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")
Esempio n. 3
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    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')