def create_models(self):
self.generic_cell = IafCell(id="generic_iaf_cell",
C = self.get_bioparameter("iaf_C").value,
thresh = self.get_bioparameter("iaf_thresh").value,
reset = self.get_bioparameter("iaf_reset").value,
leak_conductance = self.get_bioparameter("iaf_conductance").value,
leak_reversal = self.get_bioparameter("iaf_leak_reversal").value)
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 = ExpTwoSynapse(id="elec_syn",
gbase = self.get_bioparameter("elec_syn_gbase").value,
erev = self.get_bioparameter("elec_syn_erev").value,
tau_decay = self.get_bioparameter("elec_syn_decay").value,
tau_rise = self.get_bioparameter("elec_syn_rise").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_n_connection_synapse(prototype_syn, n, nml_doc):
new_id = "%s_%sconns"%(prototype_syn.id, str(n).replace('.', '_'))
if not existing_synapses.has_key(new_id):
if isinstance(prototype_syn, ExpTwoSynapse):
magnitude, unit = split_neuroml_quantity(prototype_syn.gbase)
new_syn = ExpTwoSynapse(id=new_id,
gbase = "%s%s"%(magnitude*n, unit),
erev = prototype_syn.erev,
tau_decay = prototype_syn.tau_decay,
tau_rise = prototype_syn.tau_rise)
existing_synapses[new_id] = new_syn
nml_doc.exp_two_synapses.append(new_syn)
elif isinstance(prototype_syn, GapJunction):
magnitude, unit = split_neuroml_quantity(prototype_syn.conductance)
new_syn = GapJunction(id=new_id,
conductance = "%s%s"%(magnitude*n, unit))
existing_synapses[new_id] = new_syn
nml_doc.gap_junctions.append(new_syn)
else:
new_syn = existing_synapses[new_id]
return new_syn
def get_inh_syn(self, pre_cell, post_cell, type):
self.found_specific_param = False
specific_param_template = '%s_to_%s_chem_inh_syn_%s'
if type == 'neuron_to_neuron':
gbase = self.get_conn_param(pre_cell, post_cell, specific_param_template,
'neuron_to_neuron_chem_inh_syn_%s', 'gbase')
erev = self.get_conn_param(pre_cell, post_cell, specific_param_template,
'chem_inh_syn_%s', 'erev')
decay = self.get_conn_param(pre_cell, post_cell, specific_param_template,
'chem_inh_syn_%s', 'decay')
rise = self.get_conn_param(pre_cell, post_cell, specific_param_template,
'chem_inh_syn_%s', 'rise')
conn_id = 'neuron_to_neuron_inh_syn'
elif type == 'neuron_to_muscle':
gbase = self.get_conn_param(pre_cell, post_cell, specific_param_template,
'neuron_to_muscle_chem_inh_syn_%s', 'gbase')
erev = self.get_conn_param(pre_cell, post_cell, specific_param_template,
'chem_inh_syn_%s', 'erev')
decay = self.get_conn_param(pre_cell, post_cell, specific_param_template,
'chem_inh_syn_%s', 'decay')
rise = self.get_conn_param(pre_cell, post_cell, specific_param_template,
'chem_inh_syn_%s', 'rise')
conn_id = 'neuron_to_muscle_inh_syn'
if self.found_specific_param:
conn_id = '%s_to_%s_inh_syn' % (pre_cell, post_cell)
return ExpTwoSynapse(id=conn_id,
gbase=gbase,
erev=erev,
tau_decay=decay,
tau_rise=rise)
def create_neuron_to_muscle_syn(self):
self.neuron_to_muscle_exc_syn = ExpTwoSynapse(id="neuron_to_muscle_exc_syn",
gbase = self.get_bioparameter("neuron_to_muscle_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.neuron_to_muscle_inh_syn = ExpTwoSynapse(id="neuron_to_muscle_inh_syn",
gbase = self.get_bioparameter("neuron_to_muscle_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.neuron_to_muscle_elec_syn = GapJunction(id="neuron_to_muscle_elec_syn",
conductance = self.get_bioparameter("neuron_to_muscle_elec_syn_gbase").value)
def create_neuron_to_neuron_syn(self):
self.neuron_to_neuron_exc_syn = ExpTwoSynapse(id="neuron_to_neuron_exc_syn",
gbase = self.get_bioparameter("neuron_to_neuron_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.neuron_to_neuron_inh_syn = ExpTwoSynapse(id="neuron_to_neuron_inh_syn",
gbase = self.get_bioparameter("neuron_to_neuron_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.neuron_to_neuron_elec_syn = ExpTwoSynapse(id="neuron_to_neuron_elec_syn",
gbase = self.get_bioparameter("neuron_to_neuron_elec_syn_gbase").value,
erev = self.get_bioparameter("elec_syn_erev").value,
tau_decay = self.get_bioparameter("elec_syn_decay").value,
tau_rise = self.get_bioparameter("elec_syn_rise").value)
def create_n_connection_synapse(prototype_syn, n, nml_doc):
new_id = "%s_%iconns"%(prototype_syn.id, n)
if not existing_synapses.has_key(new_id):
magnitude, unit = split_neuroml_quantity(prototype_syn.gbase)
new_syn = ExpTwoSynapse(id=new_id,
gbase = "%f%s"%(magnitude*n, unit),
erev = prototype_syn.erev,
tau_decay = prototype_syn.tau_decay,
tau_rise = prototype_syn.tau_rise)
existing_synapses[new_id] = new_syn
nml_doc.exp_two_synapses.append(new_syn)
else:
new_syn = existing_synapses[new_id]
return new_syn
unphysiological_offset_current = BioParameter("unphysiological_offset_current", "0.21nA", "KnownError", "0")
unphysiological_offset_current_dur = BioParameter("unphysiological_offset_current_dur", "200ms", "KnownError", "0")
generic_cell = IafCell(id="generic_iaf_cell",
C = iaf_C.value,
thresh = iaf_thresh.value,
reset = iaf_reset.value,
leak_conductance = iaf_conductance.value,
leak_reversal = iaf_leak_reversal.value)
exc_syn = ExpTwoSynapse(id="exc_syn",
gbase = chem_exc_syn_gbase.value,
erev = chem_exc_syn_erev.value,
tau_decay = chem_exc_syn_decay.value,
tau_rise = chem_exc_syn_rise.value)
inh_syn = ExpTwoSynapse(id="inh_syn",
gbase = chem_inh_syn_gbase.value,
erev = chem_inh_syn_erev.value,
tau_decay = chem_inh_syn_decay.value,
tau_rise = chem_inh_syn_rise.value)
offset_current = PulseGenerator(id="offset_current",
delay="0ms",
duration=unphysiological_offset_current_dur.value,
amplitude=unphysiological_offset_current.value)
v0="-70mV",
thresh="30mV",
a="0.02",
b="0.2",
c="-65.0",
d="6")
nml_doc.izhikevich_cells.append(iz0)
syn0 = ExpOneSynapse(id="syn0", gbase="14nS", erev="0mV", tau_decay="3ms")
nml_doc.exp_one_synapses.append(syn0)
syn1 = ExpTwoSynapse(id="syn1",
gbase="2nS",
erev="0mV",
tau_rise="1ms",
tau_decay="3ms")
nml_doc.exp_two_synapses.append(syn1)
gj = GapJunction(id="gj1", conductance="10pS")
nml_doc.gap_junctions.append(gj)
sil_syn = SilentSynapse(id="silent1")
nml_doc.silent_synapses.append(sil_syn)
grad_syn = GradedSynapse(id="gs1",
conductance="0.5pS",
delta="5mV",
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="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_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_n_connection_synapse(prototype_syn, n, nml_doc, existing_synapses):
new_id = "%s_%sconns"%(prototype_syn.id, str(n).replace('.', '_'))
if type(n) is float:
new_id = "%s_%sconns" % (prototype_syn.id, get_str_from_expnotation(n).replace('.', '_'))
if isinstance(prototype_syn, ExpTwoSynapse):
new_id = "%s"%(prototype_syn.id)
if not existing_synapses.has_key(new_id):
if isinstance(prototype_syn, ExpTwoSynapse):
new_syn = ExpTwoSynapse(id=new_id,
gbase = prototype_syn.gbase,
erev = prototype_syn.erev,
tau_decay = prototype_syn.tau_decay,
tau_rise = prototype_syn.tau_rise)
existing_synapses[new_id] = new_syn
nml_doc.exp_two_synapses.append(new_syn)
elif isinstance(prototype_syn, GapJunction):
magnitude, unit = bioparameters.split_neuroml_quantity(prototype_syn.conductance)
cond = "%s%s" % (magnitude * n, unit)
if type(n) is float:
cond = "%s%s" % (get_str_from_expnotation(magnitude * n), unit)
new_syn = GapJunction(id=new_id,
conductance = cond)
existing_synapses[new_id] = new_syn
nml_doc.gap_junctions.append(new_syn)
elif isinstance(prototype_syn, GradedSynapse):
magnitude, unit = bioparameters.split_neuroml_quantity(prototype_syn.conductance)
cond = "%s%s" % (magnitude * n, unit)
if type(n) is float:
cond = "%s%s" % (get_str_from_expnotation(magnitude * n), unit)
new_syn = GradedSynapse(id=new_id,
conductance = cond,
delta = prototype_syn.delta,
Vth = prototype_syn.Vth,
erev = prototype_syn.erev,
k = prototype_syn.k)
existing_synapses[new_id] = new_syn
nml_doc.graded_synapses.append(new_syn)
elif isinstance(prototype_syn, GradedSynapse2):
magnitude, unit = bioparameters.split_neuroml_quantity(prototype_syn.conductance)
cond = "%s%s" % (magnitude * n, unit)
if type(n) is float:
cond = "%s%s" % (get_str_from_expnotation(magnitude * n), unit)
new_syn = GradedSynapse2(id=new_id,
conductance = cond,
ar = prototype_syn.ar,
ad = prototype_syn.ad,
beta = prototype_syn.beta,
vth = prototype_syn.vth,
erev = prototype_syn.erev)
existing_synapses[new_id] = new_syn
nml_doc.graded_synapses.append(new_syn)
else:
new_syn = existing_synapses[new_id]
return new_syn