def __init__(self, cellname):
self.context = moose.PyMooseBase.getContext()
self.cellname = cellname
load_channels()
load_synapses(synchan_activation_correction)
MML = MorphML({'temperature': CELSIUS})
if cellname == 'PG':
filename = 'PG_aditya2010_neuroML_L1_L2_L3.xml'
self.ORNCellSynapse = 'ORN_PG'
self.somaName = 'soma_0'
self.numORNSyns = NUM_ORN_PG_SYNS
elif cellname == 'mitral':
filename = 'mitral_bbmit1993davison_neuroML_L1_L2_L3_mod_withspikeinit.xml'
self.ORNCellSynapse = 'ORN_mitral'
self.somaName = 'Seg0_soma_0'
self.numORNSyns = NUM_ORN_MITRAL_SYNS
self.cellSegmentDict = MML.readMorphMLFromFile(filename, {})
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.soma_vm = setupTable('soma_vm', self.soma, 'Vm')
if mpisize > 1:
self.soma_vm.stepMode = TAB_SPIKE # store only spikes
self.soma_vm.stepSize = -0.030 # that cross -30 mV
self.spikeTableList = []
self.attachORNs()
def __init__(self, cellname):
self.context = moose.PyMooseBase.getContext()
self.cellname = cellname
load_channels()
load_synapses(synchan_activation_correction)
MML = MorphML({'temperature':CELSIUS})
if cellname == 'PG':
filename = 'PG_aditya2010_neuroML_L1_L2_L3.xml'
self.ORNCellSynapse = 'ORN_PG'
self.somaName = 'soma_0'
self.numORNSyns = NUM_ORN_PG_SYNS
elif cellname == 'mitral':
filename = 'mitral_bbmit1993davison_neuroML_L1_L2_L3_mod_withspikeinit.xml'
self.ORNCellSynapse = 'ORN_mitral'
self.somaName = 'Seg0_soma_0'
self.numORNSyns = NUM_ORN_MITRAL_SYNS
self.cellSegmentDict = MML.readMorphMLFromFile(filename,{})
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.soma_vm = setupTable('soma_vm', self.soma,'Vm')
if mpisize>1:
self.soma_vm.stepMode = TAB_SPIKE # store only spikes
self.soma_vm.stepSize = -0.030 # that cross -30 mV
self.spikeTableList = []
self.attachORNs()
def __init__(self, OBfile, synchan_activation_correction,
tweaks, mpirank, uniquestr, granfilebase, resptuned=False, spiketable=False):
self.mpirank = mpirank
self.uniquestr = uniquestr
self.context = moose.PyMooseBase.getContext()
self.granfilebase = granfilebase
self.spiketable = spiketable
## netseed is between 'seed' and '_' / '.xml' in OBfile
## ensure there is an _ before .xml, to consider both endings.
OBfile_underscored = OBfile.replace('.xml','_.xml')
self.netseed = float((OBfile_underscored.split('seed')[1]).split('_')[0])
load_channels()
## synchan_activation_correction depends on SIMDT,
## hence passed all the way down to
## granule_mitral_GABA synapse from the top level
load_synapses(synchan_activation_correction)
self.cellSegmentDict = load_cells()
filename = OBfile
NML = NetworkML({'temperature':CELSIUS})
## Below returns populationDict = { 'populationname1':(cellname,{instanceid1:moosecell, ... }) , ... }
## and projectionDict = { 'projectionname1':(source,target,[(syn_name1,pre_seg_path,post_seg_path),...]) , ... }
(self.populationDict,self.projectionDict) = \
NML.readNetworkMLFromFile(filename,self.cellSegmentDict,params=tweaks)
if VARY_GRANS_RMP:
self.set_granules_rmp(gran_RMP_SD)
if VARY_PGS_RMP:
self.set_pgs_rmp(PG_RMP_SD) # let PGs vary also
self.set_different_pgs() # set cells as PG_LTS or PG
## Vary synapse/channel Gbar-s only after setting them as PG_LTS or PG above
self.set_pgs_input_conductances() # set input conductance based on PG type
#self.set_pgs_tca_var() # changes TCa for all PGs
## for PG_LTS, varying ELeak or TCa doesn't change RMP, varying KCa does
self.set_pgs_kca_var() # changes KCa for PG_LTSs
#### Granule baseline synapses and connected timetables are created
#### by the networkml reader from the network xml file.
#### But the firefiles need to be loaded into timetables.
self.connect_granule_baselines_to_files('granule_baseline')
#### mitral baseline synapses and connected timetables are created
#### by the networkml reader from the network xml file.
#### But the firefiles need to be loaded into timetables.
#print "Connecting 10000 files per mit"
## connect baseline inhibition to mitrals at lateral dendrites
#self.connect_granule_baselines_to_files('mitral_baseline')
## connect baseline inhibition to mitrals at tuft dendrites
#self.connect_granule_baselines_to_files('tuft_mitral')
#printCellTree(moose.Cell('granules_singles_0'))
# unordered dictionary self.populationDict['mitrals'][1] is
# renamed to a more friendly self.mitralTable
self.mitralTable = self.populationDict['mitrals'][1]
self.setupMitralRecordingTables()
#printNetTree()
print "OB Network from",OBfile,"loaded!"
def __init__(self, outputtype):
load_channels()
## synchan_activation_correction depends on SIMDT,
## hence passed all the way down to
## granule_mitral_GABA synapse from the top level
load_synapses(synchan_activation_correction)
MML = MorphML({'temperature':CELSIUS})
self.outputType = outputtype
self.cellList = []
## order matters, as self.cellname etc remain that in last iteration
for cellname in ['PG','mitral']:
if cellname == 'PG':
#filename = 'PG_aditya2010_neuroML_L1_L2_L3.xml'
filename = 'PG_aditya2012_neuroML_L1_L2_L3.xml'
self.ORNCellSynapse = 'ORN_PG'
self.somaName = 'soma_0'
self.numORNSyns = NUM_ORN_PG_SYNS
elif cellname == 'mitral':
#filename = 'mitral_bbmit1993davison_neuroML_L1_L2_L3_mod.xml'
filename = 'mitral_bbmit1993davison_neuroML_L1_L2_L3_mod_withspikeinit.xml'
#filename = 'mitral_migliore_etal_2007.xml'
self.ORNCellSynapse = 'ORN_mitral'
self.somaName = 'Seg0_soma_0'
self.numORNSyns = NUM_ORN_MITRAL_SYNS
self.cellname = cellname
## returns {cellname:segDict}
## where segDict = { segid1 : [ segname,(proximalx,proximaly,proximalz),
## (distalx,distaly,distalz),diameter,length,[potential_syn1, ... ] ] , ... }
cellDict = MML.readMorphMLFromFile(filename,{})
segDict = cellDict[cellname]
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.soma_vm = setupTable('soma_vm', self.soma,'Vm')
self.soma_vm.stepMode = TAB_SPIKE # store only spikes
self.soma_vm.stepSize = -0.020 # that cross -20 mV
self.spikeTableList = []
self.makeSynapses(segDict,self.cell)
self.attachORNs()
#printCellTree(self.cell)
#self.soma.inject = 1e-9 # Ampere
## 0th cell is PG, 1st cell is mitral
self.cellList.append((self.cell,self.soma,self.soma_vm,self.spikeTableList))
if self.outputType == 'both':
self.attachInhibition()
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')
try:
import moose
except ImportError:
print "ERROR: Could not import moose. Please add the directory containing moose.py in your PYTHONPATH"
import sys
sys.exit(1)
from pylab import *
SETTLETIME = 0.25 # s
RUNTIME = 1.0+SETTLETIME # s
SIMDT = 5e-6 #s
PLOTDT = 5e-6 #s
if __name__ == "__main__":
load_synapses(1.0)
idx = test_synapse_index
syn_name = synapse_names[idx]
context = moose.PyMooseBase.getContext()
soma = moose.Compartment('/soma')
soma.length = 1e-6 # m
soma.diameter = 1e-6 # m
soma.Cm = 0.01 * 3.14159 * soma.length * soma.diameter
synid = context.deepCopy(context.pathToId('/library/'+syn_name),soma.id,syn_name)
syn = moose.SynChan(synid)
#### connect the soma to the synapse
if syn.getField('mgblock')=='True': # If NMDA synapse based on mgblock, connect to mgblock
mgblock = moose.Mg_block(syn.path+'/mgblock')
compartment_connection = mgblock
def __init__(self,
OBfile,
synchan_activation_correction,
tweaks,
mpirank,
uniquestr,
granfilebase,
resptuned=False,
spiketable=False):
self.mpirank = mpirank
self.uniquestr = uniquestr
self.context = moose.PyMooseBase.getContext()
self.granfilebase = granfilebase
self.spiketable = spiketable
## netseed is between 'seed' and '_' / '.xml' in OBfile
## ensure there is an _ before .xml, to consider both endings.
OBfile_underscored = OBfile.replace('.xml', '_.xml')
self.netseed = float(
(OBfile_underscored.split('seed')[1]).split('_')[0])
load_channels()
## synchan_activation_correction depends on SIMDT,
## hence passed all the way down to
## granule_mitral_GABA synapse from the top level
load_synapses(synchan_activation_correction)
self.cellSegmentDict = load_cells()
filename = OBfile
NML = NetworkML({'temperature': CELSIUS})
## Below returns populationDict = { 'populationname1':(cellname,{instanceid1:moosecell, ... }) , ... }
## and projectionDict = { 'projectionname1':(source,target,[(syn_name1,pre_seg_path,post_seg_path),...]) , ... }
(self.populationDict,self.projectionDict) = \
NML.readNetworkMLFromFile(filename,self.cellSegmentDict,params=tweaks)
if VARY_GRANS_RMP:
self.set_granules_rmp(gran_RMP_SD)
if VARY_PGS_RMP:
self.set_pgs_rmp(PG_RMP_SD) # let PGs vary also
self.set_different_pgs() # set cells as PG_LTS or PG
## Vary synapse/channel Gbar-s only after setting them as PG_LTS or PG above
self.set_pgs_input_conductances(
) # set input conductance based on PG type
#self.set_pgs_tca_var() # changes TCa for all PGs
## for PG_LTS, varying ELeak or TCa doesn't change RMP, varying KCa does
self.set_pgs_kca_var() # changes KCa for PG_LTSs
#### Granule baseline synapses and connected timetables are created
#### by the networkml reader from the network xml file.
#### But the firefiles need to be loaded into timetables.
self.connect_granule_baselines_to_files('granule_baseline')
#### mitral baseline synapses and connected timetables are created
#### by the networkml reader from the network xml file.
#### But the firefiles need to be loaded into timetables.
#print "Connecting 10000 files per mit"
## connect baseline inhibition to mitrals at lateral dendrites
#self.connect_granule_baselines_to_files('mitral_baseline')
## connect baseline inhibition to mitrals at tuft dendrites
#self.connect_granule_baselines_to_files('tuft_mitral')
#printCellTree(moose.Cell('granules_singles_0'))
# unordered dictionary self.populationDict['mitrals'][1] is
# renamed to a more friendly self.mitralTable
self.mitralTable = self.populationDict['mitrals'][1]
self.setupMitralRecordingTables()
#printNetTree()
print "OB Network from", OBfile, "loaded!"
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')
import operator # has itemgetter() for sorted()
sys.path.extend(["..","../neuroml","../cells","../channels","../networks","../simulations"])
from neuroml_utils import *
from sim_utils import * # has build_tweaks()
from networkConstants import *
## these are needed to load mitral cell and get segids and synapse locations
from load_channels import *
from load_synapses import *
from load_cells import *
from simset_odor import * # has synchan_activation_correction
load_channels()
## synchan_activation_correction depends on SIMDT,
## hence passed all the way down to
## granule_mitral_GABA synapse from the top level
load_synapses(synchan_activation_correction)
## cellSegmentDict[<cellname>] = { segid1 : [ segname,(proximalx,proximaly,proximalz),
## (distalx,distaly,distalz),diameter,length,[potential_syn1, ... ] ] , ... }
cellSegmentDict = load_cells()
from pylab import *
## USAGE:
## python2.6 plot_mitral_connectivity_NetworkML.py <networkmlfile.xml>
## OR
## python2.6 plot_mitral_connectivity_NetworkML.py SYN_DECAY
## The plots are for weights and numbers of all exc/inh synapses on the central/lateral mitrals.
## The printing on the terminal gives weights located proximally vs distally from central mits.
TWOGLOMS = False#True # whether to check for mits 0,2 (True) or 0,1 (False)
INCLUDEMULTIS = True # whether to include multi-s in joint connectivity
try:
import moose
except ImportError:
print "ERROR: Could not import moose. Please add the directory containing moose.py in your PYTHONPATH"
import sys
sys.exit(1)
from pylab import *
SETTLETIME = 0.25 # s
RUNTIME = 1.0 + SETTLETIME # s
SIMDT = 5e-6 #s
PLOTDT = 5e-6 #s
if __name__ == "__main__":
load_synapses(1.0)
idx = test_synapse_index
syn_name = synapse_names[idx]
context = moose.PyMooseBase.getContext()
soma = moose.Compartment('/soma')
soma.length = 1e-6 # m
soma.diameter = 1e-6 # m
soma.Cm = 0.01 * 3.14159 * soma.length * soma.diameter
synid = context.deepCopy(context.pathToId('/library/' + syn_name), soma.id,
syn_name)
syn = moose.SynChan(synid)
#### connect the soma to the synapse
if syn.getField(
'mgblock'