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)