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)
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]
pop_params=oc_utils.add_populations_in_layers(network,boundaries,popDict,xs,zs)
#extra_params=[{'pre':'L23PyrRS','post':'SupBasket','weights':[0.05],'delays':[5],'synComps':['NMDA']}]
synapseList,projArray=oc_utils.build_connectivity(network,pop_params,"../NeuroML2/prototypes/Thalamocortical/",'../NeuroML2/prototypes/Thalamocortical/netConnList')
oc.add_synapses(nml_doc,'../NeuroML2/prototypes/Thalamocortical/',synapseList)
input_params={'TCR':[{'InputType':'GeneratePoissonTrains',
'Layer':'Thalamus',
'TrainType':'transient',
'Synapse':'Syn_AMPA_L6NT_TCR',
'AverageRateList':[200.0,150.0],
'DurationList':[100.0,50.0],
'DelayList':[50.0,200.0],
'FractionToTarget':1.0,
'LocationSpecific':False,
'TargetDict':{'dendrite_group':2 } }] }
input_params_pulses={'TCR':[{'InputType':'PulseGenerators',
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 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)
