()

           if popDictFull[cell_population][1] !='Thalamus':
             
              popDict[cell_population]=(int(round(scale_cortex*popDictFull[cell_population][0])), 
                                        popDictFull[cell_population][1],
                                        popDictFull[cell_population][2],
                                        popDictFull[cell_population][3],
                                        popDictFull[cell_population][4])
                  
              cell_count=int(round(scale_cortex*popDictFull[cell_population][0]))
                
           else:
             
              popDict[cell_population]=(int(round(scale_thalamus*popDictFull[cell_population][0])),
                                        popDictFull[cell_population][1],
                                        popDictFull[cell_population][2],
                                        popDictFull[cell_population][3],
                                        popDictFull[cell_population][4])
                  
              cell_count=int(round(scale_thalamus*popDictFull[cell_population][0]))
           
           if cell_count !=0:
    
              include_cell_population=True
               
        if include_cell_population:
        
           cell_model_list.append(popDictFull[cell_population][2])
           
           cell_diameter=oc_build.get_soma_diameter(popDictFull[cell_population][2],dir_to_cell=dir_to_cells)
           
           if popDictFull[cell_population][2] not in cell_diameter_dict.keys():
           
              cell_diameter_dict[popDictFull[cell_population][2]]=cell_diameter
           
           
    cell_model_list_final=list(set(cell_model_list))
    
    opencortex.print_comment_v("This is a final list of cell model ids: %s"%cell_model_list_final)
    
    copy_nml2_from_source=False
    
    for cell_model in cell_model_list_final:
    
       if not os.path.exists(os.path.join(dir_to_cells,"%s.cell.nml"%cell_model)):
       
          copy_nml2_from_source=True
          
          break
           
    if copy_nml2_from_source:
       
       oc_build.copy_nml2_source(dir_to_project_nml2=dir_nml2,
                        primary_nml2_dir=nml2_source_dir,
                        electrical_synapse_tags=['Elect'],
                        chemical_synapse_tags=['.synapse.'],
                        extra_channel_tags=['cad'])
                        
       passed_includes_in_cells=oc_utils.check_includes_in_cells(dir_to_cells,cell_model_list_final,extra_channel_tags=['cad'])
       
       if not passed_includes_in_cells:
       
          opencortex.print_comment_v("Execution of RunColumn.py will terminate.")
  
          quit()
             
    for cell_model in cell_model_list_final:
        
        oc_build._add_cell_and_channels(nml_doc, os.path.join(dir_to_cells,"%s.cell.nml"%cell_model), cell_model, use_prototypes=False)
        
    t1=-0
    t2=-250
    t3=-250
    t4=-200.0
    t5=-300.0
    t6=-300.0
    t7=-200.0
    t8=-200.0

    boundaries={}

    boundaries['L1']=[0,t1]
    boundaries['L23']=[t1,t1+t2+t3]
    boundaries['L4']=[t1+t2+t3,t1+t2+t3+t4]
    boundaries['L5']=[t1+t2+t3+t4,t1+t2+t3+t4+t5]
    boundaries['L6']=[t1+t2+t3+t4+t5,t1+t2+t3+t4+t5+t6]
    boundaries['Thalamus']=[t1+t2+t3+t4+t5+t6+t7,t1+t2+t3+t4+t5+t6+t7+t8]
    
    xs = [0,500]
    zs = [0,500] 
    
    passed_pops=oc_utils.check_pop_dict_and_layers(pop_dict=popDict,boundary_dict=boundaries)
    
    if passed_pops:
    
       opencortex.print_comment_v("Population parameters were specified correctly.") 
       
       if cylindrical:
       
          pop_params=oc_utils.add_populations_in_cylindrical_layers(network,boundaries,popDict,radiusOfCylinder=250,cellBodiesOverlap=cell_bodies_overlap,
                                                                    cellDiameterArray=cell_diameter_dict,numOfSides=num_of_cylinder_sides)
                                                                 
       else:
                                                                 
          pop_params=oc_utils.add_populations_in_rectangular_layers(network,boundaries,popDict,xs,zs,cellBodiesOverlap=False,cellDiameterArray=cell_diameter_dict)
       
    else:
    
       opencortex.print_comment_v("Population parameters were specified incorrectly; execution of RunColumn.py will terminate.")
       
       quit() 
    
    src_files = os.listdir("./")
    
    if 'netConnList' in src_files:
    
       full_path_to_connectivity='netConnList'
       
    else:
    
       full_path_to_connectivity="../../../neuroConstruct/pythonScripts/netbuild/netConnList"
                   
    weight_params=[{'weight':gaba_scaling,'synComp':'GABAA','synEndsWith':[],'targetCellGroup':[]},
                   {'weight':l4ss_ampa_scaling,'synComp':'Syn_AMPA_L4SS_L4SS','synEndsWith':[],'targetCellGroup':[]},
                   {'weight':l5pyr_gap_scaling,'synComp':'Syn_Elect_DeepPyr_DeepPyr','synEndsWith':[],'targetCellGroup':['CG3D_L5']},
                   {'weight':in_nrt_tcr_nmda_scaling,'synComp':'NMDA','synEndsWith':["_IN","_DeepIN","_SupIN","_SupFS","_DeepFS","_SupLTS","_DeepLTS","_nRT","_TCR"],
                   'targetCellGroup':[]},
                   {'weight':pyr_ss_nmda_scaling,'synComp':'NMDA','synEndsWith':["_IN","_DeepIN","_SupIN","_SupFS","_DeepFS","_SupLTS","_DeepLTS","_nRT","_TCR"],
                   'targetCellGroup':[]}]
                   
    delay_params=[{'delay':default_synaptic_delay,'synComp':'all'}]

    passed_weight_params=oc_utils.check_weight_params(weight_params)
    
    passed_delay_params=oc_utils.check_delay_params(delay_params)
    
    if passed_weight_params and passed_delay_params:    
    
       opencortex.print_comment_v("Synaptic weight and delay parameters were specified correctly.")     
       
       ignore_synapses = []
       if not include_gap_junctions:
           ignore_synapses = ['Syn_Elect_SupPyr_SupPyr','Syn_Elect_CortIN_CortIN','Syn_Elect_L4SS_L4SS','Syn_Elect_DeepPyr_DeepPyr','Syn_Elect_nRT_nRT']
    
       all_synapse_components,projArray,cached_segment_dicts=oc_utils.build_connectivity(net=network,
                                                                                         pop_objects=pop_params,
                                                                                         path_to_cells=dir_to_cells,
                                                                                         full_path_to_conn_summary=full_path_to_connectivity,
                                                                                         pre_segment_group_info=[{'PreSegGroup':"distal_axon",'ProjType':'Chem'}],
                                                                                         synaptic_scaling_params=weight_params,
                                                                                         synaptic_delay_params=delay_params,
                                                                                         ignore_synapses=ignore_synapses)   
                                                                                         
    else:
       
       if not passed_weight_params:
       
          opencortex.print_comment_v("Synaptic weight parameters were specified incorrectly; execution of RunColumn.py will terminate.") 
          
       if not passed_delay_params:
       
          opencortex.print_comment_v("Synaptic delay parameters were specified incorrectly; execution of RunColumn.py will terminate.")
       
       quit()
    
    ############ for testing only; will add original specifications later ##############################################################
    
    if sim_config=="Testing1":
       
       input_params={'CG3D_L23PyrRS':[{'InputType':'GeneratePoissonTrains',
                          'InputName':'Poi_CG3D_L23PyrRS',
                          'TrainType':'transient',
                          'Synapse':'Syn_AMPA_SupPyr_SupPyr',
                          'AverageRateList':[200.0,150.0],
                          'RateUnits':'Hz',
                          'TimeUnits':'ms',
                          'DurationList':[100.0,50.0],
                          'DelayList':[50.0,200.0],
                          'FractionToTarget':1.0,
                          'LocationSpecific':False,
                          'TargetDict':{'soma_group':1 }       }]              }
                       
    ###################################################################################################################################
    
    if sim_config=="Testing2":
    
       input_params_final={'CG3D_L23PyrRS':[{'InputType':'PulseGenerators',
                           'InputName':"DepCurr_L23RS",
                           'Noise':True,
                           'SmallestAmplitudeList':[5.0E-5,1.0E-5],
                           'LargestAmplitudeList':[1.0E-4,2.0E-5],
                           'DurationList':[20000.0,20000.0],
                           'DelayList':[0.0,20000.0],
                           'TimeUnits':'ms',
                           'AmplitudeUnits':'uA',
                           'FractionToTarget':1.0,
                           'LocationSpecific':False,
                           'TargetDict':{'dendrite_group':1}             }]             } 
                     
    if sim_config=="TempSimConfig":
    
       input_params ={'CG3D_L23PyrRS':[{'InputType':'PulseGenerators',
                     'InputName':"DepCurr_L23RS",
                     'Noise':True,
                     'SmallestAmplitudeList':[5.0E-5],
                     'LargestAmplitudeList':[1.0E-4],
                     'DurationList':[20000.0],
                     'DelayList':[0.0],
                     'TimeUnits':'ms',
                     'AmplitudeUnits':'uA',
                     'FractionToTarget':1.0,
                     'LocationSpecific':False,
                     'UniversalTargetSegmentID':0,
                     'UniversalFractionAlong':0.5},
                     
                      {'InputType':'GeneratePoissonTrains',
                     'InputName':"BackgroundL23RS",
                     'TrainType':'persistent',
                     'Synapse':'Syn_AMPA_SupPyr_SupPyr',
                     'AverageRateList':[float(backgroundL23Rate)],
                     'RateUnits':'Hz',
                     'FractionToTarget':1.0,
                     'LocationSpecific':False,
                     'TargetDict':{'dendrite_group':100} },
                     
                     {'InputType':'GeneratePoissonTrains',
                     'InputName':"EctopicStimL23RS",
                     'TrainType':'persistent',
                     'Synapse':'SynForEctStim',
                     'AverageRateList':[0.1],
                     'RateUnits':'Hz',
                     'FractionToTarget':1.0,
                     'LocationSpecific':False,
                     'UniversalTargetSegmentID':143,
                     'UniversalFractionAlong':0.5} ],
                     
                     'CG3D_TCR':[{'InputType':'GeneratePoissonTrains',
                     'InputName':"EctopicStimTCR",
                     'TrainType':'persistent',
                     'Synapse':'SynForEctStim',
                     'AverageRateList':[1.0],
                     'RateUnits':'Hz',
                     'FractionToTarget':1.0,
                     'LocationSpecific':False,
                     'UniversalTargetSegmentID':269,
                     'UniversalFractionAlong':0.5},
                     
                     {'InputType':'GeneratePoissonTrains',
                     'InputName':"Input_20",
                     'TrainType':'persistent',
                     'Synapse':'Syn_AMPA_L6NT_TCR',
                     'AverageRateList':[50.0],
                     'RateUnits':'Hz',
                     'FractionToTarget':1.0,
                     'LocationSpecific':False,
                     'TargetDict':{'dendrite_group':100} }],  
                     
                     'CG3D_L23PyrFRB':[{'InputType':'GeneratePoissonTrains',
                     'InputName':"EctopicStimL23FRB",
                     'TrainType':'persistent',
                     'Synapse':'SynForEctStim',
                     'AverageRateList':[0.1],
                     'RateUnits':'Hz',
                     'FractionToTarget':1.0,
                     'LocationSpecific':False,
                     'UniversalTargetSegmentID':143,
                     'UniversalFractionAlong':0.5},
                     
                     {'InputType':'PulseGenerators',
                     'InputName':"DepCurr_L23FRB",
                     'Noise':True,
                     'SmallestAmplitudeList':[2.5E-4],
                     'LargestAmplitudeList':[3.5E-4],
                     'DurationList':[20000.0],
                     'DelayList':[0.0],
                     'TimeUnits':'ms',
                     'AmplitudeUnits':'uA',
                     'FractionToTarget':1.0,
                     'LocationSpecific':False,
                     'UniversalTargetSegmentID':0,
                     'UniversalFractionAlong':0.5} ], 
                     
                     'CG3D_L6NonTuftRS':[{'InputType':'GeneratePoissonTrains',
                     'InputName':"EctopicStimL6NT",
                     'TrainType':'persistent',
                     'Synapse':'SynForEctStim',
                     'AverageRateList':[1.0],
                     'RateUnits':'Hz',
                     'FractionToTarget':1.0,
                     'LocationSpecific':False,
                     'UniversalTargetSegmentID':95,
                     'UniversalFractionAlong':0.5},
                     
                     {'InputType':'PulseGenerators',
                     'InputName':"DepCurr_L6NT",
                     'Noise':True,
                     'SmallestAmplitudeList':[5.0E-5],
                     'LargestAmplitudeList':[1.0E-4],
                     'DurationList':[20000.0],
                     'DelayList':[0.0],
                     'TimeUnits':'ms',
                     'AmplitudeUnits':'uA',
                     'FractionToTarget':1.0,
                     'LocationSpecific':False,
                     'UniversalTargetSegmentID':0,
                     'UniversalFractionAlong':0.5} ],
                     
                     'CG3D_L4SpinStell':[{'InputType':'PulseGenerators',
                     'InputName':"DepCurr_L4SS",
                     'Noise':True,
                     'SmallestAmplitudeList':[5.0E-5],
                     'LargestAmplitudeList':[1.0E-4],
                     'DurationList':[20000.0],
                     'DelayList':[0.0],
                     'TimeUnits':'ms',
                     'AmplitudeUnits':'uA',
                     'FractionToTarget':1.0,
                     'LocationSpecific':False,
                     'UniversalTargetSegmentID':0,
                     'UniversalFractionAlong':0.5}],
                     
                     'CG3D_L5TuftIB':[{'InputType':'GeneratePoissonTrains',
                     'InputName':"BackgroundL5",
                     'TrainType':'persistent',
                     'Synapse':'Syn_AMPA_L5RS_L5Pyr',
                     'AverageRateList':[float(backgroundL5Rate)],
                     'RateUnits':'Hz',
                     'FractionToTarget':1.0,
                     'LocationSpecific':False,
                     'TargetDict':{'dendrite_group':100} },
                     
                     {'InputType':'GeneratePoissonTrains',
                     'InputName':"EctopicStimL5IB",
                     'TrainType':'persistent',
                     'Synapse':'SynForEctStim',
                     'AverageRateList':[1.0],
                     'RateUnits':'Hz',
                     'FractionToTarget':1.0,
                     'LocationSpecific':False,
                     'UniversalTargetSegmentID':119,
                     'UniversalFractionAlong':0.5},
                     
                      {'InputType':'PulseGenerators',
                     'InputName':"DepCurr_L5IB",
                     'Noise':True,
                     'SmallestAmplitudeList':[5.0E-5],
                     'LargestAmplitudeList':[1.0E-4],
                     'DurationList':[20000.0],
                     'DelayList':[0.0],
                     'TimeUnits':'ms',
                     'AmplitudeUnits':'uA',
                     'FractionToTarget':1.0,
                     'LocationSpecific':False,
                     'UniversalTargetSegmentID':0,
                     'UniversalFractionAlong':0.5} ],
                     
                     'CG3D_L5TuftRS':[{'InputType':'GeneratePoissonTrains',
                     'InputName':"EctopicStimL5RS",
                     'TrainType':'persistent',
                     'Synapse':'SynForEctStim',
                     'AverageRateList':[1.0],
                     'RateUnits':'Hz',
                     'FractionToTarget':1.0,
                     'LocationSpecific':False,
                     'UniversalTargetSegmentID':119,
                     'UniversalFractionAlong':0.5},
                     
                      {'InputType':'PulseGenerators',
                     'InputName':"DepCurr_L5RS",
                     'Noise':True,
                     'SmallestAmplitudeList':[5.0E-5],
                     'LargestAmplitudeList':[1.0E-4],
                     'DurationList':[20000.0],
                     'DelayList':[0.0],
                     'TimeUnits':'ms',
                     'AmplitudeUnits':'uA',
                     'FractionToTarget':1.0,
                     'LocationSpecific':False,
                     'UniversalTargetSegmentID':0,
                     'UniversalFractionAlong':0.5}  ]   }
                     
       input_params_final={}
       
       for pop_id in pop_params.keys():
          
           if pop_id in input_params.keys():
       
              input_params_final[pop_id]=input_params[pop_id]
                     
       if deep_bias_current >= 0:
    
          for cell_group in input_params_final.keys():
       
              for input_group in range(0,len(input_params_final[cell_group])):
              
                  check_type=input_params_final[cell_group][input_group]['InputType']=="PulseGenerators"
                  
                  check_group_1= cell_group=="CG3D_L5TuftIB"
                  
                  check_group_2=cell_group =="CG3D_L5TuftRS"
                  
                  check_group_3= cell_group =="CG3D_L6NonTuftRS"
           
                  if check_type and (check_group_1 or check_group_2 or check_group_3):
	    
	             opencortex.print_comment_v("Changing offset current in 'PulseGenerators' for %s to %f"%(cell_group, deep_bias_current))
	      
	             input_params_final[cell_group][input_group]['SmallestAmplitudeList']=[ (deep_bias_current-0.05)/1000 ] 
	              
	             input_params_final[cell_group][input_group]['LargestAmplitudeList']=[ (deep_bias_current+0.05)/1000 ]
                     
    input_list_array_final, input_synapse_list=oc_utils.build_inputs(nml_doc=nml_doc,
                                                                     net=network,
                                                                     population_params=pop_params,
                                                                     input_params=input_params_final,
                                                                     cached_dicts=cached_segment_dicts,
                                                                     path_to_cells=dir_to_cells,
                                                                     path_to_synapses=dir_to_synapses)
    
    ####################################################################################################################################
    
    for input_synapse in input_synapse_list:
    
        if input_synapse not in all_synapse_components:
        
           all_synapse_components.append(input_synapse)
           
    synapse_list=[]
    
    gap_junction_list=[]
        
    for syn_ind in range(0,len(all_synapse_components)):
    
        if 'Elect' not in all_synapse_components[syn_ind]:
        
           synapse_list.append(all_synapse_components[syn_ind])
        
           all_synapse_components[syn_ind]=os.path.join(net_id,all_synapse_components[syn_ind]+".synapse.nml")
           
        else:
        
           gap_junction_list.append(all_synapse_components[syn_ind])
        
           all_synapse_components[syn_ind]=os.path.join(net_id,all_synapse_components[syn_ind]+".nml")
           
    oc_build.add_synapses(nml_doc,dir_to_synapses,synapse_list,synapse_tag=True)
    
    oc_build.add_synapses(nml_doc,dir_to_gap_junctions,gap_junction_list,synapse_tag=False)
    
    nml_file_name = '%s.net.nml'%network.id
    
    oc.save_network(nml_doc, nml_file_name, validate=True,max_memory=max_memory)
    
    oc_build.remove_component_dirs(dir_to_project_nml2="%s"%network.id,list_of_cell_ids=cell_model_list_final,extra_channel_tags=['cad'])
    
    lems_file_name=oc.generate_lems_simulation(nml_doc, 
                                               network, 
                                               nml_file_name, 
                                               duration =duration, 
                                               dt =dt,
                                               include_extra_lems_files=all_synapse_components)
     
    if simulator != None:                                          
                                               
       opencortex.print_comment_v("Starting simulation of %s.net.nml"%net_id)
                            
       oc.simulate_network(lems_file_name=lems_file_name,
                           simulator=simulator,
                           max_memory=max_memory)
def RunColumnSimulation(
    net_id="TestRunColumn",
    nml2_source_dir="../../../neuroConstruct/generatedNeuroML2/",
    sim_config="TempSimConfig",
    scale_cortex=0.1,
    scale_thalamus=0.1,
    cell_bodies_overlap=True,
    cylindrical=True,
    default_synaptic_delay=0.05,
    gaba_scaling=1.0,
    l4ss_ampa_scaling=1.0,
    l5pyr_gap_scaling=1.0,
    in_nrt_tcr_nmda_scaling=1.0,
    pyr_ss_nmda_scaling=1.0,
    deep_bias_current=-1,
    include_gap_junctions=True,
    which_models="all",
    dir_nml2="../../",
    duration=300,
    dt=0.025,
    max_memory="1000M",
    seed=1234,
    simulator=None,
    num_of_cylinder_sides=None,
):

    popDictFull = {}

    ##############   Full model ##################################

    popDictFull["CG3D_L23PyrRS"] = (1000, "L23", "L23PyrRS", "multi")
    popDictFull["CG3D_SupBask"] = (90, "L23", "SupBasket", "multi")
    popDictFull["CG3D_SupAxAx"] = (90, "L23", "SupAxAx", "multi")
    popDictFull["CG3D_L5TuftIB"] = (800, "L5", "L5TuftedPyrIB", "multi")
    popDictFull["CG3D_L5TuftRS"] = (200, "L5", "L5TuftedPyrRS", "multi")
    popDictFull["CG3D_L4SpinStell"] = (240, "L4", "L4SpinyStellate", "multi")
    popDictFull["CG3D_L23PyrFRB"] = (50, "L23", "L23PyrFRB_varInit", "multi")
    popDictFull["CG3D_L6NonTuftRS"] = (500, "L6", "L6NonTuftedPyrRS", "multi")
    popDictFull["CG3D_DeepAxAx"] = (100, "L6", "DeepAxAx", "multi")
    popDictFull["CG3D_DeepBask"] = (100, "L6", "DeepBasket", "multi")
    popDictFull["CG3D_DeepLTS"] = (100, "L6", "DeepLTSInter", "multi")
    popDictFull["CG3D_SupLTS"] = (90, "L23", "SupLTSInter", "multi")
    popDictFull["CG3D_nRT"] = (100, "Thalamus", "nRT", "multi")
    popDictFull["CG3D_TCR"] = (100, "Thalamus", "TCR", "multi")

    ###############################################################

    dir_to_cells = os.path.join(dir_nml2, "cells")

    dir_to_synapses = os.path.join(dir_nml2, "synapses")

    dir_to_gap_junctions = os.path.join(dir_nml2, "gapJunctions")

    popDict = {}

    cell_model_list = []

    cell_diameter_dict = {}

    nml_doc, network = oc.generate_network(net_id, seed)

    for cell_population in popDictFull.keys():

        include_cell_population = False

        cell_model = popDictFull[cell_population][2]

        if which_models == "all" or cell_model in which_models:

            popDict[cell_population] = ()

            if popDictFull[cell_population][1] != "Thalamus":

                popDict[cell_population] = (
                    int(round(scale_cortex * popDictFull[cell_population][0])),
                    popDictFull[cell_population][1],
                    popDictFull[cell_population][2],
                    popDictFull[cell_population][3],
                )

                cell_count = int(round(scale_cortex * popDictFull[cell_population][0]))

            else:

                popDict[cell_population] = (
                    int(round(scale_thalamus * popDictFull[cell_population][0])),
                    popDictFull[cell_population][1],
                    popDictFull[cell_population][2],
                    popDictFull[cell_population][3],
                )

                cell_count = int(round(scale_thalamus * popDictFull[cell_population][0]))

            if cell_count != 0:

                include_cell_population = True

        if include_cell_population:

            cell_model_list.append(popDictFull[cell_population][2])

            cell_diameter = oc.get_soma_diameter(popDictFull[cell_population][2], dir_to_cell=dir_to_cells)

            if popDictFull[cell_population][2] not in cell_diameter_dict.keys():

                cell_diameter_dict[popDictFull[cell_population][2]] = cell_diameter

    cell_model_list_final = list(set(cell_model_list))

    opencortex.print_comment_v("This is a final list of cell model ids: %s" % cell_model_list_final)

    copy_nml2_from_source = False

    for cell_model in cell_model_list_final:

        if not os.path.exists(os.path.join(dir_to_cells, "%s.cell.nml" % cell_model)):

            copy_nml2_from_source = True

            break

    if copy_nml2_from_source:

        oc.copy_nml2_source(
            dir_to_project_nml2=dir_nml2,
            primary_nml2_dir=nml2_source_dir,
            electrical_synapse_tags=["Elect"],
            chemical_synapse_tags=[".synapse."],
            extra_channel_tags=["cad"],
        )

        passed_includes_in_cells = oc_utils.check_includes_in_cells(
            dir_to_cells, cell_model_list_final, extra_channel_tags=["cad"]
        )

        if not passed_includes_in_cells:

            opencortex.print_comment_v("Execution of RunColumn.py will terminate.")

            quit()

    for cell_model in cell_model_list_final:

        oc.add_cell_and_channels(
            nml_doc, os.path.join(dir_to_cells, "%s.cell.nml" % cell_model), cell_model, use_prototypes=False
        )

    t1 = -0
    t2 = -250
    t3 = -250
    t4 = -200.0
    t5 = -300.0
    t6 = -300.0
    t7 = -200.0
    t8 = -200.0

    boundaries = {}

    boundaries["L1"] = [0, t1]
    boundaries["L23"] = [t1, t1 + t2 + t3]
    boundaries["L4"] = [t1 + t2 + t3, t1 + t2 + t3 + t4]
    boundaries["L5"] = [t1 + t2 + t3 + t4, t1 + t2 + t3 + t4 + t5]
    boundaries["L6"] = [t1 + t2 + t3 + t4 + t5, t1 + t2 + t3 + t4 + t5 + t6]
    boundaries["Thalamus"] = [t1 + t2 + t3 + t4 + t5 + t6 + t7, t1 + t2 + t3 + t4 + t5 + t6 + t7 + t8]

    xs = [0, 500]
    zs = [0, 500]

    passed_pops = oc_utils.check_pop_dict_and_layers(pop_dict=popDict, boundary_dict=boundaries)

    if passed_pops:

        opencortex.print_comment_v("Population parameters were specified correctly.")

        if cylindrical:

            pop_params = oc_utils.add_populations_in_cylindrical_layers(
                network,
                boundaries,
                popDict,
                radiusOfCylinder=250,
                cellBodiesOverlap=cell_bodies_overlap,
                cellDiameterArray=cell_diameter_dict,
                numOfSides=num_of_cylinder_sides,
            )

        else:

            pop_params = oc_utils.add_populations_in_rectangular_layers(
                network, boundaries, popDict, xs, zs, cellBodiesOverlap=False, cellDiameterArray=cell_diameter_dict
            )

    else:

        opencortex.print_comment_v(
            "Population parameters were specified incorrectly; execution of RunColumn.py will terminate."
        )

        quit()

    src_files = os.listdir("./")

    if "netConnList" in src_files:

        full_path_to_connectivity = "netConnList"

    else:

        full_path_to_connectivity = "../../../neuroConstruct/pythonScripts/netbuild/netConnList"

    weight_params = [
        {"weight": gaba_scaling, "synComp": "GABAA", "synEndsWith": [], "targetCellGroup": []},
        {"weight": l4ss_ampa_scaling, "synComp": "Syn_AMPA_L4SS_L4SS", "synEndsWith": [], "targetCellGroup": []},
        {
            "weight": l5pyr_gap_scaling,
            "synComp": "Syn_Elect_DeepPyr_DeepPyr",
            "synEndsWith": [],
            "targetCellGroup": ["CG3D_L5"],
        },
        {
            "weight": in_nrt_tcr_nmda_scaling,
            "synComp": "NMDA",
            "synEndsWith": ["_IN", "_DeepIN", "_SupIN", "_SupFS", "_DeepFS", "_SupLTS", "_DeepLTS", "_nRT", "_TCR"],
            "targetCellGroup": [],
        },
        {
            "weight": pyr_ss_nmda_scaling,
            "synComp": "NMDA",
            "synEndsWith": ["_IN", "_DeepIN", "_SupIN", "_SupFS", "_DeepFS", "_SupLTS", "_DeepLTS", "_nRT", "_TCR"],
            "targetCellGroup": [],
        },
    ]

    delay_params = [{"delay": default_synaptic_delay, "synComp": "all"}]

    passed_weight_params = oc_utils.check_weight_params(weight_params)

    passed_delay_params = oc_utils.check_delay_params(delay_params)

    if passed_weight_params and passed_delay_params:

        opencortex.print_comment_v("Synaptic weight and delay parameters were specified correctly.")

        ignore_synapses = []
        if not include_gap_junctions:
            ignore_synapses = [
                "Syn_Elect_SupPyr_SupPyr",
                "Syn_Elect_CortIN_CortIN",
                "Syn_Elect_L4SS_L4SS",
                "Syn_Elect_DeepPyr_DeepPyr",
                "Syn_Elect_nRT_nRT",
            ]

        all_synapse_components, projArray, cached_segment_dicts = oc_utils.build_connectivity(
            net=network,
            pop_objects=pop_params,
            path_to_cells=dir_to_cells,
            full_path_to_conn_summary=full_path_to_connectivity,
            pre_segment_group_info=[{"PreSegGroup": "distal_axon", "ProjType": "Chem"}],
            synaptic_scaling_params=weight_params,
            synaptic_delay_params=delay_params,
            ignore_synapses=ignore_synapses,
        )

    else:

        if not passed_weight_params:

            opencortex.print_comment_v(
                "Synaptic weight parameters were specified incorrectly; execution of RunColumn.py will terminate."
            )

        if not passed_delay_params:

            opencortex.print_comment_v(
                "Synaptic delay parameters were specified incorrectly; execution of RunColumn.py will terminate."
            )

        quit()

    ############ for testing only; will add original specifications later ##############################################################

    if sim_config == "Testing1":

        input_params = {
            "CG3D_L23PyrRS": [
                {
                    "InputType": "GeneratePoissonTrains",
                    "InputName": "Poi_CG3D_L23PyrRS",
                    "TrainType": "transient",
                    "Synapse": "Syn_AMPA_SupPyr_SupPyr",
                    "AverageRateList": [200.0, 150.0],
                    "RateUnits": "Hz",
                    "TimeUnits": "ms",
                    "DurationList": [100.0, 50.0],
                    "DelayList": [50.0, 200.0],
                    "FractionToTarget": 1.0,
                    "LocationSpecific": False,
                    "TargetDict": {"soma_group": 1},
                }
            ]
        }

    ###################################################################################################################################

    if sim_config == "Testing2":

        input_params_final = {
            "CG3D_L23PyrRS": [
                {
                    "InputType": "PulseGenerators",
                    "InputName": "DepCurr_L23RS",
                    "Noise": True,
                    "SmallestAmplitudeList": [5.0e-5, 1.0e-5],
                    "LargestAmplitudeList": [1.0e-4, 2.0e-5],
                    "DurationList": [20000.0, 20000.0],
                    "DelayList": [0.0, 20000.0],
                    "TimeUnits": "ms",
                    "AmplitudeUnits": "uA",
                    "FractionToTarget": 1.0,
                    "LocationSpecific": False,
                    "TargetDict": {"dendrite_group": 1},
                }
            ]
        }

    if sim_config == "TempSimConfig":

        input_params = {
            "CG3D_L23PyrRS": [
                {
                    "InputType": "PulseGenerators",
                    "InputName": "DepCurr_L23RS",
                    "Noise": True,
                    "SmallestAmplitudeList": [5.0e-5],
                    "LargestAmplitudeList": [1.0e-4],
                    "DurationList": [20000.0],
                    "DelayList": [0.0],
                    "TimeUnits": "ms",
                    "AmplitudeUnits": "uA",
                    "FractionToTarget": 1.0,
                    "LocationSpecific": False,
                    "UniversalTargetSegmentID": 0,
                    "UniversalFractionAlong": 0.5,
                },
                {
                    "InputType": "GeneratePoissonTrains",
                    "InputName": "BackgroundL23RS",
                    "TrainType": "persistent",
                    "Synapse": "Syn_AMPA_SupPyr_SupPyr",
                    "AverageRateList": [30.0],
                    "RateUnits": "Hz",
                    "FractionToTarget": 1.0,
                    "LocationSpecific": False,
                    "TargetDict": {"dendrite_group": 100},
                },
                {
                    "InputType": "GeneratePoissonTrains",
                    "InputName": "EctopicStimL23RS",
                    "TrainType": "persistent",
                    "Synapse": "SynForEctStim",
                    "AverageRateList": [0.1],
                    "RateUnits": "Hz",
                    "FractionToTarget": 1.0,
                    "LocationSpecific": False,
                    "UniversalTargetSegmentID": 143,
                    "UniversalFractionAlong": 0.5,
                },
            ],
            "CG3D_TCR": [
                {
                    "InputType": "GeneratePoissonTrains",
                    "InputName": "EctopicStimTCR",
                    "TrainType": "persistent",
                    "Synapse": "SynForEctStim",
                    "AverageRateList": [1.0],
                    "RateUnits": "Hz",
                    "FractionToTarget": 1.0,
                    "LocationSpecific": False,
                    "UniversalTargetSegmentID": 269,
                    "UniversalFractionAlong": 0.5,
                },
                {
                    "InputType": "GeneratePoissonTrains",
                    "InputName": "Input_20",
                    "TrainType": "persistent",
                    "Synapse": "Syn_AMPA_L6NT_TCR",
                    "AverageRateList": [50.0],
                    "RateUnits": "Hz",
                    "FractionToTarget": 1.0,
                    "LocationSpecific": False,
                    "TargetDict": {"dendrite_group": 100},
                },
            ],
            "CG3D_L23PyrFRB": [
                {
                    "InputType": "GeneratePoissonTrains",
                    "InputName": "EctopicStimL23FRB",
                    "TrainType": "persistent",
                    "Synapse": "SynForEctStim",
                    "AverageRateList": [0.1],
                    "RateUnits": "Hz",
                    "FractionToTarget": 1.0,
                    "LocationSpecific": False,
                    "UniversalTargetSegmentID": 143,
                    "UniversalFractionAlong": 0.5,
                },
                {
                    "InputType": "PulseGenerators",
                    "InputName": "DepCurr_L23FRB",
                    "Noise": True,
                    "SmallestAmplitudeList": [2.5e-4],
                    "LargestAmplitudeList": [3.5e-4],
                    "DurationList": [20000.0],
                    "DelayList": [0.0],
                    "TimeUnits": "ms",
                    "AmplitudeUnits": "uA",
                    "FractionToTarget": 1.0,
                    "LocationSpecific": False,
                    "UniversalTargetSegmentID": 0,
                    "UniversalFractionAlong": 0.5,
                },
            ],
            "CG3D_L6NonTuftRS": [
                {
                    "InputType": "GeneratePoissonTrains",
                    "InputName": "EctopicStimL6NT",
                    "TrainType": "persistent",
                    "Synapse": "SynForEctStim",
                    "AverageRateList": [1.0],
                    "RateUnits": "Hz",
                    "FractionToTarget": 1.0,
                    "LocationSpecific": False,
                    "UniversalTargetSegmentID": 95,
                    "UniversalFractionAlong": 0.5,
                },
                {
                    "InputType": "PulseGenerators",
                    "InputName": "DepCurr_L6NT",
                    "Noise": True,
                    "SmallestAmplitudeList": [5.0e-5],
                    "LargestAmplitudeList": [1.0e-4],
                    "DurationList": [20000.0],
                    "DelayList": [0.0],
                    "TimeUnits": "ms",
                    "AmplitudeUnits": "uA",
                    "FractionToTarget": 1.0,
                    "LocationSpecific": False,
                    "UniversalTargetSegmentID": 0,
                    "UniversalFractionAlong": 0.5,
                },
            ],
            "CG3D_L4SpinStell": [
                {
                    "InputType": "PulseGenerators",
                    "InputName": "DepCurr_L4SS",
                    "Noise": True,
                    "SmallestAmplitudeList": [5.0e-5],
                    "LargestAmplitudeList": [1.0e-4],
                    "DurationList": [20000.0],
                    "DelayList": [0.0],
                    "TimeUnits": "ms",
                    "AmplitudeUnits": "uA",
                    "FractionToTarget": 1.0,
                    "LocationSpecific": False,
                    "UniversalTargetSegmentID": 0,
                    "UniversalFractionAlong": 0.5,
                }
            ],
            "CG3D_L5TuftIB": [
                {
                    "InputType": "GeneratePoissonTrains",
                    "InputName": "BackgroundL5",
                    "TrainType": "persistent",
                    "Synapse": "Syn_AMPA_L5RS_L5Pyr",
                    "AverageRateList": [30.0],
                    "RateUnits": "Hz",
                    "FractionToTarget": 1.0,
                    "LocationSpecific": False,
                    "TargetDict": {"dendrite_group": 100},
                },
                {
                    "InputType": "GeneratePoissonTrains",
                    "InputName": "EctopicStimL5IB",
                    "TrainType": "persistent",
                    "Synapse": "SynForEctStim",
                    "AverageRateList": [1.0],
                    "RateUnits": "Hz",
                    "FractionToTarget": 1.0,
                    "LocationSpecific": False,
                    "UniversalTargetSegmentID": 119,
                    "UniversalFractionAlong": 0.5,
                },
                {
                    "InputType": "PulseGenerators",
                    "InputName": "DepCurr_L5IB",
                    "Noise": True,
                    "SmallestAmplitudeList": [5.0e-5],
                    "LargestAmplitudeList": [1.0e-4],
                    "DurationList": [20000.0],
                    "DelayList": [0.0],
                    "TimeUnits": "ms",
                    "AmplitudeUnits": "uA",
                    "FractionToTarget": 1.0,
                    "LocationSpecific": False,
                    "UniversalTargetSegmentID": 0,
                    "UniversalFractionAlong": 0.5,
                },
            ],
            "CG3D_L5TuftRS": [
                {
                    "InputType": "GeneratePoissonTrains",
                    "InputName": "EctopicStimL5RS",
                    "TrainType": "persistent",
                    "Synapse": "SynForEctStim",
                    "AverageRateList": [1.0],
                    "RateUnits": "Hz",
                    "FractionToTarget": 1.0,
                    "LocationSpecific": False,
                    "UniversalTargetSegmentID": 119,
                    "UniversalFractionAlong": 0.5,
                },
                {
                    "InputType": "PulseGenerators",
                    "InputName": "DepCurr_L5RS",
                    "Noise": True,
                    "SmallestAmplitudeList": [5.0e-5],
                    "LargestAmplitudeList": [1.0e-4],
                    "DurationList": [20000.0],
                    "DelayList": [0.0],
                    "TimeUnits": "ms",
                    "AmplitudeUnits": "uA",
                    "FractionToTarget": 1.0,
                    "LocationSpecific": False,
                    "UniversalTargetSegmentID": 0,
                    "UniversalFractionAlong": 0.5,
                },
            ],
        }

        input_params_final = {}

        for pop_id in pop_params.keys():

            if pop_id in input_params.keys():

                input_params_final[pop_id] = input_params[pop_id]

        if deep_bias_current >= 0:

            for cell_group in input_params_final.keys():

                for input_group in range(0, len(input_params_final[cell_group])):

                    check_type = input_params_final[cell_group][input_group]["InputType"] == "PulseGenerators"

                    check_group_1 = cell_group == "CG3D_L5TuftIB"

                    check_group_2 = cell_group == "CG3D_L5TuftRS"

                    check_group_3 = cell_group == "CG3D_L6NonTuftRS"

                    if check_type and (check_group_1 or check_group_2 or check_group_3):

                        opencortex.print_comment_v(
                            "Changing offset current in 'PulseGenerators' for %s to %f"
                            % (cell_group, deep_bias_current)
                        )

                        input_params_final[cell_group][input_group]["SmallestAmplitudeList"] = [
                            (deep_bias_current - 0.05) / 1000
                        ]

                        input_params_final[cell_group][input_group]["LargestAmplitudeList"] = [
                            (deep_bias_current + 0.05) / 1000
                        ]

    input_list_array_final, input_synapse_list = oc_utils.build_inputs(
        nml_doc=nml_doc,
        net=network,
        population_params=pop_params,
        input_params=input_params_final,
        cached_dicts=cached_segment_dicts,
        path_to_cells=dir_to_cells,
        path_to_synapses=dir_to_synapses,
    )

    ####################################################################################################################################

    for input_synapse in input_synapse_list:

        if input_synapse not in all_synapse_components:

            all_synapse_components.append(input_synapse)

    synapse_list = []

    gap_junction_list = []

    for syn_ind in range(0, len(all_synapse_components)):

        if "Elect" not in all_synapse_components[syn_ind]:

            synapse_list.append(all_synapse_components[syn_ind])

            all_synapse_components[syn_ind] = os.path.join(net_id, all_synapse_components[syn_ind] + ".synapse.nml")

        else:

            gap_junction_list.append(all_synapse_components[syn_ind])

            all_synapse_components[syn_ind] = os.path.join(net_id, all_synapse_components[syn_ind] + ".nml")

    oc.add_synapses(nml_doc, dir_to_synapses, synapse_list, synapse_tag=True)

    oc.add_synapses(nml_doc, dir_to_gap_junctions, gap_junction_list, synapse_tag=False)

    nml_file_name = "%s.net.nml" % network.id

    oc.save_network(nml_doc, nml_file_name, validate=True, max_memory=max_memory)

    oc.remove_component_dirs(
        dir_to_project_nml2="%s" % network.id, list_of_cell_ids=cell_model_list_final, extra_channel_tags=["cad"]
    )

    lems_file_name = oc.generate_lems_simulation(
        nml_doc, network, nml_file_name, duration=duration, dt=dt, include_extra_lems_files=all_synapse_components
    )

    if simulator != None:

        opencortex.print_comment_v("Starting simulation of %s.net.nml" % net_id)

        oc.simulate_network(lems_file_name=lems_file_name, simulator=simulator, max_memory=max_memory)
def RunPotjans2014(net_id='TestRunPotjans2014',
                   neuron_params = {'cm'        : 0.25,  # nF
                                    'i_offset'  : 0.0,   # nA
                                    'tau_m'     : 10.0,  # ms
                                    'tau_refrac': 2.0,   # ms
                                    'tau_syn_E' : 0.5,   # ms
                                    'tau_syn_I' : 0.5,   # ms
                                    'v_reset'   : -65.0,  # mV
                                    'v_rest'    : -65.0,  # mV         
                                    'v_thresh'  : -50.0,  # mV 
                                    'v_init'    : -58.0  # mV
                                    },
                   V0_mean = -58.0,
                   V0_sd= 5.0,
                   bg_rate=8.0,       # spikes/s
                   w_mean = 87.8e-3, # nA
                   w_ext = 87.8e-3, # nA
                   w_234 = 2 * 87.8e-3,  # nA
                   w_rel = 0.1,
                   w_rel_234 = 0.05,
                   d_mean = {'E': 1.5, 'I': 0.75},
                   d_sd = {'E': 0.75, 'I': 0.375},
                   K_ext = {'L23_E': 1600, 
                            'L23_I': 1500,
                            'L4_E': 2100, 
                            'L4_I': 1900,
                            'L5_E': 2000, 
                            'L5_I': 1900,
                            'L6_E': 2900, 
                            'L6_I': 2100},
                   full_mean_rates = {'L23_E': 0.971, 
                                      'L23_I': 2.868,
                                      'L4_E': 4.746, 
                                      'L4_I': 5.396,
                                      'L5_E': 8.142, 
                                      'L5_I': 9.078,
                                      'L6_E' : 0.991, 
                                      'L6_I': 7.523},
                   thal_params = {
                   # Number of neurons in thalamic population
                   'n_thal'      : 902,
                   # Connection probabilities
                   'C'           : {'L23_E': 0,
                                    'L23_I': 0,
                                    'L4_E' : 0.0983, 
                                    'L4_I': 0.0619,
                                    'L5_E' : 0, 
                                    'L5_I': 0,
                                    'L6_E' : 0.0512, 
                                    'L6_I': 0.0196},
                   'rate'        : 120.,  # spikes/s;
                   'start'       : 700.,  # ms
                   'duration'    : 10.   # ms;
                   },
                   which_populations='all',
                   which_pops_to_stimulate=[],
                   scale_excitatory_cortex=0.01,
                   scale_inhibitory_cortex=0.01,
                   scale_thalamus=0.01, 
                   K_scaling=1.0,
                   rel_inh_syn_w=-4.0,
                   input_type='poisson',
                   thalamic_input=False,
                   build_connections=True,
                   build_inputs=True,
                   duration=300.0,
                   dt=0.025,
                   max_memory='4000M',
                   seed=1234,
                   simulator=None):
    
    ######################### Full-scale model #############################################################
    
    popDictFull={}
    
    popDictFull['L23_E'] = (20683, 'L23','IF_curr_exp_L23_E','single')
    popDictFull['L23_I'] = (5834, 'L23','IF_curr_exp_L23_I','single')
    popDictFull['L4_E'] = (21915, 'L4','IF_curr_exp_L4_E','single')
    popDictFull['L4_I'] = (5479, 'L4','IF_curr_exp_L4_I','single')
    popDictFull['L5_E']= (4850,'L5','IF_curr_exp_L5_E','single')          
    popDictFull['L5_I']= (1065,'L5','IF_curr_exp_L5_I','single')
    popDictFull['L6_E']= (14395,'L23','IF_curr_exp_L6_E','single')
    popDictFull['L6_I']= (2948,'L6','IF_curr_exp_L6_I','single')
    popDictFull['Thalamus']=(thal_params['n_thal'],'Thalamus','Thalamus_Input','single')
    
    pop_ids=['L23_E','L23_I','L4_E','L4_I', 'L5_E','L5_I', 'L6_E','L6_I']
    
    n_layers = 4
    
    n_pops_per_layer = 2
              
    K_full = np.zeros([n_layers * n_pops_per_layer, n_layers * n_pops_per_layer])
    
    #######################################################################################################
    
    nml_doc, network = oc.generate_network(net_id,seed)
    
    popDict={}
    
    for cell_pop_id in popDictFull.keys():
        
        if which_populations=='all' or cell_pop_id in which_populations:
           
           if 'Thalamus' not in cell_pop_id:
           
              popDict[cell_pop_id]=()
           
              if 'E' in cell_pop_id:
             
                 popDict[cell_pop_id]=(int(round(scale_excitatory_cortex*popDictFull[cell_pop_id][0])), 
                                       popDictFull[cell_pop_id][1],
                                       popDictFull[cell_pop_id][2],
                                       popDictFull[cell_pop_id][3])
             
              if 'I' in cell_pop_id:
                  
                 popDict[cell_pop_id]=(int(round(scale_inhibitory_cortex*popDictFull[cell_pop_id][0])), 
                                       popDictFull[cell_pop_id][1],
                                       popDictFull[cell_pop_id][2],
                                       popDictFull[cell_pop_id][3])
                                       
           else:
           
              if thalamic_input:
                 
                 thal_tuple=(int(round(scale_thalamus*popDictFull[cell_pop_id][0])), 
                                       popDictFull[cell_pop_id][1],
                                       popDictFull[cell_pop_id][2],
                                       popDictFull[cell_pop_id][3])
                  
    popDictFinal={}
    
    for cell_pop_id in popDict.keys():
    
        if V0_mean != None and  V0_sd != None:

           for cell_ind in range(0,popDict[cell_pop_id][0]):
           
               v0=random.gauss(V0_mean,V0_sd)
               
               new_pop_id=cell_pop_id+str(cell_ind)
           
               PyNN_cell=neuroml.IF_curr_exp(id=popDict[cell_pop_id][2]+str(cell_ind),
                                             cm=neuron_params['cm'],
                                             i_offset=neuron_params['i_offset'],
                                             tau_syn_E=neuron_params['tau_syn_E'], 
                                             tau_syn_I=neuron_params['tau_syn_I'], 
                                             v_init=v0, 
                                             tau_m=neuron_params['tau_m'], 
                                             tau_refrac=neuron_params['tau_refrac'], 
                                             v_reset=neuron_params['v_reset'], 
                                             v_rest=neuron_params['v_rest'], 
                                             v_thresh=neuron_params['v_thresh'])
                                             
               nml_doc.IF_curr_exp.append(PyNN_cell)
               
               popDictFinal[new_pop_id]=(1,popDict[cell_pop_id][1],popDict[cell_pop_id][2]+str(cell_ind),popDict[cell_pop_id][3])
            
        else:
        
           popDictFinal[cell_pop_id]=popDict[cell_pop_id]
           
           PyNN_cell=neuroml.IF_curr_exp(id=popDict[cell_pop_id][2],
                                         cm=neuron_params['cm'],
                                         i_offset=neuron_params['i_offset'],
                                         tau_syn_E=neuron_params['tau_syn_E'], 
                                         tau_syn_I=neuron_params['tau_syn_I'], 
                                         v_init=neuron_params['v_init'], 
                                         tau_m=neuron_params['tau_m'], 
                                         tau_refrac=neuron_params['tau_refrac'], 
                                         v_reset=neuron_params['v_reset'], 
                                         v_rest=neuron_params['v_rest'], 
                                         v_thresh=neuron_params['v_thresh'])
                                             
           nml_doc.IF_curr_exp.append(PyNN_cell)
        
    t1=-100
    t2=-150
    t3=-150
    t4=-300.0
    t5=-300.0
    t6=-300.0
    t7=-200.0
    t8=-200.0

    boundaries={}

    boundaries['L1']=[0,t1]
    boundaries['L23']=[t1,t1+t2+t3]
    boundaries['L4']=[t1+t2+t3,t1+t2+t3+t4]
    boundaries['L5']=[t1+t2+t3+t4,t1+t2+t3+t4+t5]
    boundaries['L6']=[t1+t2+t3+t4+t5,t1+t2+t3+t4+t5+t6]
    boundaries['Thalamus']=[t1+t2+t3+t4+t5+t6+t7,t1+t2+t3+t4+t5+t6+t7+t8]
    
    xs = [0,1000]
    zs = [0,1000] 
    
    passed_pops=oc_utils.check_pop_dict_and_layers(pop_dict=popDictFinal,boundary_dict=boundaries)
    
    if passed_pops:
    
       opencortex.print_comment_v("Population parameters were specified correctly.") 
       
       #other options
      
       #pop_params=oc_utils.add_populations_in_cylindrical_layers(network,boundaries,popDictFinal,radiusOfCylinder=500,numOfSides=3)
       
       #pop_params=oc_utils.add_populations_in_cylindrical_layers(network,boundaries,popDictFinal,radiusOfCylinder=500)
                                                                 
       pop_params=oc_utils.add_populations_in_rectangular_layers(network,boundaries,popDictFinal,xs,zs) 
       
    else:
    
       opencortex.print_comment_v("Population parameters were specified incorrectly; execution of RunPotjans2014.py will terminate.")
       
       quit() 
    
    # Probabilities for >=1 connection between neurons in the given populations. 
    # The first index is for the target population; the second for the source population
    #             2/3e      2/3i    4e      4i      5e      5i      6e      6i
    #pop_ids=   ['L23_E','L23_I','L4_E','L4_I', 'L5_E','L5_I', 'L6_E','L6_I']
    
    conn_probs = [[0.1009,  0.1689, 0.0437, 0.0818, 0.0323, 0.,     0.0076, 0.    ],
                 [0.1346,   0.1371, 0.0316, 0.0515, 0.0755, 0.,     0.0042, 0.    ],
                 [0.0077,   0.0059, 0.0497, 0.135,  0.0067, 0.0003, 0.0453, 0.    ],
                 [0.0691,   0.0029, 0.0794, 0.1597, 0.0033, 0.,     0.1057, 0.    ],
                 [0.1004,   0.0622, 0.0505, 0.0057, 0.0831, 0.3726, 0.0204, 0.    ],
                 [0.0548,   0.0269, 0.0257, 0.0022, 0.06,   0.3158, 0.0086, 0.    ],
                 [0.0156,   0.0066, 0.0211, 0.0166, 0.0572, 0.0197, 0.0396, 0.2252],
                 [0.0364,   0.001,  0.0034, 0.0005, 0.0277, 0.008,  0.0658, 0.1443]]
                 
    conn_mean_w=[[w_mean,  rel_inh_syn_w*w_mean,   w_234 ,   rel_inh_syn_w*w_mean,  w_mean, rel_inh_syn_w*w_mean, w_mean, rel_inh_syn_w*w_mean],
                 [w_mean,  rel_inh_syn_w*w_mean,   w_mean,   rel_inh_syn_w*w_mean, w_mean, rel_inh_syn_w*w_mean, w_mean, rel_inh_syn_w*w_mean],
                 [w_mean,  rel_inh_syn_w*w_mean,   w_mean,   rel_inh_syn_w*w_mean, w_mean, rel_inh_syn_w*w_mean, w_mean, rel_inh_syn_w*w_mean],
                 [w_mean,  rel_inh_syn_w*w_mean,   w_mean,   rel_inh_syn_w*w_mean, w_mean, rel_inh_syn_w*w_mean, w_mean, rel_inh_syn_w*w_mean],
                 [w_mean,  rel_inh_syn_w*w_mean,   w_mean,   rel_inh_syn_w*w_mean, w_mean, rel_inh_syn_w*w_mean, w_mean, rel_inh_syn_w*w_mean],
                 [w_mean,  rel_inh_syn_w*w_mean,   w_mean,   rel_inh_syn_w*w_mean, w_mean, rel_inh_syn_w*w_mean, w_mean, rel_inh_syn_w*w_mean],
                 [w_mean,  rel_inh_syn_w*w_mean,   w_mean,   rel_inh_syn_w*w_mean, w_mean, rel_inh_syn_w*w_mean, w_mean, rel_inh_syn_w*w_mean],
                 [w_mean,  rel_inh_syn_w*w_mean,   w_mean,   rel_inh_syn_w*w_mean, w_mean, rel_inh_syn_w*w_mean, w_mean, rel_inh_syn_w*w_mean]]  
                
    conn_std_w=[]
                
    for target_pop_index in range(0,len(pop_ids)):
    
        conn_std_w_per_target_pop=[]
    
        for source_pop_index in range(0,len(pop_ids)):
        
            w_val=conn_mean_w[target_pop_index][source_pop_index]
        
            if pop_ids[source_pop_index]=="L4_E" and pop_ids[target_pop_index]=="L23_E":
            
               w_sd=w_val*w_rel_234
            
            else:
            
               w_sd=abs(w_val * w_rel)
               
            conn_std_w_per_target_pop.append(w_sd)
            
        conn_std_w.append(conn_std_w_per_target_pop)
                
    conn_mean_delay=[[d_mean['E'],  d_mean['I'], d_mean['E'], d_mean['I'], d_mean['E'], d_mean['I'], d_mean['E'], d_mean['I']],
                     [d_mean['E'],  d_mean['I'], d_mean['E'], d_mean['I'], d_mean['E'], d_mean['I'], d_mean['E'], d_mean['I']],
                     [d_mean['E'],  d_mean['I'], d_mean['E'], d_mean['I'], d_mean['E'], d_mean['I'], d_mean['E'], d_mean['I']],
                     [d_mean['E'],  d_mean['I'], d_mean['E'], d_mean['I'], d_mean['E'], d_mean['I'], d_mean['E'], d_mean['I']],
                     [d_mean['E'],  d_mean['I'], d_mean['E'], d_mean['I'], d_mean['E'], d_mean['I'], d_mean['E'], d_mean['I']],
                     [d_mean['E'],  d_mean['I'], d_mean['E'], d_mean['I'], d_mean['E'], d_mean['I'], d_mean['E'], d_mean['I']],
                     [d_mean['E'],  d_mean['I'], d_mean['E'], d_mean['I'], d_mean['E'], d_mean['I'], d_mean['E'], d_mean['I']],
                     [d_mean['E'],  d_mean['I'], d_mean['E'], d_mean['I'], d_mean['E'], d_mean['I'], d_mean['E'], d_mean['I']]] 
                         
    conn_std_delay=[[d_sd['E'],  d_sd['I'], d_sd['E'], d_sd['I'], d_sd['E'], d_sd['I'], d_sd['E'], d_sd['I']],
                    [d_sd['E'],  d_sd['I'], d_sd['E'], d_sd['I'], d_sd['E'], d_sd['I'], d_sd['E'], d_sd['I']],
                    [d_sd['E'],  d_sd['I'], d_sd['E'], d_sd['I'], d_sd['E'], d_sd['I'], d_sd['E'], d_sd['I']],
                    [d_sd['E'],  d_sd['I'], d_sd['E'], d_sd['I'], d_sd['E'], d_sd['I'], d_sd['E'], d_sd['I']],
                    [d_sd['E'],  d_sd['I'], d_sd['E'], d_sd['I'], d_sd['E'], d_sd['I'], d_sd['E'], d_sd['I']],
                    [d_sd['E'],  d_sd['I'], d_sd['E'], d_sd['I'], d_sd['E'], d_sd['I'], d_sd['E'], d_sd['I']],
                    [d_sd['E'],  d_sd['I'], d_sd['E'], d_sd['I'], d_sd['E'], d_sd['I'], d_sd['E'], d_sd['I']],
                    [d_sd['E'],  d_sd['I'], d_sd['E'], d_sd['I'], d_sd['E'], d_sd['I'], d_sd['E'], d_sd['I']]] 
                    
    syn=neuroml.ExpCurrSynapse(id='exp_curr_syn_all',tau_syn=0.5)
    
    nml_doc.exp_curr_synapses.append(syn)
                    
    syn_id_matrix=[['exp_curr_syn_all']]       # utils method build_probability_based_connectivity will assume that the same synapse model is shared by all projections; 
                                               # however, it must be inside 'list' because generically each physical projection might contain multiple synaptic components.
                                               
           
    #### get in-degrees for all connections in the full scale model according to scaling.py in the original project
    
    for source_pop in pop_ids:
    
        for target_pop in pop_ids:
        
            n_target = popDictFull[target_pop][0]
            
            n_source = popDictFull[source_pop][0]
            
            K_full[pop_ids.index(target_pop)][pop_ids.index(source_pop)] = round(np.log(1. -
            conn_probs[pop_ids.index(target_pop)][pop_ids.index(source_pop)]) / np.log(
            (n_target * n_source - 1.) / (n_target * n_source))) / n_target
    
    input_params ={'L23_E':[{'InputType':'GenerateSpikeSourcePoisson',
                             'InputName':"EXT_L23_E",
                             'AverageRateList':[],
                             'DurationList':[],
                             'DelayList':[],
                             'WeightList':[],
                             'Synapse':'exp_curr_syn_all',
                             'RateUnits':'Hz',
                             'TimeUnits':'ms',
                             'FractionToTarget':1.0,
                             'LocationSpecific':False,
                             'TargetDict':{None:1} },
                             
                            {'InputType':'PulseGenerators',
                             'InputName':"Ext_L23_E",
                             'Noise':False,
                             'AmplitudeList':[],
                             'DurationList':[],
                             'DelayList':[],
                             'TimeUnits':'ms',
                             'AmplitudeUnits':'nA',
                             'FractionToTarget':1.0,
                             'LocationSpecific':False,
                             'TargetDict':{None:1}  }],
                     
                   'L23_I':[{'InputType':'GenerateSpikeSourcePoisson',
                             'InputName':"EXT_L23_I",
                             'AverageRateList':[],
                             'DurationList':[],
                             'DelayList':[],
                             'WeightList':[],
                             'Synapse':'exp_curr_syn_all',
                             'RateUnits':'Hz',
                             'TimeUnits':'ms',
                             'FractionToTarget':1.0,
                             'LocationSpecific':False,
                             'TargetDict':{None:1} },
                             
                            {'InputType':'PulseGenerators',
                             'InputName':"Ext_L23_I",
                             'Noise':False,
                             'AmplitudeList':[],
                             'DurationList':[],
                             'DelayList':[],
                             'TimeUnits':'ms',
                             'AmplitudeUnits':'nA',
                             'FractionToTarget':1.0,
                             'LocationSpecific':False,
                             'TargetDict':{None:1}  }],
                     
                   'L4_E':[ {'InputType':'GenerateSpikeSourcePoisson',
                             'InputName':"EXT_L4_E",
                             'AverageRateList':[],
                             'DurationList':[],
                             'DelayList':[],
                             'WeightList':[],
                             'Synapse':'exp_curr_syn_all',
                             'RateUnits':'Hz',
                             'TimeUnits':'ms',
                             'FractionToTarget':1.0,
                             'LocationSpecific':False,
                             'TargetDict':{None:1} },
                             
                            {'InputType':'PulseGenerators',
                             'InputName':"Ext_L4_E",
                             'Noise':False,
                             'AmplitudeList':[],
                             'DurationList':[],
                             'DelayList':[],
                             'TimeUnits':'ms',
                             'AmplitudeUnits':'nA',
                             'FractionToTarget':1.0,
                             'LocationSpecific':False,
                             'TargetDict':{None:1}  }], 
                     
                   'L4_I':[ {'InputType':'GenerateSpikeSourcePoisson',
                             'InputName':"EXT_L4_I",
                             'AverageRateList':[],
                             'DurationList':[],
                             'DelayList':[],
                             'WeightList':[],
                             'Synapse':'exp_curr_syn_all',
                             'RateUnits':'Hz',
                             'TimeUnits':'ms',
                             'FractionToTarget':1.0,
                             'LocationSpecific':False,
                             'TargetDict':{None:1}},
                             
                            {'InputType':'PulseGenerators',
                             'InputName':"Ext_L4_I",
                             'Noise':False,
                             'AmplitudeList':[0.0],
                             'DurationList':[0.0],
                             'DelayList':[0.0],
                             'TimeUnits':'ms',
                             'AmplitudeUnits':'nA',
                             'FractionToTarget':1.0,
                             'LocationSpecific':False,
                             'TargetDict':{None:1}  } ],
                     
                   'L5_E':[ {'InputType':'GenerateSpikeSourcePoisson',
                             'InputName':"EXT_L5_E",
                             'AverageRateList':[],
                             'DurationList':[],
                             'DelayList':[],
                             'WeightList':[],
                             'Synapse':'exp_curr_syn_all',
                             'RateUnits':'Hz',
                             'TimeUnits':'ms',
                             'FractionToTarget':1.0,
                             'LocationSpecific':False,
                             'TargetDict':{None:1}  },
                             
                            {'InputType':'PulseGenerators',
                             'InputName':"Ext_L5_E",
                             'Noise':False,
                             'AmplitudeList':[],
                             'DurationList':[],
                             'DelayList':[],
                             'TimeUnits':'ms',
                             'AmplitudeUnits':'nA',
                             'FractionToTarget':1.0,
                             'LocationSpecific':False,
                             'TargetDict':{None:1}  } ],
                             
                   'L5_I':[ {'InputType':'GenerateSpikeSourcePoisson',
                             'InputName':"EXT_L5_I",
                             'AverageRateList':[],
                             'DurationList':[],
                             'DelayList':[],
                             'WeightList':[],
                             'Synapse':'exp_curr_syn_all',
                             'RateUnits':'Hz',
                             'TimeUnits':'ms',
                             'FractionToTarget':1.0,
                             'LocationSpecific':False,
                             'TargetDict':{None:1} },
                             
                            {'InputType':'PulseGenerators',
                             'InputName':"Ext_L5_I",
                             'Noise':False,
                             'AmplitudeList':[],
                             'DurationList':[],
                             'DelayList':[],
                             'TimeUnits':'ms',
                             'AmplitudeUnits':'nA',
                             'FractionToTarget':1.0,
                             'LocationSpecific':False,
                             'TargetDict':{None:1}  } ],
                     
                   'L6_E':[ {'InputType':'GenerateSpikeSourcePoisson',
                             'InputName':"EXT_L6_E",
                             'AverageRateList':[],
                             'DurationList':[],
                             'DelayList':[],
                             'WeightList':[],
                             'Synapse':'exp_curr_syn_all',
                             'RateUnits':'Hz',
                             'TimeUnits':'ms',
                             'FractionToTarget':1.0,
                             'LocationSpecific':False,
                             'TargetDict':{None:1} },
                             
                            {'InputType':'PulseGenerators',
                             'InputName':"Ext_L6_E",
                             'Noise':False,
                             'AmplitudeList':[],
                             'DurationList':[],
                             'DelayList':[],
                             'TimeUnits':'ms',
                             'AmplitudeUnits':'nA',
                             'FractionToTarget':1.0,
                             'LocationSpecific':False,
                             'TargetDict':{None:1}  } ],    
                             
                   'L6_I':[ {'InputType':'GenerateSpikeSourcePoisson',
                             'InputName':"EXT_L6_I",
                             'AverageRateList':[],
                             'DurationList':[],
                             'DelayList':[],
                             'WeightList':[],
                             'Synapse':'exp_curr_syn_all',
                             'RateUnits':'Hz',
                             'TimeUnits':'ms',
                             'FractionToTarget':1.0,
                             'LocationSpecific':False,
                             'TargetDict':{None:1} },
                             
                            {'InputType':'PulseGenerators',
                             'InputName':"Ext_L6_I",
                             'Noise':False,
                             'AmplitudeList':[],
                             'DurationList':[],
                             'DelayList':[],
                             'TimeUnits':'ms',
                             'AmplitudeUnits':'nA',
                             'FractionToTarget':1.0,
                             'LocationSpecific':False,
                             'TargetDict':{None:1}  } ] }
    
    DC_amp = {}
    
    for target_pop_index in range(0,len(pop_ids)):
    
        if input_type == "DC":
           
           DC_amp[pop_ids[target_pop_index]] = bg_rate *K_ext[pop_ids[target_pop_index]] * w_mean * neuron_params['tau_syn_E'] / 1000.0
           
        else:
       
           DC_amp[pop_ids[target_pop_index]]= 0.0
           
    if K_scaling != 1 :
       
       for target_pop_index in range(0,len(pop_ids)):
           
           input_coefficient = 0
           
           for source_pop_index in range(0,len(pop_ids)):
               
               input_coefficient += conn_mean_w[target_pop_index][source_pop_index] * K_full[target_pop_index][source_pop_index] * full_mean_rates[pop_ids[source_pop_index]]
               
               conn_mean_w[target_pop_index][source_pop_index] = conn_mean_w[target_pop_index][source_pop_index] / np.sqrt(K_scaling)
               
           if input_type=="poisson":
           
              input_coefficient += w_ext*K_ext[pop_ids[target_pop_index]]*bg_rate
              
              K_ext[pop_ids[target_pop_index]] = K_ext[pop_ids[target_pop_index]]*K_scaling
              
           DC_amp[pop_ids[target_pop_index]] = 0.001 * neuron_params['tau_syn_E'] * \
          (1. - np.sqrt(K_scaling)) * input_coefficient + DC_amp[pop_ids[target_pop_index]]
          
       w_ext = w_ext / np.sqrt(K_scaling)
       
    input_params_final={}
              
    for target_pop_tag in input_params.keys(): 
    
        found_target_pop=False
        
        for pop_id in pop_params.keys():
            
            if target_pop_tag  in pop_id:
               
               found_target_pop=True
               
               break
           
        if found_target_pop and ( (target_pop_tag in which_pops_to_stimulate) or (which_pops_to_stimulate==[])):
        
           input_params_final[target_pop_tag]=[]
        
           for input_group_ind in range(0,len(input_params[target_pop_tag])):
               
               if (input_type=='DC' or K_scaling !=1) and input_params[target_pop_tag][input_group_ind]['InputType']=='PulseGenerators':
            
                  input_params[target_pop_tag][input_group_ind]['AmplitudeList'].append(DC_amp[target_pop_tag])
                  
                  input_params[target_pop_tag][input_group_ind]['DurationList'].append(duration)
                  
                  input_params[target_pop_tag][input_group_ind]['DelayList'].append(0.0)
                  
                  input_params_final[target_pop_tag].append(input_params[target_pop_tag][input_group_ind])
            
               if input_type=='poisson' and input_params[target_pop_tag][input_group_ind]['InputType']=='GenerateSpikeSourcePoisson':               
    
                  input_params[target_pop_tag][input_group_ind]['AverageRateList'].append(bg_rate*K_ext[target_pop_tag])
                  
                  input_params[target_pop_tag][input_group_ind]['WeightList'].append(w_ext)
                  
                  input_params[target_pop_tag][input_group_ind]['DurationList'].append(duration)
                  
                  input_params[target_pop_tag][input_group_ind]['DelayList'].append(0.0)
                  
                  input_params_final[target_pop_tag].append(input_params[target_pop_tag][input_group_ind])
                  
    if build_connections:
    
       proj_array=oc_utils.build_probability_based_connectivity(net=network,
                                                                pop_params=pop_params,
                                                                probability_matrix=conn_probs, 
                                                                synapse_matrix=syn_id_matrix,
                                                                weight_matrix=conn_mean_w, 
                                                                delay_matrix=conn_mean_delay,
                                                                tags_on_populations=pop_ids, 
                                                                std_weight_matrix=conn_std_w,
                                                                std_delay_matrix=conn_std_delay)
                                                                
    if build_inputs: 
                                                             
       input_list_array_final, input_synapse_list=oc_utils.build_inputs(nml_doc=nml_doc,
                                                                        net=network,
                                                                        population_params=pop_params,
                                                                        input_params=input_params_final,
                                                                        cached_dicts=None,
                                                                        path_to_cells=None,
                                                                        path_to_synapses=None)
                                                                                                                                      
       if thalamic_input:
    
          if thal_tuple[0] != 0:
          
             if duration > thal_params['start']:
             
                start_time="%f ms"%thal_params['start']
            
             else:
             
                start_time="0 ms"
       
             oc.add_spike_source_poisson(nml_doc, 
                                         id=thal_tuple[2], 
                                         start=start_time, 
                                         duration="%f ms"%thal_params['duration'], 
                                         rate="%f Hz"%thal_params['rate'])
          
             thalamus_pop = neuroml.Population(id='Thalamus', component=thal_tuple[2], size=thal_tuple[0] )
          
             network.populations.append(thalamus_pop)
          
             for target_pop in thal_params['C'].keys():
          
                 for pop_id in pop_params.keys():
                
                     if target_pop in pop_id:
          
                        oc.add_probabilistic_projection_list(net=network,
                                                             presynaptic_population=thalamus_pop, 
                                                             postsynaptic_population=pop_params[pop_id]['PopObj'], 
                                                             synapse_list=['exp_curr_syn_all'],  
                                                             connection_probability=thal_params['C'][target_pop],
                                                             delay = d_mean['E'],
                                                             weight = w_ext,
                                                             presynaptic_population_list=False,
                                                             std_delay=d_sd['E'],
                                                             std_weight=w_ext*w_rel)
          else:
       
             print("Note: thalamic_input is set to True but population was scaled down to zero, thus thalamic input will not be added.")  
                                                
    nml_file_name = '%s.net.nml'%network.id
    
    oc.save_network(nml_doc, nml_file_name, validate=True,max_memory=max_memory)
    
    lems_file_name=oc.generate_lems_simulation(nml_doc, 
                                               network, 
                                               nml_file_name, 
                                               duration =duration, 
                                               dt =dt,
                                               include_extra_lems_files=["PyNN.xml"],
                                               gen_plots_for_all_v = False,
                                               gen_plots_for_only_populations=pop_params.keys(),
                                               gen_saves_for_all_v = False,
                                               gen_saves_for_only_populations=pop_params.keys() )
    
    if simulator != None:                                          
                                               
       opencortex.print_comment_v("Starting simulation of %s.net.nml"%net_id)
                            
       oc.simulate_network(lems_file_name=lems_file_name,
                           simulator=simulator,
                           max_memory=max_memory)
                  
input_params_pulses={'TCR':[{'InputType':'PulseGenerators',
                     'Layer':'Thalamus',
                     'AmplitudeList':[20.0,-20.0],
                     'DurationList':[100.0,50.0],
                     'DelayList':[50.0,200.0],
                     'FractionToTarget':1.0,
                     'LocationSpecific':False,
                     'TargetDict':{'dendrite_group':2 }       }]              }
              
              
passed=oc_utils.check_inputs(input_params_pulses,popDict,"../NeuroML2/prototypes/Thalamocortical/")

if passed:
   print("Input parameters are specified correctly")
   oc_utils.build_inputs(nml_doc=nml_doc,net=network,pop_params=pop_params,input_params=input_params_pulses,path_to_nml2="../NeuroML2/prototypes/Thalamocortical/") 

nml_file_name = '%s.net.nml'%network.id
oc.save_network(nml_doc, nml_file_name, validate=True)

for syn_ind in range(0,len(synapseList)):

    synapseList[syn_ind]="TestTraubBuildFull/"+synapseList[syn_ind]+".synapse.nml"

oc.generate_lems_simulation(nml_doc, 
                            network, 
                            nml_file_name, 
                            duration =      300, 
                            dt =            0.025,
                            include_extra_files=synapseList)
                            
def RunColumnSimulation(net_id="TestRunColumn",
                        nml2_source_dir="../../../neuroConstruct/generatedNeuroML2/",
                        sim_config="TempSimConfig",
                        scale_cortex=0.1,
                        scale_thalamus=0.1,
                        cell_bodies_overlap=True,
                        cylindrical=True,
                        default_synaptic_delay=0.05,
                        gaba_scaling=1.0,
                        l4ss_ampa_scaling=1.0,
                        l5pyr_gap_scaling =1.0,
                        in_nrt_tcr_nmda_scaling =1.0,
                        pyr_ss_nmda_scaling=1.0,
                        deep_bias_current=-1,
                        include_gap_junctions=True,
                        which_cell_types_to_include='all',
                        dir_nml2="../../",
                        backgroundL5Rate=30, # Hz
                        backgroundL23Rate=30, # Hz
                        duration=300,
                        dt=0.025,
                        max_memory='1000M',
                        seed=1234,
                        simulator=None,
                        save_format='xml',
                        num_of_cylinder_sides=None):
              
                        
    popDictFull = {}
    
    ##############   Full model ##################################
    
    popDictFull['CG3D_L23PyrRS'] = (1000, 'L23','L23PyrRS','multi', occ.L23_PRINCIPAL_CELL)
    popDictFull['CG3D_L23PyrFRB']= (50,'L23','L23PyrFRB_varInit','multi', occ.L23_PRINCIPAL_CELL_2)
    
    popDictFull['CG3D_SupBask'] = (90, 'L23','SupBasket','multi', occ.L23_INTERNEURON)  # over both l23 & l4
    popDictFull['CG3D_SupAxAx'] = (90, 'L23','SupAxAx','multi', occ.L23_INTERNEURON_2)  # over both l23 & l4
    popDictFull['CG3D_SupLTS']= (90,'L23','SupLTSInter','multi', occ.L4_INTERNEURON)    # over both l23 & l4
    
    popDictFull['CG3D_L4SpinStell']= (240,'L4','L4SpinyStellate','multi', occ.L4_PRINCIPAL_CELL)
    
    popDictFull['CG3D_L5TuftIB'] = (800, 'L5','L5TuftedPyrIB','multi', occ.L5_PRINCIPAL_CELL)
    popDictFull['CG3D_L5TuftRS']= (200,'L5','L5TuftedPyrRS','multi', occ.L5_PRINCIPAL_CELL_2)     
    
    popDictFull['CG3D_L6NonTuftRS']= (500,'L6','L6NonTuftedPyrRS','multi', occ.L6_PRINCIPAL_CELL)
    
    popDictFull['CG3D_DeepAxAx']= (100,'L6','DeepAxAx','multi', occ.L5_INTERNEURON)     # over both l5 & l6
    popDictFull['CG3D_DeepBask']= (100,'L6','DeepBasket','multi', occ.L5_INTERNEURON_2)   # over both l5 & l6
    popDictFull['CG3D_DeepLTS']= (100,'L6','DeepLTSInter','multi', occ.L6_INTERNEURON)  # over both l5 & l6
    
    popDictFull['CG3D_nRT']= (100,'Thalamus','nRT','multi', occ.THALAMUS_1)
    popDictFull['CG3D_TCR']= (100,'Thalamus','TCR','multi', occ.THALAMUS_2)
    
    ###############################################################
    
    dir_to_cells=os.path.join(dir_nml2,"cells")
    
    dir_to_synapses=os.path.join(dir_nml2,"synapses")
    
    dir_to_gap_junctions=os.path.join(dir_nml2,"gapJunctions")
    
    popDict={}
    
    cell_model_list=[]
    
    cell_diameter_dict={}
    
    nml_doc, network = oc.generate_network(net_id,seed)
    
    for cell_population in popDictFull.keys():
    
        include_cell_population=False
        
        cell_model=popDictFull[cell_population][2]
    
        if which_cell_types_to_include=='all' or cell_model in which_cell_types_to_include:
        
           popDict[cell_population]=()

           if popDictFull[cell_population][1] !='Thalamus':
             
              popDict[cell_population]=(int(round(scale_cortex*popDictFull[cell_population][0])), 
                                        popDictFull[cell_population][1],
                                        popDictFull[cell_population][2],
                                        popDictFull[cell_population][3],
                                        popDictFull[cell_population][4])
                  
              cell_count=int(round(scale_cortex*popDictFull[cell_population][0]))
                
           else:
             
              popDict[cell_population]=(int(round(scale_thalamus*popDictFull[cell_population][0])),
                                        popDictFull[cell_population][1],
                                        popDictFull[cell_population][2],
                                        popDictFull[cell_population][3],
                                        popDictFull[cell_population][4])
                  
              cell_count=int(round(scale_thalamus*popDictFull[cell_population][0]))
           
           if cell_count !=0:
    
              include_cell_population=True
               
        if include_cell_population:
        
           cell_model_list.append(popDictFull[cell_population][2])
           
           cell_diameter=oc_build.get_soma_diameter(popDictFull[cell_population][2],dir_to_cell=dir_to_cells)
           
           if popDictFull[cell_population][2] not in cell_diameter_dict.keys():
           
              cell_diameter_dict[popDictFull[cell_population][2]]=cell_diameter
           
           
    cell_model_list_final=list(set(cell_model_list))
    
    opencortex.print_comment_v("This is a final list of cell model ids: %s"%cell_model_list_final)
    
    copy_nml2_from_source=False
    
    for cell_model in cell_model_list_final:
    
       if not os.path.exists(os.path.join(dir_to_cells,"%s.cell.nml"%cell_model)):
       
          copy_nml2_from_source=True
          
          break
           
    if copy_nml2_from_source:
       
       oc_build.copy_nml2_source(dir_to_project_nml2=dir_nml2,
                        primary_nml2_dir=nml2_source_dir,
                        electrical_synapse_tags=['Elect'],
                        chemical_synapse_tags=['.synapse.'],
                        extra_channel_tags=['cad'])
                        
       passed_includes_in_cells=oc_utils.check_includes_in_cells(dir_to_cells,cell_model_list_final,extra_channel_tags=['cad'])
       
       if not passed_includes_in_cells:
       
          opencortex.print_comment_v("Execution of RunColumn.py will terminate.")
  
          quit()
             
    for cell_model in cell_model_list_final:
        
        oc_build._add_cell_and_channels(nml_doc, os.path.join(dir_to_cells,"%s.cell.nml"%cell_model), cell_model, use_prototypes=False)
        
    t1=-0
    t2=-250
    t3=-250
    t4=-200.0
    t5=-300.0
    t6=-300.0
    t7=-200.0
    t8=-200.0

    boundaries={}

    boundaries['L1']=[0,t1]
    boundaries['L23']=[t1,t1+t2+t3]
    boundaries['L4']=[t1+t2+t3,t1+t2+t3+t4]
    boundaries['L5']=[t1+t2+t3+t4,t1+t2+t3+t4+t5]
    boundaries['L6']=[t1+t2+t3+t4+t5,t1+t2+t3+t4+t5+t6]
    boundaries['Thalamus']=[t1+t2+t3+t4+t5+t6+t7,t1+t2+t3+t4+t5+t6+t7+t8]
    
    xs = [0,500]
    zs = [0,500] 
    
    passed_pops=oc_utils.check_pop_dict_and_layers(pop_dict=popDict,boundary_dict=boundaries)
    
    if passed_pops:
    
       opencortex.print_comment_v("Population parameters were specified correctly.") 
       
       if cylindrical:
       
          pop_params=oc_utils.add_populations_in_cylindrical_layers(network,boundaries,popDict,radiusOfCylinder=250,cellBodiesOverlap=cell_bodies_overlap,
                                                                    cellDiameterArray=cell_diameter_dict,numOfSides=num_of_cylinder_sides)
                                                                 
       else:
                                                                 
          pop_params=oc_utils.add_populations_in_rectangular_layers(network,boundaries,popDict,xs,zs,cellBodiesOverlap=False,cellDiameterArray=cell_diameter_dict)
       
    else:
    
       opencortex.print_comment_v("Population parameters were specified incorrectly; execution of RunColumn.py will terminate.")
       
       quit() 
    
    src_files = os.listdir("./")
    
    if 'netConnList' in src_files:
    
       full_path_to_connectivity='netConnList'
       
    else:
    
       full_path_to_connectivity="../../../neuroConstruct/pythonScripts/netbuild/netConnList"
                   
    weight_params=[{'weight':gaba_scaling,'synComp':'GABAA','synEndsWith':[],'targetCellGroup':[]},
                   {'weight':l4ss_ampa_scaling,'synComp':'Syn_AMPA_L4SS_L4SS','synEndsWith':[],'targetCellGroup':[]},
                   {'weight':l5pyr_gap_scaling,'synComp':'Syn_Elect_DeepPyr_DeepPyr','synEndsWith':[],'targetCellGroup':['CG3D_L5']},
                   {'weight':in_nrt_tcr_nmda_scaling,'synComp':'NMDA','synEndsWith':["_IN","_DeepIN","_SupIN","_SupFS","_DeepFS","_SupLTS","_DeepLTS","_nRT","_TCR"],
                   'targetCellGroup':[]},
                   {'weight':pyr_ss_nmda_scaling,'synComp':'NMDA','synEndsWith':["_IN","_DeepIN","_SupIN","_SupFS","_DeepFS","_SupLTS","_DeepLTS","_nRT","_TCR"],
                   'targetCellGroup':[]}]
                   
    delay_params=[{'delay':default_synaptic_delay,'synComp':'all'}]

    passed_weight_params=oc_utils.check_weight_params(weight_params)
    
    passed_delay_params=oc_utils.check_delay_params(delay_params)
    
    if passed_weight_params and passed_delay_params:    
    
       opencortex.print_comment_v("Synaptic weight and delay parameters were specified correctly.")     
       
       ignore_synapses = []
       if not include_gap_junctions:
           ignore_synapses = ['Syn_Elect_SupPyr_SupPyr','Syn_Elect_CortIN_CortIN','Syn_Elect_L4SS_L4SS','Syn_Elect_DeepPyr_DeepPyr','Syn_Elect_nRT_nRT']
    
       all_synapse_components,projArray,cached_segment_dicts=oc_utils.build_connectivity(net=network,
                                                                                         pop_objects=pop_params,
                                                                                         path_to_cells=dir_to_cells,
                                                                                         full_path_to_conn_summary=full_path_to_connectivity,
                                                                                         pre_segment_group_info=[{'PreSegGroup':"distal_axon",'ProjType':'Chem'}],
                                                                                         synaptic_scaling_params=weight_params,
                                                                                         synaptic_delay_params=delay_params,
                                                                                         ignore_synapses=ignore_synapses)   
                                                                                         
    else:
       
       if not passed_weight_params:
       
          opencortex.print_comment_v("Synaptic weight parameters were specified incorrectly; execution of RunColumn.py will terminate.") 
          
       if not passed_delay_params:
       
          opencortex.print_comment_v("Synaptic delay parameters were specified incorrectly; execution of RunColumn.py will terminate.")
       
       quit()
    
    ############ for testing only; will add original specifications later ##############################################################
    
    if sim_config=="Testing1":
       
       input_params={'CG3D_L23PyrRS':[{'InputType':'GeneratePoissonTrains',
                          'InputName':'Poi_CG3D_L23PyrRS',
                          'TrainType':'transient',
                          'Synapse':'Syn_AMPA_SupPyr_SupPyr',
                          'AverageRateList':[200.0,150.0],
                          'RateUnits':'Hz',
                          'TimeUnits':'ms',
                          'DurationList':[100.0,50.0],
                          'DelayList':[50.0,200.0],
                          'FractionToTarget':1.0,
                          'LocationSpecific':False,
                          'TargetDict':{'soma_group':1 }       }]              }
                       
    ###################################################################################################################################
    
    if sim_config=="Testing2":
    
       input_params_final={'CG3D_L23PyrRS':[{'InputType':'PulseGenerators',
                           'InputName':"DepCurr_L23RS",
                           'Noise':True,
                           'SmallestAmplitudeList':[5.0E-5,1.0E-5],
                           'LargestAmplitudeList':[1.0E-4,2.0E-5],
                           'DurationList':[20000.0,20000.0],
                           'DelayList':[0.0,20000.0],
                           'TimeUnits':'ms',
                           'AmplitudeUnits':'uA',
                           'FractionToTarget':1.0,
                           'LocationSpecific':False,
                           'TargetDict':{'dendrite_group':1}             }]             } 
                     
    if sim_config=="TempSimConfig":
    
       input_params ={'CG3D_L23PyrRS':[{'InputType':'PulseGenerators',
                     'InputName':"DepCurr_L23RS",
                     'Noise':True,
                     'SmallestAmplitudeList':[5.0E-5],
                     'LargestAmplitudeList':[1.0E-4],
                     'DurationList':[20000.0],
                     'DelayList':[0.0],
                     'TimeUnits':'ms',
                     'AmplitudeUnits':'uA',
                     'FractionToTarget':1.0,
                     'LocationSpecific':False,
                     'UniversalTargetSegmentID':0,
                     'UniversalFractionAlong':0.5},
                     
                      {'InputType':'GeneratePoissonTrains',
                     'InputName':"BackgroundL23RS",
                     'TrainType':'persistent',
                     'Synapse':'Syn_AMPA_SupPyr_SupPyr',
                     'AverageRateList':[float(backgroundL23Rate)],
                     'RateUnits':'Hz',
                     'FractionToTarget':1.0,
                     'LocationSpecific':False,
                     'TargetDict':{'dendrite_group':100} },
                     
                     {'InputType':'GeneratePoissonTrains',
                     'InputName':"EctopicStimL23RS",
                     'TrainType':'persistent',
                     'Synapse':'SynForEctStim',
                     'AverageRateList':[0.1],
                     'RateUnits':'Hz',
                     'FractionToTarget':1.0,
                     'LocationSpecific':False,
                     'UniversalTargetSegmentID':143,
                     'UniversalFractionAlong':0.5} ],
                     
                     'CG3D_TCR':[{'InputType':'GeneratePoissonTrains',
                     'InputName':"EctopicStimTCR",
                     'TrainType':'persistent',
                     'Synapse':'SynForEctStim',
                     'AverageRateList':[1.0],
                     'RateUnits':'Hz',
                     'FractionToTarget':1.0,
                     'LocationSpecific':False,
                     'UniversalTargetSegmentID':269,
                     'UniversalFractionAlong':0.5},
                     
                     {'InputType':'GeneratePoissonTrains',
                     'InputName':"Input_20",
                     'TrainType':'persistent',
                     'Synapse':'Syn_AMPA_L6NT_TCR',
                     'AverageRateList':[50.0],
                     'RateUnits':'Hz',
                     'FractionToTarget':1.0,
                     'LocationSpecific':False,
                     'TargetDict':{'dendrite_group':100} }],  
                     
                     'CG3D_L23PyrFRB':[{'InputType':'GeneratePoissonTrains',
                     'InputName':"EctopicStimL23FRB",
                     'TrainType':'persistent',
                     'Synapse':'SynForEctStim',
                     'AverageRateList':[0.1],
                     'RateUnits':'Hz',
                     'FractionToTarget':1.0,
                     'LocationSpecific':False,
                     'UniversalTargetSegmentID':143,
                     'UniversalFractionAlong':0.5},
                     
                     {'InputType':'PulseGenerators',
                     'InputName':"DepCurr_L23FRB",
                     'Noise':True,
                     'SmallestAmplitudeList':[2.5E-4],
                     'LargestAmplitudeList':[3.5E-4],
                     'DurationList':[20000.0],
                     'DelayList':[0.0],
                     'TimeUnits':'ms',
                     'AmplitudeUnits':'uA',
                     'FractionToTarget':1.0,
                     'LocationSpecific':False,
                     'UniversalTargetSegmentID':0,
                     'UniversalFractionAlong':0.5} ], 
                     
                     'CG3D_L6NonTuftRS':[{'InputType':'GeneratePoissonTrains',
                     'InputName':"EctopicStimL6NT",
                     'TrainType':'persistent',
                     'Synapse':'SynForEctStim',
                     'AverageRateList':[1.0],
                     'RateUnits':'Hz',
                     'FractionToTarget':1.0,
                     'LocationSpecific':False,
                     'UniversalTargetSegmentID':95,
                     'UniversalFractionAlong':0.5},
                     
                     {'InputType':'PulseGenerators',
                     'InputName':"DepCurr_L6NT",
                     'Noise':True,
                     'SmallestAmplitudeList':[5.0E-5],
                     'LargestAmplitudeList':[1.0E-4],
                     'DurationList':[20000.0],
                     'DelayList':[0.0],
                     'TimeUnits':'ms',
                     'AmplitudeUnits':'uA',
                     'FractionToTarget':1.0,
                     'LocationSpecific':False,
                     'UniversalTargetSegmentID':0,
                     'UniversalFractionAlong':0.5} ],
                     
                     'CG3D_L4SpinStell':[{'InputType':'PulseGenerators',
                     'InputName':"DepCurr_L4SS",
                     'Noise':True,
                     'SmallestAmplitudeList':[5.0E-5],
                     'LargestAmplitudeList':[1.0E-4],
                     'DurationList':[20000.0],
                     'DelayList':[0.0],
                     'TimeUnits':'ms',
                     'AmplitudeUnits':'uA',
                     'FractionToTarget':1.0,
                     'LocationSpecific':False,
                     'UniversalTargetSegmentID':0,
                     'UniversalFractionAlong':0.5}],
                     
                     'CG3D_L5TuftIB':[{'InputType':'GeneratePoissonTrains',
                     'InputName':"BackgroundL5",
                     'TrainType':'persistent',
                     'Synapse':'Syn_AMPA_L5RS_L5Pyr',
                     'AverageRateList':[float(backgroundL5Rate)],
                     'RateUnits':'Hz',
                     'FractionToTarget':1.0,
                     'LocationSpecific':False,
                     'TargetDict':{'dendrite_group':100} },
                     
                     {'InputType':'GeneratePoissonTrains',
                     'InputName':"EctopicStimL5IB",
                     'TrainType':'persistent',
                     'Synapse':'SynForEctStim',
                     'AverageRateList':[1.0],
                     'RateUnits':'Hz',
                     'FractionToTarget':1.0,
                     'LocationSpecific':False,
                     'UniversalTargetSegmentID':119,
                     'UniversalFractionAlong':0.5},
                     
                      {'InputType':'PulseGenerators',
                     'InputName':"DepCurr_L5IB",
                     'Noise':True,
                     'SmallestAmplitudeList':[5.0E-5],
                     'LargestAmplitudeList':[1.0E-4],
                     'DurationList':[20000.0],
                     'DelayList':[0.0],
                     'TimeUnits':'ms',
                     'AmplitudeUnits':'uA',
                     'FractionToTarget':1.0,
                     'LocationSpecific':False,
                     'UniversalTargetSegmentID':0,
                     'UniversalFractionAlong':0.5} ],
                     
                     'CG3D_L5TuftRS':[{'InputType':'GeneratePoissonTrains',
                     'InputName':"EctopicStimL5RS",
                     'TrainType':'persistent',
                     'Synapse':'SynForEctStim',
                     'AverageRateList':[1.0],
                     'RateUnits':'Hz',
                     'FractionToTarget':1.0,
                     'LocationSpecific':False,
                     'UniversalTargetSegmentID':119,
                     'UniversalFractionAlong':0.5},
                     
                      {'InputType':'PulseGenerators',
                     'InputName':"DepCurr_L5RS",
                     'Noise':True,
                     'SmallestAmplitudeList':[5.0E-5],
                     'LargestAmplitudeList':[1.0E-4],
                     'DurationList':[20000.0],
                     'DelayList':[0.0],
                     'TimeUnits':'ms',
                     'AmplitudeUnits':'uA',
                     'FractionToTarget':1.0,
                     'LocationSpecific':False,
                     'UniversalTargetSegmentID':0,
                     'UniversalFractionAlong':0.5}  ]   }
                     
       input_params_final={}
       
       for pop_id in pop_params.keys():
          
           if pop_id in input_params.keys():
       
              input_params_final[pop_id]=input_params[pop_id]
                     
       if deep_bias_current >= 0:
    
          for cell_group in input_params_final.keys():
       
              for input_group in range(0,len(input_params_final[cell_group])):
              
                  check_type=input_params_final[cell_group][input_group]['InputType']=="PulseGenerators"
                  
                  check_group_1= cell_group=="CG3D_L5TuftIB"
                  
                  check_group_2=cell_group =="CG3D_L5TuftRS"
                  
                  check_group_3= cell_group =="CG3D_L6NonTuftRS"
           
                  if check_type and (check_group_1 or check_group_2 or check_group_3):
	    
	             opencortex.print_comment_v("Changing offset current in 'PulseGenerators' for %s to %f"%(cell_group, deep_bias_current))
	      
	             input_params_final[cell_group][input_group]['SmallestAmplitudeList']=[ (deep_bias_current-0.05)/1000 ] 
	              
	             input_params_final[cell_group][input_group]['LargestAmplitudeList']=[ (deep_bias_current+0.05)/1000 ]
                     
    input_list_array_final, input_synapse_list=oc_utils.build_inputs(nml_doc=nml_doc,
                                                                     net=network,
                                                                     population_params=pop_params,
                                                                     input_params=input_params_final,
                                                                     cached_dicts=cached_segment_dicts,
                                                                     path_to_cells=dir_to_cells,
                                                                     path_to_synapses=dir_to_synapses)
    
    ####################################################################################################################################
    
    for input_synapse in input_synapse_list:
    
        if input_synapse not in all_synapse_components:
        
           all_synapse_components.append(input_synapse)
           
    synapse_list=[]
    
    gap_junction_list=[]
        
    for syn_ind in range(0,len(all_synapse_components)):
    
        if 'Elect' not in all_synapse_components[syn_ind]:
        
           synapse_list.append(all_synapse_components[syn_ind])
        
           all_synapse_components[syn_ind]=os.path.join(net_id,all_synapse_components[syn_ind]+".synapse.nml")
           
        else:
        
           gap_junction_list.append(all_synapse_components[syn_ind])
        
           all_synapse_components[syn_ind]=os.path.join(net_id,all_synapse_components[syn_ind]+".nml")
           
    oc_build.add_synapses(nml_doc,dir_to_synapses,synapse_list,synapse_tag=True)
    
    oc_build.add_synapses(nml_doc,dir_to_gap_junctions,gap_junction_list,synapse_tag=False)
    
    nml_file_name = '%s.net.nml'%network.id
    validate=True
    if save_format=='hdf5':
        nml_file_name += '.h5'
        validate=False
        
    
    oc.save_network(nml_doc, nml_file_name, validate=validate,max_memory=max_memory, format=save_format)
    
    oc_build.remove_component_dirs(dir_to_project_nml2="%s"%network.id,list_of_cell_ids=cell_model_list_final,extra_channel_tags=['cad'])
    
    lems_file_name=oc.generate_lems_simulation(nml_doc, 
                                               network, 
                                               nml_file_name, 
                                               duration =duration, 
                                               dt =dt,
                                               include_extra_lems_files=all_synapse_components)
     
    if simulator != None:                                          
                                               
       opencortex.print_comment_v("Starting simulation of %s.net.nml"%net_id)
                            
       oc.simulate_network(lems_file_name=lems_file_name,
                           simulator=simulator,
                           max_memory=max_memory)