def connectUsingSynChan(self, synName, prePath, post_path
, weight, threshold, delay
):
"""
Connect two compartments using SynChan
"""
postcomp = moose.Compartment(post_path)
# We usually try to reuse an existing SynChan - event based SynChans
# have an array of weights and delays and can represent multiple
# synapses i.e. a new element of the weights and delays array is
# created every time a 'synapse' message connects to the SynChan (from
# 'event' of spikegen) BUT for a graded synapse with a lookup table
# output connected to 'activation' message, not to 'synapse' message, we
# make a new synapse everytime ALSO for a saturating synapse i.e.
# KinSynChan, we always make a new synapse as KinSynChan is not meant to
# represent multiple synapses
libsyn = moose.SynChan(self.libraryPath+'/'+synName)
gradedchild = utils.get_child_Mstring(libsyn, 'graded')
# create a new synapse
if libsyn.className == 'KinSynChan' or gradedchild.value == 'True':
synNameFull = moose_methods.moosePath(synName
, utils.underscorize(prePath)
)
synObj = self.makeNewSynapse(synName, postcomp, synNameFull)
else:
# See debug/bugs for more details.
# NOTE: Change the debug/bugs to enable/disable this bug.
if bugs.BUG_NetworkML_500:
utils.dump("INFO"
, "See the code. There might be a bug here"
, frame = inspect.currentframe()
)
synNameFull = moose_methods.moosePath(synName
, utils.underscorize(prePath)
)
synObj = self.makeNewSynapse(synName, postcomp, synNameFull)
else: # If the above bug is fixed.
synNameFull = synName
if not moose.exists(post_path+'/'+synNameFull):
synObj = self.makeNewSynapse(synName, postcomp, synNameFull)
# wrap the synapse in this compartment
synPath = moose_methods.moosePath(post_path, synNameFull)
syn = moose.SynChan(synPath)
gradedchild = utils.get_child_Mstring(syn, 'graded')
# weights are set at the end according to whether the synapse is graded
# or event-based
# connect pre-comp Vm (if graded) OR spikegen/timetable (if event-based)
# to the synapse
# graded synapse
if gradedchild.value=='True':
table = moose.Table(syn.path+"/graded_table")
# always connect source to input - else 'cannot create message'
# error.
precomp = moose.Compartment(prePath)
self.connectWrapper(precomp, "VmOut", table, "msgInput")
# since there is no weight field for a graded synapse
# (no 'synapse' message connected),
# I set the Gbar to weight*Gbar
syn.Gbar = weight * syn.Gbar
# Event based synapse
else:
# synapse could be connected to spikegen at pre-compartment OR a
# file!
if 'file' not in prePath:
precomp = moose.Compartment(prePath)
if not moose.exists(prePath+'/IaF_spikegen'):
# if spikegen for this synapse doesn't exist in this
# compartment, create it spikegens for different synapse_types
# can have different thresholds
if not moose.exists(prePath+'/'+synName+'_spikegen'):
spikegen = moose.SpikeGen(prePath+'/'+synName+'_spikegen')
# spikegens for different synapse_types can have different
# thresholds
spikegen.threshold = threshold
# This ensures that spike is generated only on leading edge.
spikegen.edgeTriggered = 1
# usually events are raised at every time step that Vm >
# Threshold, can set either edgeTriggered as above or
# refractT
#spikegen.refractT = 0.25e-3
# wrap the spikegen in this compartment
spikegen = moose.SpikeGen(prePath+'/'+synName+'_spikegen')
else:
spikegen = moose.SpikeGen(prePath+'/IaF_spikegen')
# connect the spikegen to the synapse note that you need to use
# Synapse (auto-created) under SynChan to get/set weights ,
# addSpike-s etc. can get the Synapse element by
# moose.Synapse(syn.path+'/synapse') or syn.synapse Synpase is
# an array element, first add to it, to addSpike-s, get/set
# weights, etc.
syn.numSynapses += 1
m = self.connectSynapse(spikegen, syn)
else:
# if connected to a file, create a timetable,
# put in a field specifying the connected filenumbers to this segment,
# and leave it for simulation-time connection
## prePath is 'file[+<glomnum>]_<filenum1>[_<filenum2>...]' i.e. glomnum could be present
filesplit = prePath.split('+')
if len(filesplit) == 2:
glomsplit = filesplit[1].split('_', 1)
glomstr = '_'+glomsplit[0]
filenums = glomsplit[1]
else:
glomstr = ''
filenums = prePath.split('_', 1)[1]
tt_path = postcomp.path+'/'+synNameFull+glomstr+'_tt'
if not moose.exists(tt_path):
# if timetable for this synapse doesn't exist in this
# compartment, create it, and add the field 'fileNumbers'
tt = moose.TimeTable(tt_path)
tt.addField('fileNumbers')
tt.setField('fileNumbers',filenums)
# Be careful to connect the timetable only once while
# creating it as below: note that you need to use Synapse
# (auto-created) under SynChan to get/set weights ,
# addSpike-s etc. can get the Synapse element by
# moose.Synapse(syn.path+'/synapse') or syn.synapse Synpase
# is an array element, first add to it, to addSpike-s,
# get/set weights, etc.
syn.numSynapses += 1
m = self.connectSynapse(spikegen, syn.synapse)
else:
# if it exists, append file number to the field 'fileNumbers'
tt = moose.TimeTable(tt_path)
# append filenumbers from
# 'file[+<glomnum>]_<filenumber1>[_<filenumber2>...]'
filenums = moose_methods.moosePath(tt.getField('fileNumbers')
, filenums)
tt.setField('fileNumbers', filenums)
# syn.Gbar remains the same, but we play with the weight which is a
# factor to Gbar The delay and weight can be set only after
# connecting a spike event generator. delay and weight are arrays:
# multiple event messages can be connected to a single synapse first
# argument below is the array index, we connect to the latest
# synapse created above But KinSynChan ignores weight of the
# synapse, so set the Gbar for it
if libsyn.className == 'KinSynChan':
syn.Gbar = weight*syn.Gbar
else:
# note that you need to use Synapse (auto-created) under SynChan
# to get/set weights , addSpike-s etc. can get the Synpase
# element by moose.Synapse(syn.path+'/synapse') or syn.synapse
syn.synapse[-1].weight = weight
syn.synapse[-1].delay = delay # seconds
def connectUsingSynChan(self, synName, prePath, post_path, weight,
threshold, delay):
"""
Connect two compartments using SynChan
"""
postcomp = moose.Compartment(post_path)
# We usually try to reuse an existing SynChan - event based SynChans
# have an array of weights and delays and can represent multiple
# synapses i.e. a new element of the weights and delays array is
# created every time a 'synapse' message connects to the SynChan (from
# 'event' of spikegen) BUT for a graded synapse with a lookup table
# output connected to 'activation' message, not to 'synapse' message, we
# make a new synapse everytime ALSO for a saturating synapse i.e.
# KinSynChan, we always make a new synapse as KinSynChan is not meant to
# represent multiple synapses
libsyn = moose.SynChan(self.libraryPath + '/' + synName)
gradedchild = utils.get_child_Mstring(libsyn, 'graded')
# create a new synapse
if libsyn.className == 'KinSynChan' or gradedchild.value == 'True':
synNameFull = moose_methods.moosePath(synName,
utils.underscorize(prePath))
synObj = self.makeNewSynapse(synName, postcomp, synNameFull)
else:
# See debug/bugs for more details.
# NOTE: Change the debug/bugs to enable/disable this bug.
if bugs.BUG_NetworkML_500:
utils.dump("INFO",
"See the code. There might be a bug here",
frame=inspect.currentframe())
synNameFull = moose_methods.moosePath(
synName, utils.underscorize(prePath))
synObj = self.makeNewSynapse(synName, postcomp, synNameFull)
else: # If the above bug is fixed.
synNameFull = synName
if not moose.exists(post_path + '/' + synNameFull):
synObj = self.makeNewSynapse(synName, postcomp,
synNameFull)
# wrap the synapse in this compartment
synPath = moose_methods.moosePath(post_path, synNameFull)
syn = moose.SynChan(synPath)
gradedchild = utils.get_child_Mstring(syn, 'graded')
# weights are set at the end according to whether the synapse is graded
# or event-based
# connect pre-comp Vm (if graded) OR spikegen/timetable (if event-based)
# to the synapse
# graded synapse
if gradedchild.value == 'True':
table = moose.Table(syn.path + "/graded_table")
# always connect source to input - else 'cannot create message'
# error.
precomp = moose.Compartment(prePath)
self.connectWrapper(precomp, "VmOut", table, "msgInput")
# since there is no weight field for a graded synapse
# (no 'synapse' message connected),
# I set the Gbar to weight*Gbar
syn.Gbar = weight * syn.Gbar
# Event based synapse
else:
# synapse could be connected to spikegen at pre-compartment OR a
# file!
if 'file' not in prePath:
precomp = moose.Compartment(prePath)
if not moose.exists(prePath + '/IaF_spikegen'):
# if spikegen for this synapse doesn't exist in this
# compartment, create it spikegens for different synapse_types
# can have different thresholds
if not moose.exists(prePath + '/' + synName + '_spikegen'):
spikegen = moose.SpikeGen(prePath + '/' + synName +
'_spikegen')
# spikegens for different synapse_types can have different
# thresholds
spikegen.threshold = threshold
# This ensures that spike is generated only on leading edge.
spikegen.edgeTriggered = 1
# usually events are raised at every time step that Vm >
# Threshold, can set either edgeTriggered as above or
# refractT
#spikegen.refractT = 0.25e-3
# wrap the spikegen in this compartment
spikegen = moose.SpikeGen(prePath + '/' + synName +
'_spikegen')
else:
spikegen = moose.SpikeGen(prePath + '/IaF_spikegen')
# connect the spikegen to the synapse note that you need to use
# Synapse (auto-created) under SynChan to get/set weights ,
# addSpike-s etc. can get the Synapse element by
# moose.Synapse(syn.path+'/synapse') or syn.synapse Synpase is
# an array element, first add to it, to addSpike-s, get/set
# weights, etc.
syn.numSynapses += 1
m = self.connectSynapse(spikegen, syn)
else:
# if connected to a file, create a timetable,
# put in a field specifying the connected filenumbers to this segment,
# and leave it for simulation-time connection
## prePath is 'file[+<glomnum>]_<filenum1>[_<filenum2>...]' i.e. glomnum could be present
filesplit = prePath.split('+')
if len(filesplit) == 2:
glomsplit = filesplit[1].split('_', 1)
glomstr = '_' + glomsplit[0]
filenums = glomsplit[1]
else:
glomstr = ''
filenums = prePath.split('_', 1)[1]
tt_path = postcomp.path + '/' + synNameFull + glomstr + '_tt'
if not moose.exists(tt_path):
# if timetable for this synapse doesn't exist in this
# compartment, create it, and add the field 'fileNumbers'
tt = moose.TimeTable(tt_path)
tt.addField('fileNumbers')
tt.setField('fileNumbers', filenums)
# Be careful to connect the timetable only once while
# creating it as below: note that you need to use Synapse
# (auto-created) under SynChan to get/set weights ,
# addSpike-s etc. can get the Synapse element by
# moose.Synapse(syn.path+'/synapse') or syn.synapse Synpase
# is an array element, first add to it, to addSpike-s,
# get/set weights, etc.
syn.numSynapses += 1
m = self.connectSynapse(spikegen, syn.synapse)
else:
# if it exists, append file number to the field 'fileNumbers'
tt = moose.TimeTable(tt_path)
# append filenumbers from
# 'file[+<glomnum>]_<filenumber1>[_<filenumber2>...]'
filenums = moose_methods.moosePath(
tt.getField('fileNumbers'), filenums)
tt.setField('fileNumbers', filenums)
# syn.Gbar remains the same, but we play with the weight which is a
# factor to Gbar The delay and weight can be set only after
# connecting a spike event generator. delay and weight are arrays:
# multiple event messages can be connected to a single synapse first
# argument below is the array index, we connect to the latest
# synapse created above But KinSynChan ignores weight of the
# synapse, so set the Gbar for it
if libsyn.className == 'KinSynChan':
syn.Gbar = weight * syn.Gbar
else:
# note that you need to use Synapse (auto-created) under SynChan
# to get/set weights , addSpike-s etc. can get the Synpase
# element by moose.Synapse(syn.path+'/synapse') or syn.synapse
syn.synapse[-1].weight = weight
syn.synapse[-1].delay = delay # seconds
def addSegment(self, cellName, segnum, segment):
"""
Adding segment to cell.
"""
run_dia = 0.0
running_comp = None
running_dia_nums = 0
segmentname = segment.attrib['name']
debug.printDebug("DEBUG"
, "Adding segment {} in cell {}".format(segmentname, cellName)
)
# cable is an optional attribute. WARNING: Here I assume it is always
# present.
cableid = segment.attrib['cable']
segmentid = segment.attrib['id']
# Old cableid still running, hence don't start a new compartment, skip
# to next segment.
if cableid == self.running_cableid:
self.cellDictBySegmentId[cellName][1][segmentid] = running_comp
proximal = segment.find('./{'+self.mml+'}proximal')
if proximal is not None:
run_dia += float(proximal.attrib["diameter"])*self.length_factor
running_dia_nums += 1
distal = segment.find('./{'+self.mml+'}distal')
if distal is not None:
run_dia += float(distal.attrib["diameter"])*self.length_factor
running_dia_nums += 1
# new cableid starts, hence start a new compartment; also finish
# previous / last compartment.
else:
# Create a new compartment, the moose "hsolve" method assumes
# compartments to be asymmetric compartments and symmetrizes them
# but that is not what we want when translating from Neuron
# which has only symcompartments -- so be careful!
# just segmentname is NOT unique - eg: mitral bbmit exported from
# NEURON.
mooseCompname = moose_methods.moosePath(segmentname, segmentid)
mooseComppath = self.mooseCell.path + '/' + mooseCompname
mooseComp = moose.Compartment(mooseComppath)
self.cellDictBySegmentId[cellName][1][segmentid] = mooseComp
# Cables are grouped and densities set for cablegroups. Hence I
# need to refer to segment according to which cable they belong
# to..
self.cellDictByCableId[cellName][1][cableid] = mooseComp
self.running_cableid = cableid
running_segid = segmentid
running_comp = mooseComp
run_dia = 0.0
running_dia_nums = 0
if 'parent' in segment.attrib:
# I assume the parent is created before the child so that I can
# immediately # connect the child.
parentid = segment.attrib['parent']
parent = self.cellDictBySegmentId[cellName][1][parentid]
# It is always assumed that axial of parent is connected to
# raxial of moosesegment THIS IS WHAT GENESIS readcell()
# DOES!!! UNLIKE NEURON! THIS IS IRRESPECTIVE OF WHETHER
# PROXIMAL x,y,z OF PARENT = PROXIMAL x,y,z OF CHILD. THIS IS
# ALSO IRRESPECTIVE OF fraction_along_parent SPECIFIED IN
# CABLE! THUS THERE WILL BE NUMERICAL DIFFERENCES BETWEEN
# MOOSE/GENESIS and NEURON. moosesegment sends Ra and Vm to
# parent, parent sends only Vm actually for symmetric
# compartment, both parent and moosesegment require each
# other's Ra/2, but axial and raxial just serve to distinguish
# ends.
moose.connect(parent, 'axial', mooseComp, 'raxial')
else:
parent = None
proximal = segment.find('./{'+self.mml+'}proximal')
if proximal is None:
# If proximal tag is not present, then parent attribute MUST be
# present in the segment tag! if proximal is not present, then
# by default the distal end of the parent is the proximal end
# of the child
mooseComp.x0 = parent.x
mooseComp.y0 = parent.y
mooseComp.z0 = parent.z
else:
mooseComp.x0 = float(proximal.attrib["x"])*self.length_factor
mooseComp.y0 = float(proximal.attrib["y"])*self.length_factor
mooseComp.z0 = float(proximal.attrib["z"])*self.length_factor
run_dia += float(proximal.attrib["diameter"])*self.length_factor
running_dia_nums += 1
distal = segment.find('./{'+self.mml+'}distal')
if distal is not None:
run_dia += float(distal.attrib["diameter"]) * self.length_factor
running_dia_nums += 1
# finished creating new compartment Update the end position,
# diameter and length, and segDict of this comp/cable/section with
# each segment that is part of this cable (assumes contiguous
# segments in xml). This ensures that we don't have to do any
# 'closing ceremonies', if a new cable is encoutered in next
# iteration.
if distal is not None:
running_comp.x = float(distal.attrib["x"])*self.length_factor
running_comp.y = float(distal.attrib["y"])*self.length_factor
running_comp.z = float(distal.attrib["z"])*self.length_factor
# Set the compartment diameter as the average diameter of all the
# segments in this section
running_comp.diameter = run_dia / float(running_dia_nums)
# Set the compartment length
running_comp.length = math.sqrt(
(running_comp.x-running_comp.x0)**2+\
(running_comp.y-running_comp.y0)**2+\
(running_comp.z-running_comp.z0)**2
)
# NeuroML specs say that if (x0,y0,z0)=(x,y,z), then round
# compartment e.g. soma. In Moose set length = dia to give same
# surface area as sphere of dia.
if running_comp.length == 0.0:
running_comp.length = running_comp.diameter
# Set the segDict the empty list at the end below will get
# populated with the potential synapses on this segment, in
# function set_compartment_param(..)
self.segDict[running_segid] = [
running_comp.name
, (running_comp.x0 , running_comp.y0 , running_comp.z0)
, (running_comp.x,running_comp.y,running_comp.z)
, running_comp.diameter
, running_comp.length
, []
]
if neuroml_utils.neuroml_debug:
debug.printDebug(
"STEP"
, "Set up compartment/section %s" % running_comp.name
)
def addSegment(self, cellName, segnum, segment):
"""
Adding segment to cell.
"""
run_dia = 0.0
running_comp = None
running_dia_nums = 0
segmentname = segment.attrib['name']
debug.printDebug(
"DEBUG",
"Adding segment {} in cell {}".format(segmentname, cellName))
# cable is an optional attribute. WARNING: Here I assume it is always
# present.
cableid = segment.attrib['cable']
segmentid = segment.attrib['id']
# Old cableid still running, hence don't start a new compartment, skip
# to next segment.
if cableid == self.running_cableid:
self.cellDictBySegmentId[cellName][1][segmentid] = running_comp
proximal = segment.find('./{' + self.mml + '}proximal')
if proximal is not None:
run_dia += float(
proximal.attrib["diameter"]) * self.length_factor
running_dia_nums += 1
distal = segment.find('./{' + self.mml + '}distal')
if distal is not None:
run_dia += float(
distal.attrib["diameter"]) * self.length_factor
running_dia_nums += 1
# new cableid starts, hence start a new compartment; also finish
# previous / last compartment.
else:
# Create a new compartment, the moose "hsolve" method assumes
# compartments to be asymmetric compartments and symmetrizes them
# but that is not what we want when translating from Neuron
# which has only symcompartments -- so be careful!
# just segmentname is NOT unique - eg: mitral bbmit exported from
# NEURON.
mooseCompname = moose_methods.moosePath(segmentname, segmentid)
mooseComppath = self.mooseCell.path + '/' + mooseCompname
mooseComp = moose.Compartment(mooseComppath)
self.cellDictBySegmentId[cellName][1][segmentid] = mooseComp
# Cables are grouped and densities set for cablegroups. Hence I
# need to refer to segment according to which cable they belong
# to..
self.cellDictByCableId[cellName][1][cableid] = mooseComp
self.running_cableid = cableid
running_segid = segmentid
running_comp = mooseComp
run_dia = 0.0
running_dia_nums = 0
if 'parent' in segment.attrib:
# I assume the parent is created before the child so that I can
# immediately # connect the child.
parentid = segment.attrib['parent']
parent = self.cellDictBySegmentId[cellName][1][parentid]
# It is always assumed that axial of parent is connected to
# raxial of moosesegment THIS IS WHAT GENESIS readcell()
# DOES!!! UNLIKE NEURON! THIS IS IRRESPECTIVE OF WHETHER
# PROXIMAL x,y,z OF PARENT = PROXIMAL x,y,z OF CHILD. THIS IS
# ALSO IRRESPECTIVE OF fraction_along_parent SPECIFIED IN
# CABLE! THUS THERE WILL BE NUMERICAL DIFFERENCES BETWEEN
# MOOSE/GENESIS and NEURON. moosesegment sends Ra and Vm to
# parent, parent sends only Vm actually for symmetric
# compartment, both parent and moosesegment require each
# other's Ra/2, but axial and raxial just serve to distinguish
# ends.
moose.connect(parent, 'axial', mooseComp, 'raxial')
else:
parent = None
proximal = segment.find('./{' + self.mml + '}proximal')
if proximal is None:
# If proximal tag is not present, then parent attribute MUST be
# present in the segment tag! if proximal is not present, then
# by default the distal end of the parent is the proximal end
# of the child
mooseComp.x0 = parent.x
mooseComp.y0 = parent.y
mooseComp.z0 = parent.z
else:
mooseComp.x0 = float(proximal.attrib["x"]) * self.length_factor
mooseComp.y0 = float(proximal.attrib["y"]) * self.length_factor
mooseComp.z0 = float(proximal.attrib["z"]) * self.length_factor
run_dia += float(
proximal.attrib["diameter"]) * self.length_factor
running_dia_nums += 1
distal = segment.find('./{' + self.mml + '}distal')
if distal is not None:
run_dia += float(
distal.attrib["diameter"]) * self.length_factor
running_dia_nums += 1
# finished creating new compartment Update the end position,
# diameter and length, and segDict of this comp/cable/section with
# each segment that is part of this cable (assumes contiguous
# segments in xml). This ensures that we don't have to do any
# 'closing ceremonies', if a new cable is encoutered in next
# iteration.
if distal is not None:
running_comp.x = float(distal.attrib["x"]) * self.length_factor
running_comp.y = float(distal.attrib["y"]) * self.length_factor
running_comp.z = float(distal.attrib["z"]) * self.length_factor
# Set the compartment diameter as the average diameter of all the
# segments in this section
running_comp.diameter = run_dia / float(running_dia_nums)
# Set the compartment length
running_comp.length = math.sqrt(
(running_comp.x-running_comp.x0)**2+\
(running_comp.y-running_comp.y0)**2+\
(running_comp.z-running_comp.z0)**2
)
# NeuroML specs say that if (x0,y0,z0)=(x,y,z), then round
# compartment e.g. soma. In Moose set length = dia to give same
# surface area as sphere of dia.
if running_comp.length == 0.0:
running_comp.length = running_comp.diameter
# Set the segDict the empty list at the end below will get
# populated with the potential synapses on this segment, in
# function set_compartment_param(..)
self.segDict[running_segid] = [
running_comp.name,
(running_comp.x0, running_comp.y0, running_comp.z0),
(running_comp.x, running_comp.y, running_comp.z),
running_comp.diameter, running_comp.length, []
]
if neuroml_utils.neuroml_debug:
debug.printDebug(
"STEP",
"Set up compartment/section %s" % running_comp.name)