def generate(reference = "SimpleNet",
scale=1,
format='xml'):
population_size = scale_pop_size(3,scale)
nml_doc, network = oc.generate_network(reference)
oc.add_cell_and_channels(nml_doc, 'izhikevich/RS.cell.nml','RS')
pop = oc.add_population_in_rectangular_region(network,
'RS_pop',
'RS',
population_size,
0,0,0,
100,100,100)
syn = oc.add_exp_two_syn(nml_doc,
id="syn0",
gbase="2nS",
erev="0mV",
tau_rise="0.5ms",
tau_decay="10ms")
pfs = oc.add_poisson_firing_synapse(nml_doc,
id="poissonFiringSyn",
average_rate="50 Hz",
synapse_id=syn.id)
oc.add_inputs_to_population(network,
"Stim0",
pop,
pfs.id,
all_cells=True)
nml_file_name = '%s.net.nml'%network.id
oc.save_network(nml_doc,
nml_file_name,
validate=(format=='xml'),
format = format)
if format=='xml':
oc.generate_lems_simulation(nml_doc,
network,
nml_file_name,
duration = 500,
dt = 0.025)
def generate(reference="SimpleNet", scale=1, format='xml'):
population_size = scale_pop_size(3, scale)
nml_doc, network = oc.generate_network(reference)
oc.add_cell_and_channels(nml_doc, 'izhikevich/RS.cell.nml', 'RS')
pop = oc.add_population_in_rectangular_region(network, 'RS_pop', 'RS',
population_size, 0, 0, 0,
100, 100, 100)
syn = oc.add_exp_two_syn(nml_doc,
id="syn0",
gbase="2nS",
erev="0mV",
tau_rise="0.5ms",
tau_decay="10ms")
pfs = oc.add_poisson_firing_synapse(nml_doc,
id="poissonFiringSyn",
average_rate="50 Hz",
synapse_id=syn.id)
oc.add_inputs_to_population(network, "Stim0", pop, pfs.id, all_cells=True)
nml_file_name = '%s.net.nml' % network.id
oc.save_network(nml_doc,
nml_file_name,
validate=(format == 'xml'),
format=format)
if format == 'xml':
oc.generate_lems_simulation(nml_doc,
network,
nml_file_name,
duration=500,
dt=0.025)
'''
Generates a NeuroML 2 file with a LEMS file recording many details of the network
'''
import opencortex.build as oc
nml_doc, network = oc.generate_network("Recording")
##### Cells
oc.add_cell_and_channels(nml_doc, 'izhikevich/RS.cell.nml','RS')
oc.add_cell_and_channels(nml_doc, 'iaf/iaf.cell.nml','iaf')
oc.add_cell_and_channels(nml_doc, 'acnet2/pyr_4_sym_soma.cell.nml','pyr_4_sym_soma')
oc.add_cell_and_channels(nml_doc, 'acnet2/pyr_4_sym.cell.nml','pyr_4_sym')
xDim = 500
yDim = 100
zDim = 500
offset = 0
##### Synapses
synAmpa1 = oc.add_exp_two_syn(nml_doc, id="synAmpa1", gbase="1nS",
erev="0mV", tau_rise="0.5ms", tau_decay="10ms")
synAmpa2 = oc.add_exp_two_syn(nml_doc, id="synAmpa2", gbase="0.5nS",
erev="0mV", tau_rise="0.5ms", tau_decay="5ms")
t6=-300.0
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]
xs = [0,500]
zs = [0,500]
nml_doc, network = oc.generate_network("TestTraubBuild")
oc.add_cell_and_channels(nml_doc, '../NeuroML2/prototypes/Thalamocortical/L23PyrRS.cell.nml','L23PyrRS')
oc.add_cell_and_channels(nml_doc, '../NeuroML2/prototypes/Thalamocortical/SupBasket.cell.nml','SupBasket')
popObjs=oc.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.build_connectivity(network,popObjs,"Traub_conn_data.json","../NeuroML2/prototypes/Thalamocortical/",extra_params)
oc.add_synapses(nml_doc,'../NeuroML2/prototypes/Thalamocortical/',synapseList)
nml_file_name = '%s.net.nml'%network.id
oc.save_network(nml_doc, nml_file_name, validate=True)
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)
for testing purposes
'''
import opencortex.build as oc
nml_doc, network = oc.generate_network("Balanced")
scale = .4
min_pop_size = 3
def scale_pop_size(baseline):
return max(min_pop_size, int(baseline*scale))
oc.add_cell_and_channels(nml_doc, '../NeuroML2/prototypes/AllenInstituteCellTypesDB_HH/HH_464198958.cell.nml','HH_464198958')
oc.add_cell_and_channels(nml_doc, '../NeuroML2/prototypes/AllenInstituteCellTypesDB_HH/HH_471141261.cell.nml','HH_471141261')
#oc.add_cell_and_channels(nml_doc, '../NeuroML2/prototypes/BlueBrainProject_NMC/cADpyr229_L23_PC_5ecbf9b163_0_0.cell.nml', 'cADpyr229_L23_PC_5ecbf9b163_0_0')
xDim = 400
yDim = 500
zDim = 300
xs = -200
ys = -150
zs = 100
##### Synapses
synAmpa1 = oc.add_exp_two_syn(nml_doc, id="synAmpa1", gbase="1nS",
for pop_in_layer in range(0,len(popDictFull[cell_model])):
pop_in_layer_tuple=()
print round(scale*popDictFull[cell_model][pop_in_layer][0])
pop_in_layer_tuple=( int(round(scale*popDictFull[cell_model][pop_in_layer][0] )), popDictFull[cell_model][pop_in_layer][1] )
popDict[cell_model].append(pop_in_layer_tuple)
if int(round(scale*popDictFull[cell_model][pop_in_layer][0] )) !=0:
include_cell_model=True
if include_cell_model:
oc.add_cell_and_channels(nml_doc, '../NeuroML2/prototypes/Thalamocortical/%s.cell.nml'%cell_model,cell_model)
t1=-0
t2=-250
t3=-250
t4=-200.0
t5=-300.0
t6=-300.0
t7=-200.0
t8=-200.0
boundaries={}
def generate(reference = "Balanced",
num_bbp =1,
scalePops = 1,
scalex=1,
scaley=1,
scalez=1,
connections=True,
duration = 1000,
global_delay = 0,
format='xml'):
num_exc = scale_pop_size(80,scalePops)
num_inh = scale_pop_size(40,scalePops)
nml_doc, network = oc.generate_network(reference)
oc.add_cell_and_channels(nml_doc, 'AllenInstituteCellTypesDB_HH/HH_464198958.cell.nml','HH_464198958')
oc.add_cell_and_channels(nml_doc, 'AllenInstituteCellTypesDB_HH/HH_471141261.cell.nml','HH_471141261')
if num_bbp>0:
oc.add_cell_and_channels(nml_doc, 'BlueBrainProject_NMC/cADpyr229_L23_PC_5ecbf9b163_0_0.cell.nml', 'cADpyr229_L23_PC_5ecbf9b163_0_0')
xDim = 400*scalex
yDim = 500*scaley
zDim = 300*scalez
xs = -200
ys = -150
zs = 100
##### Synapses
synAmpa1 = oc.add_exp_two_syn(nml_doc, id="synAmpa1", gbase="1nS",
erev="0mV", tau_rise="0.5ms", tau_decay="5ms")
synGaba1 = oc.add_exp_two_syn(nml_doc, id="synGaba1", gbase="2nS",
erev="-80mV", tau_rise="1ms", tau_decay="20ms")
##### Input types
pfs1 = oc.add_poisson_firing_synapse(nml_doc,
id="psf1",
average_rate="150 Hz",
synapse_id=synAmpa1.id)
##### Populations
popExc = oc.add_population_in_rectangular_region(network,
'popExc',
'HH_464198958',
num_exc,
xs,ys,zs,
xDim,yDim,zDim)
popInh = oc.add_population_in_rectangular_region(network,
'popInh',
'HH_471141261',
num_inh,
xs,ys,zs,
xDim,yDim,zDim)
if num_bbp == 1:
popBBP = oc.add_single_cell_population(network,
'popBBP',
'cADpyr229_L23_PC_5ecbf9b163_0_0',
z=200)
elif num_bbp > 1:
popBBP = oc.add_population_in_rectangular_region(network,
'popBBP',
'cADpyr229_L23_PC_5ecbf9b163_0_0',
num_bbp,
xs,ys,zs,
xDim,yDim,zDim)
##### Projections
total_conns = 0
if connections:
proj = oc.add_probabilistic_projection(network, "proj0",
popExc, popExc,
synAmpa1.id, 0.3, delay = global_delay)
total_conns += len(proj.connection_wds)
proj = oc.add_probabilistic_projection(network, "proj1",
popExc, popInh,
synAmpa1.id, 0.5, delay = global_delay)
total_conns += len(proj.connection_wds)
proj = oc.add_probabilistic_projection(network, "proj3",
popInh, popExc,
synGaba1.id, 0.7, delay = global_delay)
total_conns += len(proj.connection_wds)
proj = oc.add_probabilistic_projection(network, "proj4",
popInh, popInh,
synGaba1.id, 0.5, delay = global_delay)
total_conns += len(proj.connection_wds)
if num_bbp>0:
proj = oc.add_probabilistic_projection(network, "proj5",
popExc, popBBP,
synAmpa1.id, 0.5, delay = global_delay)
total_conns += len(proj.connection_wds)
##### Inputs
oc.add_inputs_to_population(network, "Stim0",
popExc, pfs1.id,
all_cells=True)
##### Save NeuroML and LEMS Simulation files
if num_bbp != 1:
new_reference = 'Balanced_%scells_%sconns'%(num_bbp+num_exc+num_inh,total_conns)
network.id = new_reference
nml_doc.id = new_reference
nml_file_name = '%s.net.%s'%(network.id,'nml.h5' if format == 'hdf5' else 'nml')
oc.save_network(nml_doc,
nml_file_name,
validate=(format=='xml'),
format = format)
if format=='xml':
lems_file_name = oc.generate_lems_simulation(nml_doc, network,
nml_file_name,
duration = duration,
dt = 0.025)
else:
lems_file_name = None
return nml_doc, nml_file_name, lems_file_name
import opencortex.build as oc
population_size = 3
nml_doc, network = oc.generate_network("SimpleNet")
oc.add_cell_and_channels(nml_doc, '../NeuroML2/prototypes/izhikevich/RS.cell.nml','RS')
pop = oc.add_population_in_rectangular_region(network,
'RS_pop',
'RS',
population_size,
0,0,0,
100,100,100)
syn = oc.add_exp_two_syn(nml_doc,
id="syn0",
gbase="2nS",
erev="0mV",
tau_rise="0.5ms",
tau_decay="10ms")
pfs = oc.add_poisson_firing_synapse(nml_doc,
id="poissonFiringSyn",
average_rate="50 Hz",
synapse_id=syn.id)
oc.add_inputs_to_population(network,
"Stim0",
pop,
def generate(reference = "L23TraubDemo",
num_rs =2,
num_bask =2,
scalex=1,
scaley=1,
scalez=1,
connections=True,
poisson_inputs=True,
offset_curents=False,
global_delay = 0,
duration = 300,
segments_to_plot_record = {'pop_rs':[0],'pop_bask':[0]},
format='xml'):
nml_doc, network = oc.generate_network(reference)
#oc.add_cell_and_channels(nml_doc, 'acnet2/pyr_4_sym.cell.nml','pyr_4_sym')
oc.add_cell_and_channels(nml_doc, 'Thalamocortical/L23PyrRS.cell.nml','L23PyrRS')
oc.add_cell_and_channels(nml_doc, 'Thalamocortical/SupBasket.cell.nml','SupBasket')
xDim = 500*scalex
yDim = 200*scaley
zDim = 500*scalez
pop_rs = oc.add_population_in_rectangular_region(network,
'pop_rs',
'L23PyrRS',
num_rs,
0,0,0,
xDim,yDim,zDim)
pop_bask = oc.add_population_in_rectangular_region(network,
'pop_bask',
'SupBasket',
num_bask,
0,0,0,
xDim,yDim,zDim)
syn0 = oc.add_exp_two_syn(nml_doc,
id="syn0",
gbase="1nS",
erev="0mV",
tau_rise="0.5ms",
tau_decay="10ms")
syn1 = oc.add_exp_two_syn(nml_doc,
id="syn1",
gbase="2nS",
erev="0mV",
tau_rise="1ms",
tau_decay="15ms")
if poisson_inputs:
pfs = oc.add_poisson_firing_synapse(nml_doc,
id="poissonFiringSyn",
average_rate="150 Hz",
synapse_id=syn0.id)
oc.add_inputs_to_population(network,
"Stim0",
pop_rs,
pfs.id,
all_cells=True)
if offset_curents:
pg0 = oc.add_pulse_generator(nml_doc,
id="pg0",
delay="0ms",
duration="%sms"%duration,
amplitude="0.5nA")
oc.add_inputs_to_population(network,
"Stim0",
pop_rs,
pg0.id,
all_cells=True)
oc.add_inputs_to_population(network,
"Stim0",
pop_bask,
pg0.id,
all_cells=True)
total_conns = 0
if connections:
proj = oc.add_probabilistic_projection(network,
"proj0",
pop_rs,
pop_bask,
syn1.id,
0.3,
weight=0.05,
delay=global_delay)
if proj:
total_conns += len(proj.connection_wds)
if num_rs != 2 or num_bask!=2:
new_reference = '%s_%scells_%sconns'%(nml_doc.id,num_rs+num_bask,total_conns)
network.id = new_reference
nml_doc.id = new_reference
nml_file_name = '%s.net.%s'%(network.id,'nml.h5' if format == 'hdf5' else 'nml')
oc.save_network(nml_doc,
nml_file_name,
validate=(format=='xml'),
format = format)
if format=='xml':
gen_plots_for_quantities = {} # Dict with displays vs lists of quantity paths
gen_saves_for_quantities = {} # Dict with file names vs lists of quantity paths
for pop in segments_to_plot_record.keys():
pop_nml = network.get_by_id(pop)
if pop_nml is not None and pop_nml.size>0:
for i in range(int(pop_nml.size)):
gen_plots_for_quantities['Display_%s_%i_v'%(pop,i)] = []
gen_saves_for_quantities['Sim_%s.%s.%i.v.dat'%(nml_doc.id,pop,i)] = []
for seg in segments_to_plot_record[pop]:
quantity = '%s/%i/%s/%i/v'%(pop,i,pop_nml.component,seg)
gen_plots_for_quantities['Display_%s_%i_v'%(pop,i)].append(quantity)
gen_saves_for_quantities['Sim_%s.%s.%i.v.dat'%(nml_doc.id,pop,i)].append(quantity)
lems_file_name = oc.generate_lems_simulation(nml_doc, network,
nml_file_name,
duration = duration,
dt = 0.025,
gen_plots_for_all_v = False,
gen_plots_for_quantities = gen_plots_for_quantities,
gen_saves_for_all_v = False,
gen_saves_for_quantities = gen_saves_for_quantities)
else:
lems_file_name = None
return nml_doc, nml_file_name, lems_file_name
import opencortex.build as oc
nml_doc, network = oc.generate_network("IClamps")
oc.add_cell_and_channels(nml_doc, '../NeuroML2/prototypes/izhikevich/RS.cell.nml', 'RS')
oc.add_cell_and_channels(nml_doc, '../NeuroML2/prototypes/acnet2/pyr_4_sym_soma.cell.nml', 'pyr_4_sym_soma')
#oc.add_cell_and_channels(nml_doc, '../NeuroML2/prototypes/BlueBrainProject_NMC/cADpyr229_L23_PC_5ecbf9b163_0_0.cell.nml', 'cADpyr229_L23_PC_5ecbf9b163_0_0')
popIzh = oc.add_single_cell_population(network,
'popIzh',
'RS')
popHH = oc.add_single_cell_population(network,
'popHH',
'pyr_4_sym_soma',
z=100)
'''
popBBP = oc.add_single_cell_population(network,
'popBBP',
'cADpyr229_L23_PC_5ecbf9b163_0_0',
z=200)'''
pgIzh = oc.add_pulse_generator(nml_doc,
id="pgIzh",
delay="100ms",
duration="300ms",
amplitude="0.7nA")
pgHH = oc.add_pulse_generator(nml_doc,
id="pgHH",
import opencortex.build as oc
population_size0 = 10
population_size1 = 10
nml_doc, network = oc.generate_network("SpikingNet")
oc.add_cell_and_channels(nml_doc, 'izhikevich/RS.cell.nml','RS')
pop_pre = oc.add_population_in_rectangular_region(network,
'pop_pre',
'RS',
population_size0,
0,0,0,
100,100,100)
pop_post = oc.add_population_in_rectangular_region(network,
'pop_post',
'RS',
population_size1,
0,100,0,
100,200,100)
syn0 = oc.add_exp_two_syn(nml_doc,
id="syn0",
gbase="1nS",
erev="0mV",
tau_rise="0.5ms",
tau_decay="10ms")
def generate(reference = "ACNet",
num_pyr = 48,
num_bask = 12,
scalex=1,
scaley=1,
scalez=1,
connections=True,
global_delay = 0,
duration = 300,
segments_to_plot_record = {'pop_pyr':[0],'pop_bask':[0]},
format='xml'):
nml_doc, network = oc.generate_network(reference)
oc.add_cell_and_channels(nml_doc, 'acnet2/pyr_4_sym.cell.nml','pyr_4_sym')
oc.add_cell_and_channels(nml_doc, 'acnet2/bask.cell.nml','bask')
xDim = 500*scalex
yDim = 50*scaley
zDim = 500*scalez
pop_pyr = oc.add_population_in_rectangular_region(network, 'pop_pyr',
'pyr_4_sym', num_pyr,
0,0,0, xDim,yDim,zDim)
pop_bask = oc.add_population_in_rectangular_region(network, 'pop_bask',
'bask', num_bask,
0,yDim,0, xDim,yDim+yDim,zDim)
ampa_syn = oc.add_exp_two_syn(nml_doc, id="AMPA_syn",
gbase="30e-9S", erev="0mV",
tau_rise="0.003s", tau_decay="0.0031s")
ampa_syn_inh = oc.add_exp_two_syn(nml_doc, id="AMPA_syn_inh",
gbase="0.15e-9S", erev="0mV",
tau_rise="0.003s", tau_decay="0.0031s")
gaba_syn = oc.add_exp_two_syn(nml_doc, id="GABA_syn",
gbase="0.6e-9S", erev="-0.080V",
tau_rise="0.005s", tau_decay="0.012s")
gaba_syn_inh = oc.add_exp_two_syn(nml_doc, id="GABA_syn_inh",
gbase="0S", erev="-0.080V",
tau_rise="0.003s", tau_decay="0.008s")
pfs = oc.add_poisson_firing_synapse(nml_doc, id="poissonFiringSyn",
average_rate="30 Hz", synapse_id=ampa_syn.id)
oc.add_inputs_to_population(network, "Stim0",
pop_pyr, pfs.id, all_cells=True)
total_conns = 0
if connections:
this_syn=ampa_syn.id
proj = oc.add_chem_projection0(nml_doc,
network,
"Proj_pyr_pyr",
pop_pyr,
pop_pyr,
targeting_mode='convergent',
synapse_list=[this_syn],
pre_segment_group = 'soma_group',
post_segment_group = 'dendrite_group',
number_conns_per_cell=7,
delays_dict = {this_syn:global_delay})
if proj:
total_conns += len(proj[0].connection_wds)
this_syn=ampa_syn_inh.id
proj = oc.add_chem_projection0(nml_doc,
network,
"Proj_pyr_bask",
pop_pyr,
pop_bask,
targeting_mode='convergent',
synapse_list=[this_syn],
pre_segment_group = 'soma_group',
post_segment_group = 'all',
number_conns_per_cell=21,
delays_dict = {this_syn:global_delay})
if proj:
total_conns += len(proj[0].connection_wds)
this_syn=gaba_syn.id
proj = oc.add_chem_projection0(nml_doc,
network,
"Proj_bask_pyr",
pop_bask,
pop_pyr,
targeting_mode='convergent',
synapse_list=[this_syn],
pre_segment_group = 'soma_group',
post_segment_group = 'all',
number_conns_per_cell=21,
delays_dict = {this_syn:global_delay})
if proj:
total_conns += len(proj[0].connection_wds)
this_syn=gaba_syn_inh.id
proj = oc.add_chem_projection0(nml_doc,
network,
"Proj_bask_bask",
pop_bask,
pop_bask,
targeting_mode='convergent',
synapse_list=[this_syn],
pre_segment_group = 'soma_group',
post_segment_group = 'all',
number_conns_per_cell=5,
delays_dict = {this_syn:global_delay})
if proj:
total_conns += len(proj[0].connection_wds)
if num_pyr != 48 or num_bask!=12:
new_reference = '%s_%scells_%sconns'%(nml_doc.id,num_pyr+num_bask,total_conns)
network.id = new_reference
nml_doc.id = new_reference
nml_file_name = '%s.net.%s'%(network.id,'nml.h5' if format == 'hdf5' else 'nml')
oc.save_network(nml_doc,
nml_file_name,
validate=(format=='xml'),
format = format)
if format=='xml':
gen_plots_for_quantities = {} # Dict with displays vs lists of quantity paths
gen_saves_for_quantities = {} # Dict with file names vs lists of quantity paths
for pop in segments_to_plot_record.keys():
pop_nml = network.get_by_id(pop)
if pop_nml is not None and pop_nml.size>0:
group = len(segments_to_plot_record[pop]) == 1
if group:
display = 'Display_%s_v'%(pop)
file_ = 'Sim_%s.%s.v.dat'%(nml_doc.id,pop)
gen_plots_for_quantities[display] = []
gen_saves_for_quantities[file_] = []
for i in range(int(pop_nml.size)):
if not group:
display = 'Display_%s_%i_v'%(pop,i)
file_ = 'Sim_%s.%s.%i.v.dat'%(nml_doc.id,pop,i)
gen_plots_for_quantities[display] = []
gen_saves_for_quantities[file_] = []
for seg in segments_to_plot_record[pop]:
quantity = '%s/%i/%s/%i/v'%(pop,i,pop_nml.component,seg)
gen_plots_for_quantities[display].append(quantity)
gen_saves_for_quantities[file_].append(quantity)
lems_file_name = oc.generate_lems_simulation(nml_doc, network,
nml_file_name,
duration = duration,
dt = 0.025,
gen_plots_for_all_v = False,
gen_plots_for_quantities = gen_plots_for_quantities,
gen_saves_for_all_v = False,
gen_saves_for_quantities = gen_saves_for_quantities)
else:
lems_file_name = None
return nml_doc, nml_file_name, lems_file_name
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)
