def generateRandomMorphology():
morphology = Morphology()
p = Point3DWithDiam(x=0, y=0, z=0, diameter=soma_diam)
d = Point3DWithDiam(x=soma_len, y=0, z=0, diameter=soma_diam)
soma = Segment(proximal=p, distal=d, name='Soma', id=0)
morphology.segments.append(soma)
parent_seg = soma
for dend_id in range(0, dend_num):
p = Point3DWithDiam(x=d.x, y=d.y, z=d.z, diameter=dend_diam)
d = Point3DWithDiam(x=p.x, y=p.y + dend_len, z=p.z, diameter=dend_diam)
dend = Segment(proximal=p,
distal=d,
name='Dend_%i' % dend_id,
id=1 + dend_id)
dend.parent = SegmentParent(segments=parent_seg.id)
parent_seg = dend
morphology.segments.append(dend)
morphology.id = "TestMorphology"
return morphology
def generateRandomMorphology():
morphology = Morphology()
p = Point3DWithDiam(x=0,y=0,z=0,diameter=soma_diam)
d = Point3DWithDiam(x=soma_len,y=0,z=0,diameter=soma_diam)
soma = Segment(proximal=p, distal=d, name = 'Soma', id = 0)
morphology.segments.append(soma)
parent_seg = soma
for dend_id in range(0,dend_num):
p = Point3DWithDiam(x=d.x,y=d.y,z=d.z,diameter=dend_diam)
d = Point3DWithDiam(x=p.x,y=p.y+dend_len,z=p.z,diameter=dend_diam)
dend = Segment(proximal=p, distal=d, name = 'Dend_%i'%dend_id, id = 1+dend_id)
dend.parent = SegmentParent(segments=parent_seg.id)
parent_seg = dend
morphology.segments.append(dend)
morphology.id = "TestMorphology"
return morphology
def createMorphoMlFile(fileName, cell):
'''
Convert to new neuroml structures and write
'''
if not muscle_dict.has_key(cell.name):
neuroMlwriter = NeuroMlWriter(fileName, cell.name)
neuroMlwriter.addCell(cell)
neuroMlwriter.writeDocumentToFile()
return
#
# Incomplete code to use the neuroml interface to write the file,
# used for muscles, doesn't produce good enough result on neurons yet.
#
seg0 = cell.segments[0].position
soma = Segment(proximal=cvt_pt(seg0.proximal_point),
distal=cvt_pt(seg0.distal_point))
soma.name = 'Soma'
soma.id = 0
axon_segments = []
for seg1 in cell.segments[1:]:
parent = SegmentParent(segments=seg1.parent)
if seg1.position.proximal_point is None:
p = None
else:
p = cvt_pt(seg1.position.proximal_point)
axon_segment = Segment(proximal = p,
distal = cvt_pt(seg1.position.distal_point),
parent = parent)
axon_segment.id = seg1.id
axon_segment.name = seg1.name
axon_segments.append(axon_segment)
morphology = Morphology()
morphology.segments.append(soma)
morphology.segments += axon_segments
morphology.id = 'morphology_' + cell.name
nml_cell = neuroml_Cell()
nml_cell.id = cell.name
nml_cell.morphology = morphology
doc = NeuroMLDocument()
doc.cells.append(nml_cell)
#addCell(doc, cell)
doc.id = "TestNeuroMLDocument"
writers.NeuroMLWriter.write(doc, "Output/%s.nml" % fileName)
def createMorphoMlFile(fileName, cell):
'''
Convert to new neuroml structures and write
'''
if not muscle_dict.has_key(cell.name):
neuroMlwriter = NeuroMlWriter(fileName, cell.name)
neuroMlwriter.addCell(cell)
neuroMlwriter.writeDocumentToFile()
return
#
# Incomplete code to use the neuroml interface to write the file,
# used for muscles, doesn't produce good enough result on neurons yet.
#
seg0 = cell.segments[0].position
soma = Segment(proximal=cvt_pt(seg0.proximal_point),
distal=cvt_pt(seg0.distal_point))
soma.name = 'Soma'
soma.id = 0
axon_segments = []
for seg1 in cell.segments[1:]:
parent = SegmentParent(segments=seg1.parent)
if seg1.position.proximal_point is None:
p = None
else:
p = cvt_pt(seg1.position.proximal_point)
axon_segment = Segment(proximal=p,
distal=cvt_pt(seg1.position.distal_point),
parent=parent)
axon_segment.id = seg1.id
axon_segment.name = seg1.name
axon_segments.append(axon_segment)
morphology = Morphology()
morphology.segments.append(soma)
morphology.segments += axon_segments
morphology.id = 'morphology_' + cell.name
nml_cell = neuroml_Cell()
nml_cell.id = cell.name
nml_cell.morphology = morphology
doc = NeuroMLDocument()
doc.cells.append(nml_cell)
#addCell(doc, cell)
doc.id = "TestNeuroMLDocument"
writers.NeuroMLWriter.write(doc, "Output/%s.nml" % fileName)
def createMorphoMlFile(fileName, cell):
'''
Convert to new neuroml structures and write
'''
seg0 = cell.segments[0].position
soma = Segment(proximal=cvt_pt(seg0.proximal_point),
distal=cvt_pt(seg0.distal_point))
soma.name = 'Soma'
soma.id = 0
axon_segments = []
for seg1 in cell.segments[1:]:
parent = SegmentParent(segments=seg1.parent)
if seg1.position.distal_point is None:
p = None
else:
p = cvt_pt(seg1.position.distal_point)
axon_segment = Segment(proximal = p,
distal = cvt_pt(seg1.position.distal_point),
parent = parent)
axon_segment.id = seg1.id
axon_segment.name = seg1.name
axon_segments.append(axon_segment)
morphology = Morphology()
morphology.segments.append(soma)
morphology.segments += axon_segments
morphology.id = 'morphology_' + cell.name
nml_cell = neuroml_Cell()
nml_cell.id = cell.name
nml_cell.morphology = morphology
doc = NeuroMLDocument()
#doc.name = "Test neuroML document"
doc.cells.append(nml_cell)
doc.id = fileName
writers.NeuroMLWriter.write(doc, "Output/%s.nml" % fileName)
def createMorphoMlFile(fileName, cell):
'''
Convert to new neuroml structures and write
'''
seg0 = cell.segments[0].position
soma = Segment(proximal=cvt_pt(seg0.proximal_point),
distal=cvt_pt(seg0.distal_point))
soma.name = 'Soma'
soma.id = 0
axon_segments = []
for seg1 in cell.segments[1:]:
parent = SegmentParent(segments=seg1.parent)
if seg1.position.distal_point is None:
p = None
else:
p = cvt_pt(seg1.position.distal_point)
axon_segment = Segment(proximal=p,
distal=cvt_pt(seg1.position.distal_point),
parent=parent)
axon_segment.id = seg1.id
axon_segment.name = seg1.name
axon_segments.append(axon_segment)
morphology = Morphology()
morphology.segments.append(soma)
morphology.segments += axon_segments
morphology.id = 'morphology_' + cell.name
nml_cell = neuroml_Cell()
nml_cell.id = cell.name
nml_cell.morphology = morphology
doc = NeuroMLDocument()
#doc.name = "Test neuroML document"
doc.cells.append(nml_cell)
doc.id = fileName
writers.NeuroMLWriter.write(doc, "Output/%s.nml" % fileName)
def create_cell(cell_id):
# type: (str) -> Cell
"""Create a NeuroML Cell.
Initialises the cell with these properties assigning IDs where applicable:
- Morphology: "morphology"
- BiophysicalProperties: "biophys"
- MembraneProperties
- IntracellularProperties
- SegmentGroups: "all", "soma_group", "dendrite_group", "axon_group" which
can be used to include all, soma, dendrite, and axon segments
respectively.
Note that since this cell does not currently include a segment in its
morphology, it is *not* a valid NeuroML construct. Use the `add_segment`
function to add segments. `add_segment` will also populate the default
segment groups this creates.
:param cell_id: id of the cell
:type cell_id: str
:returns: created cell object of type neuroml.Cell
"""
cell = Cell(id=cell_id)
cell.morphology = Morphology(id='morphology')
membrane_properties = MembraneProperties()
intracellular_properties = IntracellularProperties()
cell.biophysical_properties = BiophysicalProperties(
id="biophys",
intracellular_properties=intracellular_properties,
membrane_properties=membrane_properties)
seg_group_all = SegmentGroup(id='all')
seg_group_soma = SegmentGroup(
id='soma_group',
neuro_lex_id=neuro_lex_ids["soma"],
notes="Default soma segment group for the cell")
seg_group_axon = SegmentGroup(
id='axon_group',
neuro_lex_id=neuro_lex_ids["axon"],
notes="Default axon segment group for the cell")
seg_group_dend = SegmentGroup(
id='dendrite_group',
neuro_lex_id=neuro_lex_ids["dend"],
notes="Default dendrite segment group for the cell")
cell.morphology.segment_groups.append(seg_group_all)
cell.morphology.segment_groups.append(seg_group_soma)
cell.morphology.segment_groups.append(seg_group_axon)
cell.morphology.segment_groups.append(seg_group_dend)
return cell
def create_object(name, color, x=0, y=0, z=0):
obj = Cell()
obj.name = name
obj.id = name
nml_doc.cells.append(obj)
morphology = Morphology(id='mm')
obj.morphology = morphology
pop = Population(id="Pop_%s" % name,
component=obj.id,
type="populationList",
size=0)
net.populations.append(pop)
populations[name] = pop
pop.properties.append(Property(tag="color", value=color))
add_instance(name, x, y, z)
sg = SegmentGroup(id='all')
obj.morphology.segment_groups.append(sg)
return obj
def create_generic_neuron_cell(self):
self.generic_neuron_cell = Cell(id="GenericNeuronCell")
morphology = Morphology()
morphology.id = "morphology_" + self.generic_neuron_cell.id
self.generic_neuron_cell.morphology = morphology
prox_point = Point3DWithDiam(
x="0",
y="0",
z="0",
diameter=self.get_bioparameter("cell_diameter").value)
dist_point = Point3DWithDiam(
x="0",
y="0",
z="0",
diameter=self.get_bioparameter("cell_diameter").value)
segment = Segment(id="0",
name="soma",
proximal=prox_point,
distal=dist_point)
morphology.segments.append(segment)
self.generic_neuron_cell.biophysical_properties = BiophysicalProperties(
id="biophys_" + self.generic_neuron_cell.id)
mp = MembraneProperties()
self.generic_neuron_cell.biophysical_properties.membrane_properties = mp
mp.init_memb_potentials.append(
InitMembPotential(
value=self.get_bioparameter("initial_memb_pot").value))
mp.specific_capacitances.append(
SpecificCapacitance(value=self.get_bioparameter(
"neuron_specific_capacitance").value))
mp.spike_threshes.append(
SpikeThresh(
value=self.get_bioparameter("neuron_spike_thresh").value))
mp.channel_densities.append(
ChannelDensity(cond_density=self.get_bioparameter(
"neuron_leak_cond_density").value,
id="Leak_all",
ion_channel="Leak",
erev=self.get_bioparameter("leak_erev").value,
ion="non_specific"))
mp.channel_densities.append(
ChannelDensity(cond_density=self.get_bioparameter(
"neuron_k_slow_cond_density").value,
id="k_slow_all",
ion_channel="k_slow",
erev=self.get_bioparameter("k_slow_erev").value,
ion="k"))
'''
mp.channel_densities.append(ChannelDensity(cond_density=self.get_bioparameter("neuron_k_fast_cond_density").value,
id="k_fast_all",
ion_channel="k_fast",
erev=self.get_bioparameter("k_fast_erev").value,
ion="k"))'''
mp.channel_densities.append(
ChannelDensity(cond_density=self.get_bioparameter(
"neuron_ca_simple_cond_density").value,
id="ca_simple_all",
ion_channel="ca_simple",
erev=self.get_bioparameter("ca_simple_erev").value,
ion="ca"))
ip = IntracellularProperties()
self.generic_neuron_cell.biophysical_properties.intracellular_properties = ip
# NOTE: resistivity/axial resistance not used for single compartment cell models, so value irrelevant!
ip.resistivities.append(Resistivity(value="0.1 kohm_cm"))
# NOTE: Ca reversal potential not calculated by Nernst, so initial_ext_concentration value irrelevant!
species = Species(id="ca",
ion="ca",
concentration_model="CaPool",
initial_concentration="0 mM",
initial_ext_concentration="2E-6 mol_per_cm3")
ip.species.append(species)
def create_models(self):
self.generic_cell = Cell(id = "GenericCell")
morphology = Morphology()
morphology.id = "morphology_"+self.generic_cell.id
self.generic_cell.morphology = morphology
prox_point = Point3DWithDiam(x="0", y="0", z="0", diameter=self.get_bioparameter("cell_diameter").value)
dist_point = Point3DWithDiam(x="0", y="0", z=self.get_bioparameter("cell_length").value, diameter=self.get_bioparameter("cell_diameter").value)
segment = Segment(id="0",
name="soma",
proximal = prox_point,
distal = dist_point)
morphology.segments.append(segment)
self.generic_cell.biophysical_properties = BiophysicalProperties(id="biophys_"+self.generic_cell.id)
mp = MembraneProperties()
self.generic_cell.biophysical_properties.membrane_properties = mp
mp.init_memb_potentials.append(InitMembPotential(value=self.get_bioparameter("initial_memb_pot").value))
mp.specific_capacitances.append(SpecificCapacitance(value=self.get_bioparameter("specific_capacitance").value))
mp.spike_threshes.append(SpikeThresh(value=self.get_bioparameter("spike_thresh").value))
mp.channel_densities.append(ChannelDensity(cond_density=self.get_bioparameter("leak_cond_density").value,
id="Leak_all",
ion_channel="Leak",
erev=self.get_bioparameter("leak_erev").value,
ion="non_specific"))
mp.channel_densities.append(ChannelDensity(cond_density=self.get_bioparameter("k_slow_cond_density").value,
id="k_slow_all",
ion_channel="k_slow",
erev=self.get_bioparameter("k_slow_erev").value,
ion="k"))
mp.channel_densities.append(ChannelDensity(cond_density=self.get_bioparameter("k_fast_cond_density").value,
id="k_fast_all",
ion_channel="k_fast",
erev=self.get_bioparameter("k_fast_erev").value,
ion="k"))
mp.channel_densities.append(ChannelDensity(cond_density=self.get_bioparameter("ca_boyle_cond_density").value,
id="ca_boyle_all",
ion_channel="ca_boyle",
erev=self.get_bioparameter("ca_boyle_erev").value,
ion="ca"))
ip = IntracellularProperties()
self.generic_cell.biophysical_properties.intracellular_properties = ip
# NOTE: resistivity/axial resistance not used for single compartment cell models, so value irrelevant!
ip.resistivities.append(Resistivity(value="0.1 kohm_cm"))
# NOTE: Ca reversal potential not calculated by Nernst, so initial_ext_concentration value irrelevant!
species = Species(id="ca",
ion="ca",
concentration_model="CaPool",
initial_concentration="0 mM",
initial_ext_concentration="2E-6 mol_per_cm3")
ip.species.append(species)
self.exc_syn = GradedSynapse(id="exc_syn",
conductance = self.get_bioparameter("exc_syn_conductance").value,
delta = self.get_bioparameter("exc_syn_delta").value,
Vth = self.get_bioparameter("exc_syn_vth").value,
erev = self.get_bioparameter("exc_syn_erev").value,
k = self.get_bioparameter("exc_syn_k").value)
self.inh_syn = GradedSynapse(id="inh_syn",
conductance = self.get_bioparameter("inh_syn_conductance").value,
delta = self.get_bioparameter("inh_syn_delta").value,
Vth = self.get_bioparameter("inh_syn_vth").value,
erev = self.get_bioparameter("inh_syn_erev").value,
k = self.get_bioparameter("inh_syn_k").value)
self.elec_syn = GapJunction(id="elec_syn",
conductance = self.get_bioparameter("elec_syn_gbase").value)
self.offset_current = PulseGenerator(id="offset_current",
delay=self.get_bioparameter("unphysiological_offset_current_del").value,
duration=self.get_bioparameter("unphysiological_offset_current_dur").value,
amplitude=self.get_bioparameter("unphysiological_offset_current").value)
def neuroml_single_cell(skeleton_id, nodes, pre, post):
""" Encapsulate a single skeleton into a NeuroML Cell instance.
skeleton_id: the ID of the skeleton to which all nodes belong.
nodes: a dictionary of node ID vs tuple of node parent ID, location as a tuple of 3 floats, and radius. In nanometers.
pre: a dictionary of node ID vs list of connector ID
post: a dictionary of node ID vs list of connector ID
Returns a Cell with id=skeleton_id.
"""
# Collect the children of every node
successors = defaultdict(list) # parent node ID vs list of children node IDs
rootID = None
for nodeID, props in nodes.iteritems():
parentID = props[0]
if not parentID:
rootID = nodeID
continue
successors[parentID].append(nodeID)
# Cache of Point3DWithDiam
points = {}
def asPoint(nodeID):
""" Return the node as a Point3DWithDiam, in micrometers. """
p = points.get(nodeID)
if not p:
props = nodes[nodeID]
radius = props[2]
if radius < 0:
radius = 0.1 # FUTURE Will have to change
loc = props[1]
# Point in micrometers
p = Point3DWithDiam(loc[0] / 1000.0, loc[1] / 1000.0, loc[2] / 1000.0, radius)
points[nodeID] = p
return p
# Starting from the root node, iterate towards the end nodes, adding a segment
# for each parent-child pair.
segments = []
segment_id = 1
todo = [rootID]
# VERY CONFUSINGLY, the Segment.parent is a SegmentParent with the same id as the parent Segment. An unseemly overheady way to reference the parent Segment.
while todo:
nodeID = todo.pop()
children = successors[nodeID]
if not children:
continue
p1 = asPoint(nodeID)
parent = segments[-1] if segments else None
segment_parent = SegmentParent(segments=parent.id) if parent else None
for childID in children:
p2 = asPoint(childID)
segment_id += 1
segment = Segment(proximal=p1, distal=p2, parent=segment_parent)
segment.id = segment_id
segment.name = "%s-%s" % (nodeID, childID)
segments.append(segment)
todo.append(childID)
# Pack the segments into a Cell
morphology = Morphology()
morphology.segments.extend(segments)
morphology.id = "Skeleton #%s" % skeleton_id
# Synapses: TODO requires input from Padraig Gleeson
cell = Cell()
cell.name = 'Cell'
cell.id = skeleton_id
cell.morphology = morphology
return cell
def create_generic_muscle_cell(self):
self.generic_muscle_cell = Cell(id = "GenericMuscleCell")
morphology = Morphology()
morphology.id = "morphology_"+self.generic_muscle_cell.id
self.generic_muscle_cell.morphology = morphology
prox_point = Point3DWithDiam(x="0", y="0", z="0", diameter=self.get_bioparameter("cell_diameter").value)
dist_point = Point3DWithDiam(x="0", y="0", z="0", diameter=self.get_bioparameter("cell_diameter").value)
segment = Segment(id="0",
name="soma",
proximal = prox_point,
distal = dist_point)
morphology.segments.append(segment)
self.generic_muscle_cell.biophysical_properties = BiophysicalProperties(id="biophys_"+self.generic_muscle_cell.id)
mp = MembraneProperties()
self.generic_muscle_cell.biophysical_properties.membrane_properties = mp
mp.init_memb_potentials.append(InitMembPotential(value=self.get_bioparameter("initial_memb_pot").value))
mp.specific_capacitances.append(SpecificCapacitance(value=self.get_bioparameter("specific_capacitance").value))
mp.spike_threshes.append(SpikeThresh(value=self.get_bioparameter("muscle_spike_thresh").value))
mp.channel_densities.append(ChannelDensity(cond_density=self.get_bioparameter("muscle_leak_cond_density").value,
id="Leak_all",
ion_channel="Leak",
erev=self.get_bioparameter("leak_erev").value,
ion="non_specific"))
mp.channel_densities.append(ChannelDensity(cond_density=self.get_bioparameter("muscle_k_slow_cond_density").value,
id="k_slow_all",
ion_channel="k_slow",
erev=self.get_bioparameter("k_slow_erev").value,
ion="k"))
mp.channel_densities.append(ChannelDensity(cond_density=self.get_bioparameter("muscle_k_fast_cond_density").value,
id="k_fast_all",
ion_channel="k_fast",
erev=self.get_bioparameter("k_fast_erev").value,
ion="k"))
mp.channel_densities.append(ChannelDensity(cond_density=self.get_bioparameter("muscle_ca_boyle_cond_density").value,
id="ca_boyle_all",
ion_channel="ca_boyle",
erev=self.get_bioparameter("ca_boyle_erev").value,
ion="ca"))
ip = IntracellularProperties()
self.generic_muscle_cell.biophysical_properties.intracellular_properties = ip
# NOTE: resistivity/axial resistance not used for single compartment cell models, so value irrelevant!
ip.resistivities.append(Resistivity(value=self.get_bioparameter("resistivity").value))
# NOTE: Ca reversal potential not calculated by Nernst, so initial_ext_concentration value irrelevant!
species = Species(id="ca",
ion="ca",
concentration_model="CaPool",
initial_concentration="0 mM",
initial_ext_concentration="2E-6 mol_per_cm3")
ip.species.append(species)
def neuroml_single_cell(skeleton_id, nodes, pre, post):
""" Encapsulate a single skeleton into a NeuroML Cell instance.
skeleton_id: the ID of the skeleton to which all nodes belong.
nodes: a dictionary of node ID vs tuple of node parent ID, location as a tuple of 3 floats, and radius. In nanometers.
pre: a dictionary of node ID vs list of connector ID
post: a dictionary of node ID vs list of connector ID
Returns a Cell with id=skeleton_id.
"""
# Collect the children of every node
successors = defaultdict(
list) # parent node ID vs list of children node IDs
rootID = None
for nodeID, props in nodes.iteritems():
parentID = props[0]
if not parentID:
rootID = nodeID
continue
successors[parentID].append(nodeID)
# Cache of Point3DWithDiam
points = {}
def asPoint(nodeID):
""" Return the node as a Point3DWithDiam, in micrometers. """
p = points.get(nodeID)
if not p:
props = nodes[nodeID]
radius = props[2]
if radius < 0:
radius = 0.1 # FUTURE Will have to change
loc = props[1]
# Point in micrometers
p = Point3DWithDiam(loc[0] / 1000.0, loc[1] / 1000.0,
loc[2] / 1000.0, radius)
points[nodeID] = p
return p
# Starting from the root node, iterate towards the end nodes, adding a segment
# for each parent-child pair.
segments = []
segment_id = 1
todo = [rootID]
# VERY CONFUSINGLY, the Segment.parent is a SegmentParent with the same id as the parent Segment. An unseemly overheady way to reference the parent Segment.
while todo:
nodeID = todo.pop()
children = successors[nodeID]
if not children:
continue
p1 = asPoint(nodeID)
parent = segments[-1] if segments else None
segment_parent = SegmentParent(segments=parent.id) if parent else None
for childID in children:
p2 = asPoint(childID)
segment_id += 1
segment = Segment(proximal=p1, distal=p2, parent=segment_parent)
segment.id = segment_id
segment.name = "%s-%s" % (nodeID, childID)
segments.append(segment)
todo.append(childID)
# Pack the segments into a Cell
morphology = Morphology()
morphology.segments.extend(segments)
morphology.id = "Skeleton #%s" % skeleton_id
# Synapses: TODO requires input from Padraig Gleeson
cell = Cell()
cell.name = 'Cell'
cell.id = skeleton_id
cell.morphology = morphology
return cell
distal=P(x=229, y=0, z=100, diameter=0.60),
name='axoncoll',
parent=axonNOR2)
axoncoll2 = Segment(proximal=P(x=229, y=0, z=100, diameter=0.60),
distal=P(x=229, y=0, z=200, diameter=0.60),
name='axoncoll2',
parent=axoncoll1)
axon = [
axonAIS, axonAISK, axonmyelin1, axonNOR1, axonmyelin2, axonNOR2,
axonmyelin3, axonNOR3, axonmyelin4, axoncoll1, axoncoll2
]
segments = [soma] + dendrites + axon
for i, seg in enumerate(segments):
seg.id = i
morph = NeuroMLMorphology(Morphology(segments=segments))
morph.section_groups["dend"] = np.arange(1, 1 + len(dendrites)) # dendrites
subsets = np.genfromtxt("ModelViewParmSubset.txt", dtype=int)
for subset_id in range(88):
morph.section_groups["dend_subset{}".format(subset_id)] = \
1 + subsets[:, 0][subsets[:, 1] == subset_id] # offset is because dend indices start at 1
Purkinje = sim.MultiCompartmentNeuron # standard base class for multi-compartment neurons
Purkinje.label = "PurkinjeNeuron"
Purkinje.ion_channels = {
name: NMODLChannel(name)
for name in ('Leak', 'HCN1', 'Nav1_6', 'Kv3_4', 'Kv1_1', 'Cav3_2',
'Kca3_1', 'Kir2_3', 'Kca1_1', 'Kca2_2', 'Kv4_3', 'Kv3_3',
'Cav3_1', 'pas')
}
def generate_with_morphology(db_name,cfg_file) :
parser = SafeConfigParser()
parser.read(cfg_file)
conn = sqlite3.connect(db_name)
cursor = conn.cursor()
cursor.execute("select distinct name from swc_data order by name")
rets = cursor.fetchall()
names =[]
c=0
for entity in rets :
names.append(entity[0])
prefix=""
if db_name.startswith(".."):
prefix = db_name.split("/")
print ".. prefix: ", prefix,"\nNot sure this works correctly..."
else:
prefix = "/".join(db_name.split(".")[0].split("/")[:-1])
print "prefix: ", prefix
morphology = Morphology()
p = Point3DWithDiam(x=0,y=0,z=0,diameter=soma_diam)
d = Point3DWithDiam(x=soma_len,y=0,z=0,diameter=soma_diam)
soma = Segment(proximal=p, distal=d, name = 'Soma', id = 0)
morphology.segments.append(soma)
parent_seg = soma
morphology.id = "NeuroMLMorphology"
return morphology
all_points = {}
soma_radius = None
for name in names :
all_points[name] = {}
cursor.execute("select * from swc_data where name=? order by id",(str(name),) )
rets = cursor.fetchall()
points = {}
is_soma = True
print 'processing: ', name
for entity in rets :
contents = {}
index = entity[0]
if is_soma:
points['soma'] = (entity[3],entity[4],entity[5])
soma_radius = parser.getint(entity[1].split("__")[0],"soma_radius")
print "found soma_radius: ", soma_radius
is_soma = False
contents['name'] = entity[1]
contents['swc_type'] = entity[2]
contents['f'] = (entity[3],entity[4],entity[5])
contents['t'] = (entity[6],entity[7],entity[8])
contents['r'] = entity[9]
points[entity[0]] =contents
all_points[name] = points
def create_models(self):
self.generic_cell = Cell(id = "GenericCell")
morphology = Morphology()
morphology.id = "morphology_"+self.generic_cell.id
self.generic_cell.morphology = morphology
prox_point = Point3DWithDiam(x="0", y="0", z="0", diameter=self.get_bioparameter("cell_diameter").value)
dist_point = Point3DWithDiam(x="0", y="0", z=self.get_bioparameter("cell_length").value, diameter=self.get_bioparameter("cell_diameter").value)
segment = Segment(id="0",
name="soma",
proximal = prox_point,
distal = dist_point)
morphology.segments.append(segment)
self.generic_cell.biophysical_properties = BiophysicalProperties(id="biophys_"+self.generic_cell.id)
mp = MembraneProperties()
self.generic_cell.biophysical_properties.membrane_properties = mp
mp.init_memb_potentials.append(InitMembPotential(value=self.get_bioparameter("initial_memb_pot").value))
mp.specific_capacitances.append(SpecificCapacitance(value=self.get_bioparameter("specific_capacitance").value))
mp.spike_threshes.append(SpikeThresh(value=self.get_bioparameter("spike_thresh").value))
mp.channel_densities.append(ChannelDensity(cond_density=self.get_bioparameter("leak_cond_density").value,
id="Leak_all",
ion_channel="Leak",
erev=self.get_bioparameter("leak_erev").value,
ion="non_specific"))
mp.channel_densities.append(ChannelDensity(cond_density=self.get_bioparameter("k_slow_cond_density").value,
id="k_slow_all",
ion_channel="k_slow",
erev=self.get_bioparameter("k_slow_erev").value,
ion="k"))
mp.channel_densities.append(ChannelDensity(cond_density=self.get_bioparameter("k_fast_cond_density").value,
id="k_fast_all",
ion_channel="k_fast",
erev=self.get_bioparameter("k_fast_erev").value,
ion="k"))
mp.channel_densities.append(ChannelDensity(cond_density=self.get_bioparameter("ca_boyle_cond_density").value,
id="ca_boyle_all",
ion_channel="ca_boyle",
erev=self.get_bioparameter("ca_boyle_erev").value,
ion="ca"))
ip = IntracellularProperties()
self.generic_cell.biophysical_properties.intracellular_properties = ip
# NOTE: resistivity/axial resistance not used for single compartment cell models, so value irrelevant!
ip.resistivities.append(Resistivity(value="0.1 kohm_cm"))
# NOTE: Ca reversal potential not calculated by Nernst, so initial_ext_concentration value irrelevant!
species = Species(id="ca",
ion="ca",
concentration_model="CaPool",
initial_concentration="0 mM",
initial_ext_concentration="2E-6 mol_per_cm3")
ip.species.append(species)
self.exc_syn = ExpTwoSynapse(id="exc_syn",
gbase = self.get_bioparameter("chem_exc_syn_gbase").value,
erev = self.get_bioparameter("chem_exc_syn_erev").value,
tau_decay = self.get_bioparameter("chem_exc_syn_decay").value,
tau_rise = self.get_bioparameter("chem_exc_syn_rise").value)
self.inh_syn = ExpTwoSynapse(id="inh_syn",
gbase = self.get_bioparameter("chem_inh_syn_gbase").value,
erev = self.get_bioparameter("chem_inh_syn_erev").value,
tau_decay = self.get_bioparameter("chem_inh_syn_decay").value,
tau_rise = self.get_bioparameter("chem_inh_syn_rise").value)
self.elec_syn = GapJunction(id="elec_syn",
conductance = self.get_bioparameter("elec_syn_gbase").value)
self.offset_current = PulseGenerator(id="offset_current",
delay=self.get_bioparameter("unphysiological_offset_current_del").value,
duration=self.get_bioparameter("unphysiological_offset_current_dur").value,
amplitude=self.get_bioparameter("unphysiological_offset_current").value)
def create_cell():
"""Create the cell.
:returns: name of the cell nml file
"""
# Create the nml file and add the ion channels
hh_cell_doc = NeuroMLDocument(id="cell", notes="HH cell")
hh_cell_fn = "HH_example_cell.nml"
hh_cell_doc.includes.append(IncludeType(href=create_na_channel()))
hh_cell_doc.includes.append(IncludeType(href=create_k_channel()))
hh_cell_doc.includes.append(IncludeType(href=create_leak_channel()))
# Define a cell
hh_cell = Cell(id="hh_cell", notes="A single compartment HH cell")
# Define its biophysical properties
bio_prop = BiophysicalProperties(id="hh_b_prop")
# notes="Biophysical properties for HH cell")
# Membrane properties are a type of biophysical properties
mem_prop = MembraneProperties()
# Add membrane properties to the biophysical properties
bio_prop.membrane_properties = mem_prop
# Append to cell
hh_cell.biophysical_properties = bio_prop
# Channel density for Na channel
na_channel_density = ChannelDensity(id="na_channels", cond_density="120.0 mS_per_cm2", erev="50.0 mV", ion="na", ion_channel="na_channel")
mem_prop.channel_densities.append(na_channel_density)
# Channel density for k channel
k_channel_density = ChannelDensity(id="k_channels", cond_density="360 S_per_m2", erev="-77mV", ion="k", ion_channel="k_channel")
mem_prop.channel_densities.append(k_channel_density)
# Leak channel
leak_channel_density = ChannelDensity(id="leak_channels", cond_density="3.0 S_per_m2", erev="-54.3mV", ion="non_specific", ion_channel="leak_channel")
mem_prop.channel_densities.append(leak_channel_density)
# Other membrane properties
mem_prop.spike_threshes.append(SpikeThresh(value="-20mV"))
mem_prop.specific_capacitances.append(SpecificCapacitance(value="1.0 uF_per_cm2"))
mem_prop.init_memb_potentials.append(InitMembPotential(value="-65mV"))
intra_prop = IntracellularProperties()
intra_prop.resistivities.append(Resistivity(value="0.03 kohm_cm"))
# Add to biological properties
bio_prop.intracellular_properties = intra_prop
# Morphology
morph = Morphology(id="hh_cell_morph")
# notes="Simple morphology for the HH cell")
seg = Segment(id="0", name="soma", notes="Soma segment")
# We want a diameter such that area is 1000 micro meter^2
# surface area of a sphere is 4pi r^2 = 4pi diam^2
diam = math.sqrt(1000 / math.pi)
proximal = distal = Point3DWithDiam(x="0", y="0", z="0", diameter=str(diam))
seg.proximal = proximal
seg.distal = distal
morph.segments.append(seg)
hh_cell.morphology = morph
hh_cell_doc.cells.append(hh_cell)
pynml.write_neuroml2_file(nml2_doc=hh_cell_doc, nml2_file_name=hh_cell_fn, validate=True)
return hh_cell_fn
