def loadGran98NeuroML_L123(filename, params):
neuromlR = NeuroML()
populationDict, projectionDict = \
neuromlR.readNeuroMLFromFile(filename,params=params)
print "Number of compartments =",\
len(moose.Neuron(populationDict['CA1group'][1][0].path).children)
soma_path = populationDict['CA1group'][1][0].path + '/Seg0_soma_0_0'
somaVm = setupTable('somaVm', moose.Compartment(soma_path), 'Vm')
#somaCa = setupTable('somaCa',moose.CaConc(soma_path+'/Gran_CaPool_98'),'Ca')
#somaIKCa = setupTable('somaIKCa',moose.HHChannel(soma_path+'/Gran_KCa_98'),'Gk')
#KDrX = setupTable('ChanX',moose.HHChannel(soma_path+'/Gran_KDr_98'),'X')
soma = moose.Compartment(soma_path)
print "Reinit MOOSE ... "
resetSim(['/elec', '/cells'], simdt, plotdt,
simmethod='hsolve') # from moose.utils
print "Running ... "
moose.start(runtime)
tvec = arange(0.0, runtime, simdt)
plot(tvec, somaVm.vector[1:])
title('Soma Vm')
xlabel('time (s)')
ylabel('Voltage (V)')
print "Showing plots ..."
show()
def loadXMLModel(self, filename, target):
"""Load a model in some XML format.
Looks inside the XML to figure out if this is a neuroML or an
SBML file and calls the corresponding loader functions.
Currently only SBML and neuroML are support. In future 9ml
support will be provided as the specification becomes stable.
"""
with open(filename, 'r') as xmlfile:
for line in xmlfile:
self._xmlreader.feed(line)
if self._saxhandler.model_type is not None:
break
ret = self._saxhandler.model_type
self._saxhandler.model_type = None
self._xmlreader.reset()
if ret == MooseHandler.type_neuroml:
#self._context.loadModel(filename, target)
neuromlR = NeuroML()
populationDict, projectionDict = neuromlR.readNeuroMLFromFile(filename)
elif ret == MooseHandler.type_sbml:
print 'Unsupported in GUI Mode'
return ret
def _loadElec(self, efile, elecname, combineSegments):
library = moose.Neutral('/library')
if (efile[len(efile) - 2:] == ".p"):
self.elecid = moose.loadModel(efile,
self.model.path + '/' + elecname)
else:
nm = NeuroML()
nm.readNeuroMLFromFile( efile, \
params = {'combineSegments': combineSegments, \
'createPotentialSynapses': True } )
if moose.exists('/cells'):
kids = moose.wildcardFind('/cells/#')
else:
kids = moose.wildcardFind(
'/library/#[ISA=Neuron],/library/#[TYPE=Neutral]')
if (kids[0].name == 'spine'):
kids = kids[1:]
assert (len(kids) > 0)
self.elecid = kids[0]
temp = moose.wildcardFind(self.elecid.path +
'/#[ISA=CompartmentBase]')
moose.move(self.elecid, self.model)
self.elecid.name = elecname
self._transformNMDAR(self.elecid.path)
kids = moose.wildcardFind('/library/##[0]')
for i in kids:
i.tick = -1
def loadGran98NeuroML_L123(filename):
neuromlR = NeuroML()
populationDict, projectionDict = \
neuromlR.readNeuroMLFromFile(filename)
soma_path = populationDict['Gran'][1][0].path + '/Soma_0'
somaVm = setupTable('somaVm', moose.Compartment(soma_path), 'Vm')
somaCa = setupTable('somaCa', moose.CaConc(soma_path + '/Gran_CaPool_98'),
'Ca')
somaIKCa = setupTable('somaIKCa',
moose.HHChannel(soma_path + '/Gran_KCa_98'), 'Gk')
#KDrX = setupTable('ChanX',moose.HHChannel(soma_path+'/Gran_KDr_98'),'X')
soma = moose.Compartment(soma_path)
print("Reinit MOOSE ... ")
resetSim(['/elec', '/cells'], simdt, plotdt,
simmethod='ee') # from moose.utils
print("Running ... ")
moose.start(runtime)
tvec = arange(0.0, runtime, plotdt)
plot(tvec, somaVm.vector[1:])
title('Soma Vm')
xlabel('time (s)')
ylabel('Voltage (V)')
figure()
plot(tvec, somaCa.vector[1:])
title('Soma Ca')
xlabel('time (s)')
ylabel('Ca conc (mol/m^3)')
figure()
plot(tvec, somaIKCa.vector[1:])
title('KCa current (A)')
xlabel('time (s)')
ylabel('')
print("Showing plots ...")
show()
def _loadElec( self, efile, elecname, combineSegments ):
library = moose.Neutral( '/library' )
if ( efile[ len( efile ) - 2:] == ".p" ):
self.elecid = moose.loadModel( efile, self.model.path + '/' + elecname )
else:
nm = NeuroML()
nm.readNeuroMLFromFile( efile, \
params = {'combineSegments': combineSegments, \
'createPotentialSynapses': True } )
if moose.exists( '/cells' ):
kids = moose.wildcardFind( '/cells/#' )
else:
kids = moose.wildcardFind( '/library/#[ISA=Neuron],/library/#[TYPE=Neutral]' )
if ( kids[0].name == 'spine' ):
kids = kids[1:]
assert( len( kids ) > 0 )
self.elecid = kids[0]
temp = moose.wildcardFind( self.elecid.path + '/#[ISA=CompartmentBase]' )
moose.move( self.elecid, self.model )
self.elecid.name = elecname
self._transformNMDAR( self.elecid.path )
kids = moose.wildcardFind( '/library/##[0]' )
for i in kids:
i.tick = -1
def loadGran98NeuroML_L123(filename, nogui=False):
neuromlR = NeuroML()
populationDict, projectionDict = neuromlR.readNeuroMLFromFile(filename)
soma_path = populationDict["Gran"][1][0].path + "/Soma_0"
somaVm = setupTable("somaVm", moose.Compartment(soma_path), "Vm")
somaCa = setupTable("somaCa", moose.CaConc(soma_path + "/Gran_CaPool_98"), "Ca")
somaIKCa = setupTable("somaIKCa", moose.HHChannel(soma_path + "/Gran_KCa_98"), "Gk")
soma = moose.Compartment(soma_path)
print ("Reinit MOOSE ... ")
resetSim(["/elec", "/cells"], simdt, plotdt, simmethod="ee") # from moose.utils
print ("Running ... ")
moose.start(runtime)
print ("Finished simulation of %s seconds" % runtime)
if not nogui:
from pylab import *
tvec = arange(0.0, runtime, plotdt)
plot(tvec, somaVm.vector[1:])
title("Soma Vm")
xlabel("time (s)")
ylabel("Voltage (V)")
figure()
plot(tvec, somaCa.vector[1:])
title("Soma Ca")
xlabel("time (s)")
ylabel("Ca conc (mol/m^3)")
figure()
plot(tvec, somaIKCa.vector[1:])
title("KCa current (A)")
xlabel("time (s)")
ylabel("")
print "Showing plots ..."
show()
def _loadElec(self, efile, elecname):
if (efile[len(efile) - 2:] == ".p"):
self.elecid = moose.loadModel(efile, '/library/' + elecname)[0]
print(self.elecid)
elif (efile[len(efile) - 4:] == ".swc"):
self.elecid = moose.loadModel(efile, '/library/' + elecname)[0]
else:
nm = NeuroML()
print("in _loadElec, combineSegments = ", self.combineSegments)
nm.readNeuroMLFromFile( efile, \
params = {'combineSegments': self.combineSegments, \
'createPotentialSynapses': True } )
if moose.exists('/cells'):
kids = moose.wildcardFind('/cells/#')
else:
kids = moose.wildcardFind(
'/library/#[ISA=Neuron],/library/#[TYPE=Neutral]')
if (kids[0].name == 'spine'):
kids = kids[1:]
assert (len(kids) > 0)
self.elecid = kids[0]
temp = moose.wildcardFind(self.elecid.path +
'/#[ISA=CompartmentBase]')
transformNMDAR(self.elecid.path)
kids = moose.wildcardFind('/library/##[0]')
for i in kids:
i.tick = -1
def loadGran98NeuroML_L123(filename):
neuromlR = NeuroML()
populationDict, projectionDict = \
neuromlR.readNeuroMLFromFile(filename)
soma_path = populationDict['Gran'][1][0].path+'/Soma_0'
somaVm = setupTable('somaVm',moose.Compartment(soma_path),'Vm')
somaCa = setupTable('somaCa',moose.CaConc(soma_path+'/Gran_CaPool_98'),'Ca')
somaIKCa = setupTable('somaIKCa',moose.HHChannel(soma_path+'/Gran_KCa_98'),'Gk')
## Am not able to plot KDr gating variable X when running under hsolve
#KDrX = setupTable('ChanX',moose.HHChannel(soma_path+'/Gran_KDr_98'),'X')
print "Reinit MOOSE ... "
resetSim(['/elec',cells_path], simdt, plotdt, simmethod='hsolve')
print "Running ... "
moose.start(runtime)
tvec = arange(0.0,runtime*2.0,plotdt)
tvec = tvec[ : somaVm.vector.size ]
plot(tvec,somaVm.vector)
title('Soma Vm')
xlabel('time (s)')
ylabel('Voltage (V)')
figure()
plot(tvec,somaCa.vector)
title('Soma Ca')
xlabel('time (s)')
ylabel('Ca conc (mol/m^3)')
figure()
plot(tvec,somaIKCa.vector)
title('soma KCa current')
xlabel('time (s)')
ylabel('KCa current (A)')
print "Showing plots ..."
show()
def runSTGNeuroML_L123(filename):
neuromlR = NeuroML()
populationDict, projectionDict = \
neuromlR.readNeuroMLFromFile(filename)
soma1_path = populationDict['AB_PD'][1][0].path+'/Soma_0'
soma1Vm = setupTable('somaVm',moose.Compartment(soma1_path),'Vm')
soma2_path = populationDict['LP'][1][0].path+'/Soma_0'
soma2Vm = setupTable('somaVm',moose.Compartment(soma2_path),'Vm')
soma3_path = populationDict['PY'][1][0].path+'/Soma_0'
soma3Vm = setupTable('somaVm',moose.Compartment(soma3_path),'Vm')
#somaCa = setupTable('somaCa',moose.CaConc(soma_path+'/CaPool_STG'),'Ca')
#somaIKCa = setupTable('somaIKCa',moose.HHChannel(soma_path+'/KCa_STG'),'Ik')
#somaCaE = setupTable('somaCaE',moose.HHChannel(soma_path+'/CaT_STG/nernst'),'E')
## Am not able to plot KDr gating variable X when running under hsolve
#KDrX = setupTable('ChanX',moose.HHChannel(soma_path+'/Gran_KDr_98'),'X')
print "Reinit MOOSE ... "
resetSim(['/elec',cells_path], simdt, plotdt, simmethod='hsolve')
print "Running ... "
moose.start(runtime)
tvec = arange(0.0,runtime+2*plotdt,plotdt)
tvec = tvec[ : soma1Vm.vec.size ]
plot(tvec,soma1Vm.vec,label='AB_PD',color='g',linestyle='dashed')
plot(tvec,soma2Vm.vec,label='LP',color='r',linestyle='solid')
plot(tvec,soma3Vm.vec,label='PY',color='b',linestyle='dashed')
legend()
title('Soma Vm')
xlabel('time (s)')
ylabel('Voltage (V)')
print "Showing plots ..."
show()
def loadGran98NeuroML_L123(filename):
neuromlR = NeuroML()
populationDict, projectionDict = \
neuromlR.readNeuroMLFromFile(filename)
soma_path = populationDict['Gran'][1][0].path+'/Soma_0'
somaVm = setupTable('somaVm',moose.Compartment(soma_path),'Vm')
somaCa = setupTable('somaCa',moose.CaConc(soma_path+'/Gran_CaPool_98'),'Ca')
somaIKCa = setupTable('somaIKCa',moose.HHChannel(soma_path+'/Gran_KCa_98'),'Gk')
#KDrX = setupTable('ChanX',moose.HHChannel(soma_path+'/Gran_KDr_98'),'X')
soma = moose.Compartment(soma_path)
print("Reinit MOOSE ... ")
resetSim(['/elec','/cells'],simdt,plotdt,simmethod='ee') # from moose.utils
print("Running ... ")
moose.start(runtime)
tvec = arange(0.0,runtime,plotdt)
plot(tvec,somaVm.vector[1:])
title('Soma Vm')
xlabel('time (s)')
ylabel('Voltage (V)')
figure()
plot(tvec,somaCa.vector[1:])
title('Soma Ca')
xlabel('time (s)')
ylabel('Ca conc (mol/m^3)')
figure()
plot(tvec,somaIKCa.vector[1:])
title('KCa current (A)')
xlabel('time (s)')
ylabel('')
print("Showing plots ...")
show()
def _loadElec( self, efile, elecname ):
if ( efile[ len( efile ) - 2:] == ".p" ):
self.elecid = moose.loadModel( efile, '/library/' + elecname)[0]
print self.elecid
elif ( efile[ len( efile ) - 4:] == ".swc" ):
self.elecid = moose.loadModel( efile, '/library/' + elecname)[0]
else:
nm = NeuroML()
print "in _loadElec, combineSegments = ", self.combineSegments
nm.readNeuroMLFromFile( efile, \
params = {'combineSegments': self.combineSegments, \
'createPotentialSynapses': True } )
if moose.exists( '/cells' ):
kids = moose.wildcardFind( '/cells/#' )
else:
kids = moose.wildcardFind( '/library/#[ISA=Neuron],/library/#[TYPE=Neutral]' )
if ( kids[0].name == 'spine' ):
kids = kids[1:]
assert( len( kids ) > 0 )
self.elecid = kids[0]
temp = moose.wildcardFind( self.elecid.path + '/#[ISA=CompartmentBase]' )
transformNMDAR( self.elecid.path )
kids = moose.wildcardFind( '/library/##[0]' )
for i in kids:
i.tick = -1
def loadGran98NeuroML_L123(filename):
neuromlR = NeuroML()
populationDict, projectionDict = \
neuromlR.readNeuroMLFromFile(filename)
soma_path = populationDict['Gran'][1][0].path+'/Soma_0'
somaVm = setupTable('somaVm',moose.Compartment(soma_path),'Vm')
somaCa = setupTable('somaCa',moose.CaConc(soma_path+'/Gran_CaPool_98'),'Ca')
somaIKCa = setupTable('somaIKCa',moose.HHChannel(soma_path+'/Gran_KCa_98'),'Gk')
## Am not able to plot KDr gating variable X when running under hsolve
#KDrX = setupTable('ChanX',moose.HHChannel(soma_path+'/Gran_KDr_98'),'X')
print "Reinit MOOSE ... "
resetSim(['/elec',cells_path], simdt, plotdt, simmethod='hsolve')
print "Running ... "
moose.start(runtime)
tvec = arange(0.0,runtime*2.0,plotdt)
tvec = tvec[ : somaVm.vector.size ]
plot(tvec,somaVm.vector)
title('Soma Vm')
xlabel('time (s)')
ylabel('Voltage (V)')
figure()
plot(tvec,somaCa.vector)
title('Soma Ca')
xlabel('time (s)')
ylabel('Ca conc (mol/m^3)')
figure()
plot(tvec,somaIKCa.vector)
title('soma KCa current')
xlabel('time (s)')
ylabel('KCa current (A)')
print "Showing plots ..."
show()
def loadGran98NeuroML_L123(filename):
neuromlR = NeuroML()
populationDict, projectionDict = \
neuromlR.readNeuroMLFromFile(filename)
soma_path = populationDict['CA1group'][1][0].path+'/Seg0_soma_0_0'
somaVm = setupTable('somaVm',moose.Compartment(soma_path),'Vm')
soma = moose.Compartment(soma_path)
moose.reinit()
moose.start(runtime)
tvec = np.arange(0.0,runtime,simdt)
res = count.spike_train_simple_stat( somaVm.vector )
return res['number of spikes']
def loadGran98NeuroML_L123(filename):
neuromlR = NeuroML()
populationDict, projectionDict = \
neuromlR.readNeuroMLFromFile(filename)
soma_path = populationDict['CA1group'][1][0].path + '/Seg0_soma_0_0'
somaVm = setupTable('somaVm', moose.Compartment(soma_path), 'Vm')
soma = moose.Compartment(soma_path)
moose.reinit()
moose.start(runtime)
tvec = np.arange(0.0, runtime, simdt)
res = count.spike_train_simple_stat(somaVm.vector)
return res['number of spikes']
def loadGran98NeuroML_L123(filename):
neuromlR = NeuroML()
populationDict, projectionDict = \
neuromlR.readNeuroMLFromFile(filename)
soma_path = populationDict['Gran'][1][0].path+'/Soma_0'
somaVm = setupTable('somaVm',moose.Compartment(soma_path),'Vm')
somaCa = setupTable('somaCa',moose.CaConc(soma_path+'/Gran_CaPool_98'),'Ca')
somaIKCa = setupTable('somaIKCa',moose.HHChannel(soma_path+'/Gran_KCa_98'),'Gk')
#KDrX = setupTable('ChanX',moose.HHChannel(soma_path+'/Gran_KDr_98'),'X')
soma = moose.Compartment(soma_path)
print "Reinit MOOSE ... "
resetSim(['/elec','/cells'],simdt,plotdt,simmethod='ee') # from moose.utils
print "Running ... "
moose.start(runtime)
print 'Finished simulation for', runtime, 'seconds'
def loadModel(filename):
global soma_, cellSpikeTable_
neuromlR = NeuroML()
neuromlR.readNeuroMLFromFile(filename)
libcell = moose.Neuron('/library/CA1soma')
CA1Cellid = moose.copy(libcell,moose.Neutral('/cells'),'CA1')
CA1Cell = moose.Neuron(CA1Cellid)
spikeGen = moose.SpikeGen(CA1Cell.path+'/spikeGen')
spikeGen.threshold = -30e-3 # V
soma_ = moose.Compartment(CA1Cell.path+'/Seg0_soma_0_0')
soma_.inject = 0 # by default the cell has a current injection
moose.connect(soma_,'VmOut',spikeGen,'Vm')
table_path = moose.Neutral(CA1Cell.path+'/data').path
cellSpikeTable_ = moose.Table(table_path+'/spikesTable')
moose.connect(spikeGen,'spikeOut', cellSpikeTable_,'input')
def loadGran98NeuroML_L123(filename,params):
neuromlR = NeuroML()
populationDict, projectionDict = \
neuromlR.readNeuroMLFromFile(filename,params=params)
print "Number of compartments =",\
len(moose.Neuron(populationDict['CA1group'][1][0].path).children)
soma_path = populationDict['CA1group'][1][0].path+'/Seg0_soma_0_0'
somaVm = setupTable('somaVm',moose.Compartment(soma_path),'Vm')
#somaCa = setupTable('somaCa',moose.CaConc(soma_path+'/Gran_CaPool_98'),'Ca')
#somaIKCa = setupTable('somaIKCa',moose.HHChannel(soma_path+'/Gran_KCa_98'),'Gk')
#KDrX = setupTable('ChanX',moose.HHChannel(soma_path+'/Gran_KDr_98'),'X')
soma = moose.Compartment(soma_path)
print "Reinit MOOSE ... "
resetSim(['/elec','/cells'],simdt,plotdt,simmethod='hsolve') # from moose.utils
print "Running ... "
moose.start(runtime)
tvec = arange(0.0,runtime,simdt)
def loadGran98NeuroML_L123(filename):
neuromlR = NeuroML()
populationDict, projectionDict = \
neuromlR.readNeuroMLFromFile(filename)
soma_path = populationDict['Gran'][1][0].path + '/Soma_0'
somaVm = setupTable('somaVm', moose.Compartment(soma_path), 'Vm')
somaCa = setupTable('somaCa', moose.CaConc(soma_path + '/Gran_CaPool_98'),
'Ca')
somaIKCa = setupTable('somaIKCa',
moose.HHChannel(soma_path + '/Gran_KCa_98'), 'Gk')
#KDrX = setupTable('ChanX',moose.HHChannel(soma_path+'/Gran_KDr_98'),'X')
soma = moose.Compartment(soma_path)
print "Reinit MOOSE ... "
resetSim(['/elec', '/cells'], simdt, plotdt,
simmethod='ee') # from moose.utils
print "Running ... "
moose.start(runtime)
print 'Finished simulation for', runtime, 'seconds'
def loadGran98NeuroML_L123(filename):
neuromlR = NeuroML()
populationDict, projectionDict = \
neuromlR.readNeuroMLFromFile(filename)
# readNeuroMLFromFile returns populationDict = { 'populationname1':(cellname,{int(instanceid1):moosecell, ... }) , ... }
# and projectionDict = { 'projectionname1':(source,target,[(syn_name1,pre_seg_path,post_seg_path),...]) , ... }
soma_path = populationDict['Gran'][1][0].path+'/Soma_0'
somaVm = setupTable('somaVm',moose.Compartment(soma_path),'Vm')
somaCa = setupTable('somaCa',moose.CaConc(soma_path+'/Gran_CaPool_98'),'Ca')
somaIKCa = setupTable('somaIKCa',moose.HHChannel(soma_path+'/Gran_KCa_98'),'Gk')
#KDrX = setupTable('ChanX',moose.HHChannel(soma_path+'/Gran_KDr_98'),'X')
soma = moose.Compartment(soma_path)
print "Reinit MOOSE ... "
resetSim(['/elec','/cells'],simdt,plotdt,simmethod='ee') # from moose.utils
print "Running ... "
moose.start(runtime)
# plotting
tvec = arange(0.0,runtime,plotdt)
plot(tvec,somaVm.vector[1:])
title('Soma Vm')
xlabel('time (s)')
ylabel('Voltage (V)')
figure()
plot(tvec,somaCa.vector[1:])
title('Soma Ca')
xlabel('time (s)')
ylabel('Ca conc (mol/m^3)')
figure()
plot(tvec,somaIKCa.vector[1:])
title('KCa current (A)')
xlabel('time (s)')
ylabel('')
print "Showing plots ..."
show()
def loadRunSTGNeuroML_L123(filename):
'Loads and runs the pyloric rhythm generator from NeuroML files.'
# for graded synapses, else NeuroML event-based are used
from load_synapses import load_synapses
moose.Neutral('/library')
# set graded to False to use event based synapses
# if False, neuroml event-based synapses get searched for and loaded
# True to load graded synapses
graded_syn = True
#graded_syn = False
if graded_syn:
load_synapses()
neuromlR = NeuroML()
## readNeuroMLFromFile below returns:
# This returns
# populationDict = {
# 'populationname1':('cellName',{('instanceid1'):moosecell, ... })
# , ...
# }
# (cellName and instanceid are strings, mooosecell is a moose.Neuron object instance)
# and
# projectionDict = {
# 'projName1':('source','target',[('syn_name1','pre_seg_path','post_seg_path')
# ,...])
# , ...
# }
populationDict, projectionDict = \
neuromlR.readNeuroMLFromFile(filename)
soma1_path = populationDict['AB_PD'][1][0].path+'/Soma_0'
soma1Vm = setupTable('somaVm',moose.Compartment(soma1_path),'Vm')
soma2_path = populationDict['LP'][1][0].path+'/Soma_0'
soma2Vm = setupTable('somaVm',moose.Compartment(soma2_path),'Vm')
soma3_path = populationDict['PY'][1][0].path+'/Soma_0'
soma3Vm = setupTable('somaVm',moose.Compartment(soma3_path),'Vm')
# monitor channel current
channel_path = soma1_path + '/KCa_STG'
channel_Ik = setupTable('KCa_Ik',moose.element(channel_path),'Ik')
# monitor Ca
capool_path = soma1_path + '/CaPool_STG'
capool_Ca = setupTable('CaPool_Ca',moose.element(capool_path),'Ca')
# monitor synaptic current
soma2 = moose.element(soma2_path)
print "Children of",soma2_path,"are:"
for child in soma2.children:
print child.className, child.path
if graded_syn:
syn_path = soma2_path+'/DoubExpSyn_Ach__cells-0-_AB_PD_0-0-_Soma_0'
syn = moose.element(syn_path)
else:
syn_path = soma2_path+'/DoubExpSyn_Ach'
syn = moose.element(syn_path)
syn_Ik = setupTable('DoubExpSyn_Ach_Ik',syn,'Ik')
print "Reinit MOOSE ... "
resetSim(['/elec',cells_path], simdt, plotdt, simmethod='hsolve')
print "Using graded synapses? = ", graded_syn
print "Running model filename = ",filename," ... "
moose.start(runtime)
tvec = np.arange(0.0,runtime+2*plotdt,plotdt)
tvec = tvec[ : soma1Vm.vector.size ]
fig = plt.figure(facecolor='w',figsize=(10,6))
axA = plt.subplot2grid((3,2),(0,0),rowspan=3,colspan=1,frameon=False)
img = plt.imread( 'STG.png' )
imgplot = axA.imshow( img )
for tick in axA.get_xticklines():
tick.set_visible(False)
for tick in axA.get_yticklines():
tick.set_visible(False)
axA.set_xticklabels([])
axA.set_yticklabels([])
ax = plt.subplot2grid((3,2),(0,1),rowspan=1,colspan=1)
ax.plot(tvec,soma1Vm.vector*1000,label='AB_PD',color='g',linestyle='solid')
ax.set_xticklabels([])
ax.set_ylabel('AB_PD (mV)')
ax = plt.subplot2grid((3,2),(1,1),rowspan=1,colspan=1)
ax.plot(tvec,soma2Vm.vector*1000,label='LP',color='r',linestyle='solid')
ax.set_xticklabels([])
ax.set_ylabel('LP (mV)')
ax = plt.subplot2grid((3,2),(2,1),rowspan=1,colspan=1)
ax.plot(tvec,soma3Vm.vector*1000,label='PY',color='b',linestyle='solid')
ax.set_ylabel('PY (mV)')
ax.set_xlabel('time (s)')
fig.tight_layout()
fig = plt.figure(facecolor='w')
plt.plot(tvec,soma2Vm.vector*1000,label='LP',color='r',linestyle='solid')
plt.plot(tvec,soma3Vm.vector*1000,label='PY',color='b',linestyle='solid')
plt.legend()
plt.xlabel('time (s)')
plt.ylabel('Soma Vm (mV)')
plt.figure(facecolor='w')
plt.plot(tvec,channel_Ik.vector,color='b',linestyle='solid')
plt.title('KCa current; Ca conc')
plt.xlabel('time (s)')
plt.ylabel('Ik (Amp)')
plt.twinx()
plt.plot(tvec,capool_Ca.vector,color='r',linestyle='solid')
plt.ylabel('Ca (mol/m^3)')
plt.figure(facecolor='w')
plt.plot(tvec,syn_Ik.vector,color='b',linestyle='solid')
plt.title('Ach syn current in '+soma2_path)
plt.xlabel('time (s)')
plt.ylabel('Isyn (S)')
print "Showing plots ..."
plt.show()
import sys
sys.path.append('.')
import moose
from moose.utils import *
from moose.neuroml.NeuroML import NeuroML
from pylab import *
SIMDT = 25e-6 # s
PLOTDT = 25e-6 # s
RUNTIME = 1.0 # s
injectmax = 2e-12 # Amperes
neuromlR = NeuroML()
neuromlR.readNeuroMLFromFile('cells_channels/CA1soma.morph.xml')
libcell = moose.Neuron('/library/CA1soma')
CA1Cellid = moose.copy(libcell,moose.Neutral('/cells'),'CA1')
CA1Cell = moose.Neuron(CA1Cellid)
#printCellTree(CA1Cell)
## edge-detect the spikes using spike-gen (table does not have edge detect)
spikeGen = moose.SpikeGen(CA1Cell.path+'/spikeGen')
spikeGen.threshold = -30e-3 # V
CA1CellSoma = moose.Compartment(CA1Cell.path+'/Seg0_soma_0_0')
CA1CellSoma.inject = 0 # by default the cell has a current injection
moose.connect(CA1CellSoma,'VmOut',spikeGen,'Vm')
## save spikes in table
table_path = moose.Neutral(CA1Cell.path+'/data').path
CA1CellSpikesTable = moose.Table(table_path+'/spikesTable')
RM_soma = 6.29013 # for somatic compartments RM (uniform)
RM_end = 3.1916 # for non somatic compartments RM (uniform)
RM_halfdist = 100.05
RM_slope = 50.48
CM = 1.0595e-6 * 1e4 # for somatic compartments CM (uniform)
RA = 2.18 # for non somatic compartments RA (uniform)
sag_cond = 1.9359 # sag conductance multiplier as used in NEURON simulation for HCN on
#sag_cond = 0 # sag conductance multiplier as used in NEURON simulation for HCN off
initVm = -62e-3 #: Resting membrane potential
E_leak = -69e-3
libraryName = '/library'
cell_path = '/library/CA1'
# Read in the CA1 cell morphology
filename = 'CA1_nrn_morph_nobiophys_Ri04.xml'
neuromlR = NeuroML()
neuromlR.readNeuroMLFromFile(filename)
# Re-create the python variable pointing to the CA1_cell to limit results just to
# type compartment (excludes the spines and allows for createDataTables function to
# work properly)
CA1_cell = moose.wildcardFind(cell_path + '/' + '#[TYPE=Compartment]')
# Define the origin to be used in the simulation as the center of the soma, as the soma is defined by
# multiple compartments
soma_xloc = CA1_cell[0].x0 + (
CA1_cell[0].x - CA1_cell[0].x0) / 2 # x for center of compartment
soma_yloc = CA1_cell[0].y0 + (
CA1_cell[0].y - CA1_cell[0].y0) / 2 # x for center of compartment
soma_zloc = CA1_cell[0].z0 + (
CA1_cell[0].z - CA1_cell[0].z0) / 2 # x for center of compartment
def loadSTGNeuroML_L123(filename):
neuromlR = NeuroML()
## readNeuroMLFromFile below returns:
# This returns
# populationDict = {
# 'populationname1':('cellName',{('instanceid1'):moosecell, ... })
# , ...
# }
# (cellName and instanceid are strings, mooosecell is a moose.Neuron object instance)
# and
# projectionDict = {
# 'projName1':('source','target',[('syn_name1','pre_seg_path','post_seg_path')
# ,...])
# , ...
# }
populationDict, projectionDict = \
neuromlR.readNeuroMLFromFile(filename)
soma1_path = populationDict['AB_PD'][1][0].path+'/Soma_0'
soma1Vm = setupTable('somaVm',moose.Compartment(soma1_path),'Vm')
soma2_path = populationDict['LP'][1][0].path+'/Soma_0'
soma2Vm = setupTable('somaVm',moose.Compartment(soma2_path),'Vm')
soma3_path = populationDict['PY'][1][0].path+'/Soma_0'
soma3Vm = setupTable('somaVm',moose.Compartment(soma3_path),'Vm')
# monitor channel current
channel_path = soma1_path + '/KCa_STG'
channel_Ik = setupTable('KCa_Ik',moose.element(channel_path),'Ik')
# monitor Ca
capool_path = soma1_path + '/CaPool_STG'
capool_Ca = setupTable('CaPool_Ca',moose.element(capool_path),'Ca')
# monitor synaptic current
soma2 = moose.element(soma2_path)
print "Children of",soma2_path,"are:"
for child in soma2.children:
print child.className, child.path
if graded_syn:
syn_path = soma2_path+'/DoubExpSyn_Ach__cells-0-_AB_PD_0-0-_Soma_0'
syn = moose.element(syn_path)
else:
syn_path = soma2_path+'/DoubExpSyn_Ach'
syn = moose.element(syn_path)
syn_Ik = setupTable('DoubExpSyn_Ach_Ik',syn,'Ik')
print "Reinit MOOSE ... "
resetSim(['/elec',cells_path], simdt, plotdt, simmethod='ee')
print "Running ... "
moose.start(runtime)
tvec = arange(0.0,runtime+2*plotdt,plotdt)
tvec = tvec[ : soma1Vm.vector.size ]
figure(facecolor='w')
plot(tvec,soma1Vm.vector,label='AB_PD',color='g',linestyle='solid')
plot(tvec,soma2Vm.vector,label='LP',color='r',linestyle='solid')
plot(tvec,soma3Vm.vector,label='PY',color='b',linestyle='solid')
legend()
title('Soma Vm')
xlabel('time (s)')
ylabel('Voltage (V)')
figure(facecolor='w')
plot(tvec,channel_Ik.vector,color='b',linestyle='solid')
title('KCa current; Ca conc')
xlabel('time (s)')
ylabel('Ik (Amp)')
twinx()
plot(tvec,capool_Ca.vector,color='r',linestyle='solid')
ylabel('Ca (mol/m^3)')
figure(facecolor='w')
plot(tvec,syn_Ik.vector,color='b',linestyle='solid')
title('Ach syn current in '+soma2_path)
xlabel('time (s)')
ylabel('Isyn (S)')
print "Showing plots ..."
show()
import sys
sys.path.append('.')
import moose
from moose.utils import *
from moose.neuroml.NeuroML import NeuroML
from pylab import *
SIMDT = 25e-6 # s
PLOTDT = 25e-6 # s
RUNTIME = 2.0 # s
injectmax = 20e-12 # Amperes
neuromlR = NeuroML()
neuromlR.readNeuroMLFromFile('cells_channels/Granule_98.morph.xml')
libcell = moose.Neuron('/library/Granule_98')
granCellid = moose.copy(libcell,moose.Neutral('/cells'),'granCell')
granCell = moose.Neuron(granCellid)
## edge-detect the spikes using spike-gen (table does not have edge detect)
spikeGen = moose.SpikeGen(granCell.path+'/spikeGen')
spikeGen.threshold = 0e-3 # V
granCellSoma = moose.Compartment(granCell.path+'/Soma_0')
moose.connect(granCellSoma,'VmOut',spikeGen,'Vm')
## save spikes in table
table_path = moose.Neutral(granCell.path+'/data').path
granCellSpikesTable = moose.Table(table_path+'/spikesTable')
moose.connect(spikeGen,'spikeOut',granCellSpikesTable,'input')
def loadRunSTGNeuroML_L123(filename):
'Loads and runs the pyloric rhythm generator from NeuroML files.'
# for graded synapses, else NeuroML event-based are used
from load_synapses import load_synapses
moose.Neutral('/library')
# set graded to False to use event based synapses
# if False, neuroml event-based synapses get searched for and loaded
# True to load graded synapses
graded_syn = True
#graded_syn = False
if graded_syn:
load_synapses()
neuromlR = NeuroML()
## readNeuroMLFromFile below returns:
# This returns
# populationDict = {
# 'populationname1':('cellName',{('instanceid1'):moosecell, ... })
# , ...
# }
# (cellName and instanceid are strings, mooosecell is a moose.Neuron object instance)
# and
# projectionDict = {
# 'projName1':('source','target',[('syn_name1','pre_seg_path','post_seg_path')
# ,...])
# , ...
# }
populationDict, projectionDict = \
neuromlR.readNeuroMLFromFile(filename)
soma1_path = populationDict['AB_PD'][1][0].path + '/Soma_0'
soma1Vm = setupTable('somaVm', moose.Compartment(soma1_path), 'Vm')
soma2_path = populationDict['LP'][1][0].path + '/Soma_0'
soma2Vm = setupTable('somaVm', moose.Compartment(soma2_path), 'Vm')
soma3_path = populationDict['PY'][1][0].path + '/Soma_0'
soma3Vm = setupTable('somaVm', moose.Compartment(soma3_path), 'Vm')
# monitor channel current
channel_path = soma1_path + '/KCa_STG'
channel_Ik = setupTable('KCa_Ik', moose.element(channel_path), 'Ik')
# monitor Ca
capool_path = soma1_path + '/CaPool_STG'
capool_Ca = setupTable('CaPool_Ca', moose.element(capool_path), 'Ca')
# monitor synaptic current
soma2 = moose.element(soma2_path)
print "Children of", soma2_path, "are:"
for child in soma2.children:
print child.className, child.path
if graded_syn:
syn_path = soma2_path + '/DoubExpSyn_Ach__cells-0-_AB_PD_0-0-_Soma_0'
syn = moose.element(syn_path)
else:
syn_path = soma2_path + '/DoubExpSyn_Ach'
syn = moose.element(syn_path)
syn_Ik = setupTable('DoubExpSyn_Ach_Ik', syn, 'Ik')
print "Reinit MOOSE ... "
resetSim(['/elec', cells_path], simdt, plotdt, simmethod='hsolve')
print "Using graded synapses? = ", graded_syn
print "Running model filename = ", filename, " ... "
moose.start(runtime)
tvec = np.arange(0.0, runtime + 2 * plotdt, plotdt)
tvec = tvec[:soma1Vm.vector.size]
fig = plt.figure(facecolor='w', figsize=(10, 6))
axA = plt.subplot2grid((3, 2), (0, 0), rowspan=3, colspan=1, frameon=False)
img = plt.imread('STG.png')
imgplot = axA.imshow(img)
for tick in axA.get_xticklines():
tick.set_visible(False)
for tick in axA.get_yticklines():
tick.set_visible(False)
axA.set_xticklabels([])
axA.set_yticklabels([])
ax = plt.subplot2grid((3, 2), (0, 1), rowspan=1, colspan=1)
ax.plot(tvec,
soma1Vm.vector * 1000,
label='AB_PD',
color='g',
linestyle='solid')
ax.set_xticklabels([])
ax.set_ylabel('AB_PD (mV)')
ax = plt.subplot2grid((3, 2), (1, 1), rowspan=1, colspan=1)
ax.plot(tvec,
soma2Vm.vector * 1000,
label='LP',
color='r',
linestyle='solid')
ax.set_xticklabels([])
ax.set_ylabel('LP (mV)')
ax = plt.subplot2grid((3, 2), (2, 1), rowspan=1, colspan=1)
ax.plot(tvec,
soma3Vm.vector * 1000,
label='PY',
color='b',
linestyle='solid')
ax.set_ylabel('PY (mV)')
ax.set_xlabel('time (s)')
fig.tight_layout()
fig = plt.figure(facecolor='w')
plt.plot(tvec,
soma2Vm.vector * 1000,
label='LP',
color='r',
linestyle='solid')
plt.plot(tvec,
soma3Vm.vector * 1000,
label='PY',
color='b',
linestyle='solid')
plt.legend()
plt.xlabel('time (s)')
plt.ylabel('Soma Vm (mV)')
plt.figure(facecolor='w')
plt.plot(tvec, channel_Ik.vector, color='b', linestyle='solid')
plt.title('KCa current; Ca conc')
plt.xlabel('time (s)')
plt.ylabel('Ik (Amp)')
plt.twinx()
plt.plot(tvec, capool_Ca.vector, color='r', linestyle='solid')
plt.ylabel('Ca (mol/m^3)')
plt.figure(facecolor='w')
plt.plot(tvec, syn_Ik.vector, color='b', linestyle='solid')
plt.title('Ach syn current in ' + soma2_path)
plt.xlabel('time (s)')
plt.ylabel('Isyn (S)')
print "Showing plots ..."
plt.show()
import sys
sys.path.append('.')
import moose
from moose.utils import *
from moose.neuroml.NeuroML import NeuroML
from pylab import *
SIMDT = 25e-6 # s
PLOTDT = 25e-6 # s
RUNTIME = 2.0 # s
injectmax = 20e-12 # Amperes
neuromlR = NeuroML()
neuromlR.readNeuroMLFromFile('cells_channels/Granule_98.morph.xml')
libcell = moose.Neuron('/library/Granule_98')
granCellid = moose.copy(libcell, moose.Neutral('/cells'), 'granCell')
granCell = moose.Neuron(granCellid)
## edge-detect the spikes using spike-gen (table does not have edge detect)
spikeGen = moose.SpikeGen(granCell.path + '/spikeGen')
spikeGen.threshold = 0e-3 # V
granCellSoma = moose.Compartment(granCell.path + '/Soma_0')
moose.connect(granCellSoma, 'VmOut', spikeGen, 'Vm')
## save spikes in table
table_path = moose.Neutral(granCell.path + '/data').path
granCellSpikesTable = moose.Table(table_path + '/spikesTable')
moose.connect(spikeGen, 'spikeOut', granCellSpikesTable, 'input')
