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, 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 _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')
## 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 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):
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'
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')
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()
