def __init__(self,figname):
load_channels()
MML = MorphML({'temperature':CELSIUS})
## Figure 2G,H has 2013 (LTS) first, then 2010 (plateauing)
## 50pA injected if PG2010, else 100pA injected -- see in run() below
if '2010' in figname:
MML.readMorphMLFromFile('PG_aditya2010unified_neuroML_L1_L2_L3.xml',{})
self.figname = 'PG2010'
elif '2013' in figname:
MML.readMorphMLFromFile('PG_aditya2013unified_neuroML_L1_L2_L3.xml',{})
self.figname = 'PG2013'
else:
print "Give PG2010 or PG2013 as argument"
sys.exit(1)
self.libname = 'PG'
#MML.readMorphMLFromFile('PG_aditya2013_neuroML_L1_L2_L3.xml',{})
#self.libname = 'PG_LTS'
self.context = moose.PyMooseBase.getContext()
self.PGcell = self.context.deepCopy(self.context.pathToId('/library/'+self.libname),self.context.pathToId('/'),"PG")
self.PGsoma = moose.Compartment('/PG/soma_0')
self.soma_vm = self.setupTable('soma_vm', self.PGsoma,'Vm')
self.caconc_conc = self.setupChannelTable('Ca_mit_conc_Ca', moose.CaConc(self.PGsoma.path+'/Ca_mit_conc'),'Ca')
self.tca_Ik = self.setupChannelTable('TCa_Ik', moose.HHChannel(self.PGsoma.path+'/TCa_d'),'Ik')
self.na_rat_ms_X = self.setupChannelTable('Na_rat_ms_X',moose.HHChannel(self.PGsoma.path+"/Na_rat_ms"),"X")
self.na_rat_ms_Y = self.setupChannelTable('Na_rat_ms_Y',moose.HHChannel(self.PGsoma.path+"/Na_rat_ms"),"Y")
self.kdr_ms_X = self.setupChannelTable('KDR_ms_X',moose.HHChannel(self.PGsoma.path+"/KDR_ms"),"X")
self.ka_ms_X = self.setupChannelTable('KA_ms_X',moose.HHChannel(self.PGsoma.path+"/KA_ms"),"X")
self.tca_d_X = self.setupChannelTable('TCa_d_X',moose.HHChannel(self.PGsoma.path+"/TCa_d"),"X")
self.tca_d_Y = self.setupChannelTable('TCa_d_Y',moose.HHChannel(self.PGsoma.path+"/TCa_d"),"Y")
self.ih_cb_X = self.setupChannelTable('Ih_cb_X',moose.HHChannel(self.PGsoma.path+"/Ih_cb"),"X")
def __init__(self, figname):
load_channels()
MML = MorphML({'temperature': CELSIUS})
## Figure 2G,H has 2013 (LTS) first, then 2010 (plateauing)
## 50pA injected if PG2010, else 100pA injected -- see in run() below
if '2010' in figname:
MML.readMorphMLFromFile(
'PG_aditya2010unified_neuroML_L1_L2_L3.xml', {})
self.figname = 'PG2010'
elif '2013' in figname:
MML.readMorphMLFromFile(
'PG_aditya2013unified_neuroML_L1_L2_L3.xml', {})
self.figname = 'PG2013'
else:
print "Give PG2010 or PG2013 as argument"
sys.exit(1)
self.libname = 'PG'
#MML.readMorphMLFromFile('PG_aditya2013_neuroML_L1_L2_L3.xml',{})
#self.libname = 'PG_LTS'
self.context = moose.PyMooseBase.getContext()
self.PGcell = self.context.deepCopy(
self.context.pathToId('/library/' + self.libname),
self.context.pathToId('/'), "PG")
self.PGsoma = moose.Compartment('/PG/soma_0')
self.soma_vm = self.setupTable('soma_vm', self.PGsoma, 'Vm')
self.caconc_conc = self.setupChannelTable(
'Ca_mit_conc_Ca', moose.CaConc(self.PGsoma.path + '/Ca_mit_conc'),
'Ca')
self.tca_Ik = self.setupChannelTable(
'TCa_Ik', moose.HHChannel(self.PGsoma.path + '/TCa_d'), 'Ik')
self.na_rat_ms_X = self.setupChannelTable(
'Na_rat_ms_X', moose.HHChannel(self.PGsoma.path + "/Na_rat_ms"),
"X")
self.na_rat_ms_Y = self.setupChannelTable(
'Na_rat_ms_Y', moose.HHChannel(self.PGsoma.path + "/Na_rat_ms"),
"Y")
self.kdr_ms_X = self.setupChannelTable(
'KDR_ms_X', moose.HHChannel(self.PGsoma.path + "/KDR_ms"), "X")
self.ka_ms_X = self.setupChannelTable(
'KA_ms_X', moose.HHChannel(self.PGsoma.path + "/KA_ms"), "X")
self.tca_d_X = self.setupChannelTable(
'TCa_d_X', moose.HHChannel(self.PGsoma.path + "/TCa_d"), "X")
self.tca_d_Y = self.setupChannelTable(
'TCa_d_Y', moose.HHChannel(self.PGsoma.path + "/TCa_d"), "Y")
self.ih_cb_X = self.setupChannelTable(
'Ih_cb_X', moose.HHChannel(self.PGsoma.path + "/Ih_cb"), "X")
def __init__(self,
cellname,
pseudosynname,
libsynname,
synfactor,
Vclamp,
numsyns,
syntime,
stagger,
comp_name=None):
load_channels()
## synchan_activation_correction is for graded synapses -- not useful for this script
load_synapses(synchan_activation_correction)
MML = MorphML({'temperature': CELSIUS})
if cellname == 'PG':
#filename = 'PG_aditya2010_neuroML_L1_L2_L3.xml'
#filename = 'PG_aditya2012_neuroML_L1_L2_L3.xml'
#filename = 'PG_aditya2013_neuroML_L1_L2_L3.xml'
#cellname = 'PG_LTS'
## Choose one the two below
## set the ORN->PG and mitral->PG to 0.45 vs 1.25 nS for plateauing vs LTS
#filename = 'PG_aditya2010unified_neuroML_L1_L2_L3.xml' # plateauing i.e. non-LTS
filename = 'PG_aditya2013unified_neuroML_L1_L2_L3.xml' # LTS
self.somaName = 'soma_0'
elif cellname == 'mitral':
#filename = 'mitral_bbmit1993davison_neuroML_L1_L2_L3_mod.xml'
#filename = 'mitral_bbmit1993davison_neuroML_L1_L2_L3.xml'
filename = 'mitral_bbmit1993davison_neuroML_L1_L2_L3_mod_withspikeinit.xml'
#filename = 'mitral_bbmit1993davison_neuroML_TEST_L1_L2_L3.xml'
#filename = 'mitral_bbmit1993davisonMS_neuroML_L1_L2_L3.xml'
#filename = 'mitral_migliore_etal_2007.xml'
self.somaName = 'Seg0_soma_0'
elif cellname == 'granule':
filename = 'granule_granadityaMS2007_neuroML_L1_L2_L3.xml'
self.somaName = 'soma_0'
self.cellName = cellname
self.synName = libsynname
self.pseudoSynName = pseudosynname
self.synFactor = synfactor
self.Vclamp = Vclamp
self.numSyns = numsyns
self.synTime = syntime
self.stagger = stagger
self.compName = comp_name
if self.stagger:
self.synRUNTIME = self.synTime * (self.numSyns) + 2 * SETTLETIME
else:
self.synRUNTIME = self.synTime + 2 * SETTLETIME
self.cellsDict = MML.readMorphMLFromFile(filename, {})
self.context = moose.PyMooseBase.getContext()
self.cell = moose.Cell( self.context.deepCopy(\
self.context.pathToId('/library/'+cellname),self.context.pathToId('/'),cellname) )
self.soma = moose.Compartment('/' + self.cellName + '/' +
self.somaName)
self.somaVm = setupTable('soma_vm', self.soma, 'Vm')
if cellname == 'mitral':
## inject a current into tuft of mitral
tuftcomp = moose.Compartment('/' + self.cellName +
'/Seg0_glom_81_102')
tuftcomp.inject = injtuft
## inject a current into soma of mitral
self.soma.inject = injsoma
## if nerve shock, inject a sharp current into tuft compartments
if nerve_shock:
## We want a single sharp current injection at SETTLETIME, so cannot use soma.inject
## segment info - see MorphML_reader.py
## Only connect to 25 tuft compartments
for seginfo in (self.cellsDict[self.cellName].values()
)[0:num_tuft_comps_shock]:
## seginfo[5] is a list of potential synapses at this segment
if 'ORN_mitral' in seginfo[5]:
## wrap this segment, seginfo[0] is segment name
tuftcomp = moose.Compartment('/' + self.cellName +
'/' + seginfo[0])
## compartment, name_extn, start_time, duration, current (all SI)
setup_iclamp(tuftcomp, '_nerveshock',\
SETTLETIME, 1e-3, tuft_shock_inject)
self.spikeTableList = []
self.attachSpikeTables()
#printCellTree(self.cell)
if cellname == 'mitral':
## monitor Vm at the base of the tuft
tuftbase_seg = moose.Compartment('/'+self.cellName+\
#'/Seg0_tuftden_19_23') # for migliore and shepherd 2007 cell
'/Seg0_prim_dend_5_20') # tuft base
self.tuftBaseTable = setupTable('mitdendTable', tuftbase_seg, 'Vm')
## monitor Vm at secondary dendrite
dend_seg = moose.Compartment('/'+self.cellName+\
#'/Seg0_sec_dendp4_0_254') # at 43.86 microns from soma
#'/Seg0_sec_dendd3_0_204') # at 190.56 microns from soma
'/Seg0_sec_dendd4_2_269') # at 1004.47 microns from soma
self.secDendTable = setupTable('mitdendTable', dend_seg, 'Vm')
## monitor Vm in the tuft compartment
## same as current injection above
self.tuftTable = setupTable('mitTuftTable', tuftcomp, 'Vm')
def __init__(self, cellname, pseudosynname, libsynname,
synfactor, Vclamp, numsyns, syntime, stagger, comp_name=None):
load_channels()
## synchan_activation_correction is for graded synapses -- not useful for this script
load_synapses(synchan_activation_correction)
MML = MorphML({'temperature':CELSIUS})
if cellname == 'PG':
#filename = 'PG_aditya2010_neuroML_L1_L2_L3.xml'
#filename = 'PG_aditya2012_neuroML_L1_L2_L3.xml'
#filename = 'PG_aditya2013_neuroML_L1_L2_L3.xml'
#cellname = 'PG_LTS'
## Choose one the two below
## set the ORN->PG and mitral->PG to 0.45 vs 1.25 nS for plateauing vs LTS
#filename = 'PG_aditya2010unified_neuroML_L1_L2_L3.xml' # plateauing i.e. non-LTS
filename = 'PG_aditya2013unified_neuroML_L1_L2_L3.xml' # LTS
self.somaName = 'soma_0'
elif cellname == 'mitral':
#filename = 'mitral_bbmit1993davison_neuroML_L1_L2_L3_mod.xml'
#filename = 'mitral_bbmit1993davison_neuroML_L1_L2_L3.xml'
filename = 'mitral_bbmit1993davison_neuroML_L1_L2_L3_mod_withspikeinit.xml'
#filename = 'mitral_bbmit1993davison_neuroML_TEST_L1_L2_L3.xml'
#filename = 'mitral_bbmit1993davisonMS_neuroML_L1_L2_L3.xml'
#filename = 'mitral_migliore_etal_2007.xml'
self.somaName = 'Seg0_soma_0'
elif cellname == 'granule':
filename = 'granule_granadityaMS2007_neuroML_L1_L2_L3.xml'
self.somaName = 'soma_0'
self.cellName = cellname
self.synName = libsynname
self.pseudoSynName = pseudosynname
self.synFactor = synfactor
self.Vclamp = Vclamp
self.numSyns = numsyns
self.synTime = syntime
self.stagger = stagger
self.compName = comp_name
if self.stagger:
self.synRUNTIME = self.synTime*(self.numSyns)+2*SETTLETIME
else:
self.synRUNTIME = self.synTime+2*SETTLETIME
self.cellsDict = MML.readMorphMLFromFile(filename,{})
self.context = moose.PyMooseBase.getContext()
self.cell = moose.Cell( self.context.deepCopy(\
self.context.pathToId('/library/'+cellname),self.context.pathToId('/'),cellname) )
self.soma = moose.Compartment('/'+self.cellName+'/'+self.somaName)
self.somaVm = setupTable('soma_vm', self.soma,'Vm')
if cellname=='mitral':
## inject a current into tuft of mitral
tuftcomp = moose.Compartment('/'+self.cellName+'/Seg0_glom_81_102')
tuftcomp.inject = injtuft
## inject a current into soma of mitral
self.soma.inject = injsoma
## if nerve shock, inject a sharp current into tuft compartments
if nerve_shock:
## We want a single sharp current injection at SETTLETIME, so cannot use soma.inject
## segment info - see MorphML_reader.py
## Only connect to 25 tuft compartments
for seginfo in (self.cellsDict[self.cellName].values())[0:num_tuft_comps_shock]:
## seginfo[5] is a list of potential synapses at this segment
if 'ORN_mitral' in seginfo[5]:
## wrap this segment, seginfo[0] is segment name
tuftcomp = moose.Compartment('/'+self.cellName+'/'+seginfo[0])
## compartment, name_extn, start_time, duration, current (all SI)
setup_iclamp(tuftcomp, '_nerveshock',\
SETTLETIME, 1e-3, tuft_shock_inject)
self.spikeTableList = []
self.attachSpikeTables()
#printCellTree(self.cell)
if cellname=='mitral':
## monitor Vm at the base of the tuft
tuftbase_seg = moose.Compartment('/'+self.cellName+\
#'/Seg0_tuftden_19_23') # for migliore and shepherd 2007 cell
'/Seg0_prim_dend_5_20') # tuft base
self.tuftBaseTable = setupTable('mitdendTable',tuftbase_seg,'Vm')
## monitor Vm at secondary dendrite
dend_seg = moose.Compartment('/'+self.cellName+\
#'/Seg0_sec_dendp4_0_254') # at 43.86 microns from soma
#'/Seg0_sec_dendd3_0_204') # at 190.56 microns from soma
'/Seg0_sec_dendd4_2_269') # at 1004.47 microns from soma
self.secDendTable = setupTable('mitdendTable',dend_seg,'Vm')
## monitor Vm in the tuft compartment
## same as current injection above
self.tuftTable = setupTable('mitTuftTable',tuftcomp,'Vm')
