def create_neuron_to_muscle_syn(self):
self.neuron_to_muscle_exc_syn = GradedSynapse(
id="neuron_to_muscle_exc_syn",
conductance=self.get_bioparameter(
"neuron_to_muscle_exc_syn_conductance").value,
delta=self.get_bioparameter(
"neuron_to_muscle_exc_syn_delta").value,
Vth=self.get_bioparameter("neuron_to_muscle_exc_syn_vth").value,
erev=self.get_bioparameter("neuron_to_muscle_exc_syn_erev").value,
k=self.get_bioparameter("neuron_to_muscle_exc_syn_k").value)
self.neuron_to_muscle_inh_syn = GradedSynapse(
id="neuron_to_muscle_inh_syn",
conductance=self.get_bioparameter(
"neuron_to_muscle_inh_syn_conductance").value,
delta=self.get_bioparameter(
"neuron_to_muscle_inh_syn_delta").value,
Vth=self.get_bioparameter("neuron_to_muscle_inh_syn_vth").value,
erev=self.get_bioparameter("neuron_to_muscle_inh_syn_erev").value,
k=self.get_bioparameter("neuron_to_muscle_inh_syn_k").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_models(self):
self.generic_cell = IafActivityCell(
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,
tau1=self.get_bioparameter("iaf_tau1").value)
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 get_inh_syn(self, pre_cell, post_cell, type):
self.found_specific_param = False
specific_param_template = '%s_to_%s_inh_syn_%s'
if type == 'neuron_to_neuron':
conductance = self.get_conn_param(pre_cell, post_cell,
specific_param_template,
'neuron_to_neuron_inh_syn_%s',
'conductance')
erev = self.get_conn_param(pre_cell, post_cell,
specific_param_template, 'inh_syn_%s',
'erev')
delta = self.get_conn_param(pre_cell, post_cell,
specific_param_template, 'inh_syn_%s',
'delta')
vth = self.get_conn_param(pre_cell, post_cell,
specific_param_template, 'inh_syn_%s',
'vth')
k = self.get_conn_param(pre_cell, post_cell,
specific_param_template, 'inh_syn_%s', 'k')
conn_id = 'neuron_to_neuron_inh_syn'
elif type == 'neuron_to_muscle':
conductance = self.get_conn_param(pre_cell, post_cell,
specific_param_template,
'neuron_to_muscle_inh_syn_%s',
'conductance')
erev = self.get_conn_param(pre_cell, post_cell,
specific_param_template, 'inh_syn_%s',
'erev')
delta = self.get_conn_param(pre_cell, post_cell,
specific_param_template, 'inh_syn_%s',
'delta')
vth = self.get_conn_param(pre_cell, post_cell,
specific_param_template, 'inh_syn_%s',
'vth')
k = self.get_conn_param(pre_cell, post_cell,
specific_param_template, 'inh_syn_%s', 'k')
conn_id = 'neuron_to_muscle_inh_syn'
if self.found_specific_param:
conn_id = '%s_to_%s_inh_syn' % (pre_cell, post_cell)
return GradedSynapse(id=conn_id,
conductance=conductance,
delta=delta,
Vth=vth,
erev=erev,
k=k)
def create_n_connection_synapse(prototype_syn, n, nml_doc, existing_synapses):
new_id = "%s_%sconns" % (prototype_syn.id, str(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)
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)
elif isinstance(prototype_syn, GradedSynapse):
magnitude, unit = bioparameters.split_neuroml_quantity(
prototype_syn.conductance)
new_syn = GradedSynapse(id=new_id,
conductance="%s%s" % (magnitude * n, unit),
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)
else:
new_syn = existing_synapses[new_id]
return new_syn
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)
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",
Vth="-55mV",
k="0.025per_ms",
erev="0mV")
nml_doc.graded_synapses.append(grad_syn)
pfs = PoissonFiringSynapse(id='pfs',
average_rate='150Hz',
synapse=syn0.id,
spike_target="./%s" % syn0.id)
nml_doc.poisson_firing_synapses.append(pfs)
net = Network(id="CompleteNet",
type="networkWithTemperature",
temperature="6.3 degC")
def generate(reference = "Weights",
num_each = 6,
connections=True,
duration = 1000,
format='xml'):
nml_doc, network = oc.generate_network(reference)
cell_id = 'HH_477127614'
cell = oc.include_opencortex_cell(nml_doc, 'AllenInstituteCellTypesDB_HH/%s.cell.nml'%cell_id)
xDim = 500
yDim = 500
zDim = 30
pop_pre = oc.add_population_in_rectangular_region(network, 'pop_pre',
cell_id, num_each,
0,0,0, xDim,yDim,zDim,
color='.8 0 0')
pop_post_chem_exc = oc.add_population_in_rectangular_region(network, 'pop_post_chem_exc',
cell_id, num_each+1,
0,yDim,0, xDim,yDim,zDim,
color='0 0 .8')
pop_post_chem_inh = oc.add_population_in_rectangular_region(network, 'pop_post_chem_inh',
cell_id, num_each+2,
xDim,yDim,0, xDim,yDim,zDim,
color='0 .8 .8')
pop_post_cont = oc.add_population_in_rectangular_region(network, 'pop_post_cont',
cell_id, num_each+3,
xDim,0,0, xDim,yDim,zDim,
color='0 .8 0')
ampa_syn = oc.add_exp_two_syn(nml_doc, id="AMPA_syn",
gbase="10nS", erev="0mV",
tau_rise="2ms", tau_decay="10ms")
gaba_syn = oc.add_exp_two_syn(nml_doc, id="GABA_syn",
gbase="10nS", erev="-80mV",
tau_rise="3ms", tau_decay="30ms")
gj_syn = oc.add_gap_junction_synapse(nml_doc, id="gj0",
conductance=".05nS")
analog_syn = GradedSynapse(id='analog_syn',
conductance="10nS",
delta="5mV",
Vth="-35mV",
k="0.025per_ms",
erev="0mV")
silent_syn = SilentSynapse(id="silent1")
nml_doc.graded_synapses.append(analog_syn)
nml_doc.silent_synapses.append(silent_syn)
pfs = oc.add_poisson_firing_synapse(nml_doc, id="poissonFiringSyn",
average_rate="10 Hz", synapse_id=ampa_syn.id)
oc.add_inputs_to_population(network, "Stim0",
pop_pre, pfs.id, all_cells=True)
if connections:
proj_chem_exc = oc.add_probabilistic_projection(network,
"proj_chem_exc",
pop_pre,
pop_post_chem_exc,
ampa_syn.id,
0.7,
weight=1,
delay=5)
for conn in proj_chem_exc.connection_wds:
if conn.get_pre_cell_id() < 3 and conn.get_post_cell_id() < 3:
conn.weight = 0.5
proj_chem_inh = oc.add_probabilistic_projection(network,
"proj_chem_inh",
pop_pre,
pop_post_chem_inh,
gaba_syn.id,
0.7,
weight=1,
delay=5)
for conn in proj_chem_inh.connection_wds:
if conn.get_pre_cell_id() < 3 and conn.get_post_cell_id() < 3:
conn.weight = 2
proj_cont = ContinuousProjection(id='proj_cont', \
presynaptic_population=pop_pre.id,
postsynaptic_population=pop_post_cont.id)
network.continuous_projections.append(proj_cont)
for i in range(pop_pre.get_size()):
for j in range(pop_post_cont.get_size()):
conn0 = ContinuousConnectionInstanceW(id='%s'%(j+i*pop_pre.get_size()), \
pre_cell='../%s/%s/%s'%(pop_pre.id,i,cell_id),
post_cell='../%s/%s/%s'%(pop_post_cont.id,j,cell_id),
pre_component=silent_syn.id,
post_component=analog_syn.id,
weight=(i+j)/10.0)
proj_cont.continuous_connection_instance_ws.append(conn0)
gj_pops = [pop_pre, pop_post_chem_exc, pop_post_chem_inh, pop_post_cont]
for pre in gj_pops:
for post in gj_pops:
proj_gap = oc.add_targeted_electrical_projection(nml_doc,
network,
"proj_",
pre,
post,
targeting_mode='convergent',
synapse_list=[gj_syn.id],
pre_segment_group = 'soma_group',
post_segment_group = 'soma_group',
number_conns_per_cell=3)
for conn in network.electrical_projections[-1].electrical_connection_instance_ws:
conn.weight = conn.get_pre_cell_id() + conn.get_post_cell_id()
nml_file_name = '%s.net.%s'%(network.id,'nml.h5' if format == 'hdf5' else 'nml')
target_dir = 'HDF5/' if format == 'hdf5' else './'
oc.save_network(nml_doc,
nml_file_name,
validate=(format=='xml'),
format = format,
target_dir=target_dir)
if format=='xml':
lems_file_name = oc.generate_lems_simulation(nml_doc, network,
nml_file_name,
duration = duration,
dt = 0.025,
gen_plots_for_all_v = True,
gen_saves_for_all_v = True)
else:
lems_file_name = None
return nml_doc, nml_file_name, lems_file_name
