def make_cable_cell(gid):
b = arbor.flat_cell_builder()
# Soma with radius 6 μm.
s = b.add_sphere(6, "soma")
# Single dendrite of length 100 μm and radius 2 μm attached to soma.
b1 = b.add_cable(parent=s, length=100, radius=2, name="dend", ncomp=1)
# Attach two dendrites of length 50 μm to the end of the first dendrite.
# Radius tapers from 2 to 0.5 μm over the length of the dendrite.
b2 = b.add_cable(parent=b1, length=50, radius=(2,0.5), name="dend", ncomp=1)
# Constant radius of 1 μm over the length of the dendrite.
b3 = b.add_cable(parent=b1, length=50, radius=1, name="dend", ncomp=1)
# Mark location for synapse at the midpoint of branch 1 (the first dendrite).
b.add_label('synapse_site', '(location 1 0.5)')
# Mark the root of the tree.
b.add_label('root', '(root)')
cell = b.build()
# Put hh dynamics on soma, and passive properties on the dendrites.
cell.paint('soma', 'hh')
cell.paint('dend', 'pas')
# Attach a single synapse.
cell.place('synapse_site', 'expsyn')
# Attach a spike detector with threshold of -10 mV.
cell.place('root', arbor.spike_detector(-10))
return cell
def cell_description(self, gid):
tree = arbor.segment_tree()
tree.append(arbor.mnpos,
arbor.mpoint(-3, 0, 0, 3),
arbor.mpoint(3, 0, 0, 3),
tag=1)
labels = arbor.label_dict({
'soma': '(tag 1)',
'center': '(location 0 0.5)'
})
decor = arbor.decor()
decor.set_property(Vm=-40)
decor.paint('(all)', arbor.density('hh'))
decor.place('"center"', arbor.spike_detector(-10), "detector")
decor.place('"center"', arbor.synapse('expsyn'), "synapse")
mech = arbor.mechanism('expsyn_stdp')
mech.set("max_weight", 1.)
syn = arbor.synapse(mech)
decor.place('"center"', syn, "stpd_synapse")
cell = arbor.cable_cell(tree, labels, decor)
return cell
def make_cable_cell(gid):
# (1) Build a segment tree
tree = arbor.segment_tree()
# Soma (tag=1) with radius 6 μm, modelled as cylinder of length 2*radius
s = tree.append(arbor.mnpos,
arbor.mpoint(-12, 0, 0, 6),
arbor.mpoint(0, 0, 0, 6),
tag=1)
# Single dendrite (tag=3) of length 50 μm and radius 2 μm attached to soma.
b0 = tree.append(s,
arbor.mpoint(0, 0, 0, 2),
arbor.mpoint(50, 0, 0, 2),
tag=3)
# Attach two dendrites (tag=3) of length 50 μm to the end of the first dendrite.
# Radius tapers from 2 to 0.5 μm over the length of the dendrite.
b1 = tree.append(b0,
arbor.mpoint(50, 0, 0, 2),
arbor.mpoint(50 + 50 / sqrt(2), 50 / sqrt(2), 0, 0.5),
tag=3)
# Constant radius of 1 μm over the length of the dendrite.
b2 = tree.append(b0,
arbor.mpoint(50, 0, 0, 1),
arbor.mpoint(50 + 50 / sqrt(2), -50 / sqrt(2), 0, 1),
tag=3)
# Associate labels to tags
labels = arbor.label_dict()
labels['soma'] = '(tag 1)'
labels['dend'] = '(tag 3)'
# (2) Mark location for synapse at the midpoint of branch 1 (the first dendrite).
labels['synapse_site'] = '(location 1 0.5)'
labels['synapse_site2'] = '(location 1 0.5)'
# Mark the root of the tree.
labels['root'] = '(root)'
# (3) Create a decor and a cable_cell
decor = arbor.decor()
# Put hh dynamics on soma, and passive properties on the dendrites.
decor.paint('"soma"', 'hh')
decor.paint('"dend"', 'pas')
# (4) Attach a single synapse.
decor.place('"synapse_site"', 'expsyn')
decor.place('"synapse_site2"', 'expsyn')
# Attach a spike detector with threshold of -10 mV.
decor.place('"root"', arbor.spike_detector(-10))
cell = arbor.cable_cell(tree, labels, decor)
return cell
def make_cable_cell(gid):
# Build a segment tree
tree = arbor.segment_tree()
# Soma with radius 5 μm and length 2 * radius = 10 μm, (tag = 1)
s = tree.append(arbor.mnpos,
arbor.mpoint(-10, 0, 0, 5),
arbor.mpoint(0, 0, 0, 5),
tag=1)
# Single dendrite with radius 2 μm and length 40 μm, (tag = 2)
b = tree.append(s,
arbor.mpoint(0, 0, 0, 2),
arbor.mpoint(40, 0, 0, 2),
tag=2)
# Label dictionary for cell components
labels = arbor.label_dict()
labels['soma'] = '(tag 1)'
labels['dend'] = '(tag 2)'
# Mark location for synapse site at midpoint of dendrite (branch 0 = soma + dendrite)
labels['synapse_site'] = '(location 0 0.6)'
# Gap junction site at connection point of soma and dendrite
labels['gj_site'] = '(location 0 0.2)'
# Label root of the tree
labels['root'] = '(root)'
# Paint dynamics onto the cell, hh on soma and passive properties on dendrite
decor = arbor.decor()
decor.paint('"soma"', arbor.density("hh"))
decor.paint('"dend"', arbor.density("pas"))
# Attach one synapse and gap junction each on their labeled sites
decor.place('"synapse_site"', arbor.synapse('expsyn'), 'syn')
decor.place('"gj_site"', arbor.junction('gj'), 'gj')
# Attach spike detector to cell root
decor.place('"root"', arbor.spike_detector(-10), 'detector')
cell = arbor.cable_cell(tree, labels, decor)
return cell
def cable_cell():
# (1) Create a morphology with a single (cylindrical) segment of length=diameter=6 μm
tree = arbor.segment_tree()
tree.append(
arbor.mnpos,
arbor.mpoint(-3, 0, 0, 3),
arbor.mpoint(3, 0, 0, 3),
tag=1,
)
# (2) Define the soma and its midpoint
labels = arbor.label_dict({'soma': '(tag 1)',
'midpoint': '(location 0 0.5)'})
# (3) Create cell and set properties
decor = arbor.decor()
decor.set_property(Vm=-40)
decor.paint('"soma"', arbor.density('hh'))
decor.place('"midpoint"', arbor.iclamp( 10, 2, 0.8), "iclamp")
decor.place('"midpoint"', arbor.spike_detector(-10), "detector")
return arbor.cable_cell(tree, labels, decor)
def create_arbor_cell(cell, nl_network, gid):
if cell.arbor_cell=='cable_cell':
default_tree = arbor.segment_tree()
radius = evaluate(cell.parameters['radius'], nl_network.parameters) if 'radius' in cell.parameters else 3
default_tree.append(arbor.mnpos, arbor.mpoint(-1*radius, 0, 0, radius), arbor.mpoint(radius, 0, 0, radius), tag=1)
labels = arbor.label_dict({'soma': '(tag 1)',
'center': '(location 0 0.5)'})
labels['root'] = '(root)'
decor = arbor.decor()
v_init = evaluate(cell.parameters['v_init'], nl_network.parameters) if 'v_init' in cell.parameters else -70
decor.set_property(Vm=v_init)
decor.paint('"soma"', cell.parameters['mechanism'])
if gid==0:
ic = arbor.iclamp( nl_network.parameters['input_del'], nl_network.parameters['input_dur'], nl_network.parameters['input_amp'])
print_v("Stim: %s"%ic)
decor.place('"center"', ic)
decor.place('"center"', arbor.spike_detector(-10))
# (2) Mark location for synapse at the midpoint of branch 1 (the first dendrite).
labels['synapse_site'] = '(location 0 0.5)'
# (4) Attach a single synapse.
decor.place('"synapse_site"', 'expsyn')
default_cell = arbor.cable_cell(default_tree, labels, decor)
print_v("Created a new cell for gid %i: %s"%(gid,cell))
print_v("%s"%(default_cell))
return default_cell
# (1) Create a morphology with a single (cylindrical) segment of length=diameter=6 μm
tree = arbor.segment_tree()
tree.append(arbor.mnpos,
arbor.mpoint(-3, 0, 0, 3),
arbor.mpoint(3, 0, 0, 3),
tag=1)
# (2) Define the soma and its midpoint
labels = arbor.label_dict({'soma': '(tag 1)', 'midpoint': '(location 0 0.5)'})
# (3) Create cell and set properties
decor = arbor.decor()
decor.set_property(Vm=-40)
decor.paint('"soma"', 'hh')
decor.place('"midpoint"', arbor.iclamp(10, 2, 0.8))
decor.place('"midpoint"', arbor.spike_detector(-10))
# (4) Create cell and the single cell model based on it
cell = arbor.cable_cell(tree, labels, decor)
# (5) Make single cell model.
m = arbor.single_cell_model(cell)
# (6) Attach voltage probe sampling at 10 kHz (every 0.1 ms).
m.probe('voltage', '"midpoint"', frequency=10000)
# (7) Run simulation for 30 ms of simulated activity.
m.run(tfinal=30)
# (8) Print spike times.
if len(m.spikes) > 0:
decor.set_ion('ca', method=mech('nernst/x=ca'))
#decor.set_ion('ca', method='nernst/x=ca')
# hh mechanism on the soma and axon.
decor.paint('"soma"', 'hh')
decor.paint('"axon"', 'hh')
# pas mechanism the dendrites.
decor.paint('"dend"', 'pas')
# Increase resistivity on dendrites.
decor.paint('"dend"', rL=500)
# Attach stimuli that inject 4 nA current for 1 ms, starting at 3 and 8 ms.
decor.place('"root"', arbor.iclamp(10, 1, current=5), "iclamp0")
decor.place('"stim_site"', arbor.iclamp(3, 1, current=0.5), "iclamp1")
decor.place('"stim_site"', arbor.iclamp(10, 1, current=0.5), "iclamp2")
decor.place('"stim_site"', arbor.iclamp(8, 1, current=4), "iclamp3")
# Detect spikes at the soma with a voltage threshold of -10 mV.
decor.place('"axon_end"', arbor.spike_detector(-10), "detector")
# Create the policy used to discretise the cell into CVs.
# Use a single CV for the soma, and CVs of maximum length 1 μm elsewhere.
soma_policy = arbor.cv_policy_single('"soma"')
dflt_policy = arbor.cv_policy_max_extent(1.0)
policy = dflt_policy | soma_policy
decor.discretization(policy)
# Combine morphology with region and locset definitions to make a cable cell.
cell = arbor.cable_cell(morpho, labels, decor)
print(cell.locations('axon_end'))
# Make single cell model.
m = arbor.single_cell_model(cell)
# Set initial membrane potential everywhere on the cell to -40 mV.
cell.set_properties(Vm=-40)
# Put hh dynamics on soma, and passive properties on the dendrites.
cell.paint('"soma"', 'hh')
cell.paint('"dend"', 'pas')
# Set axial resistivity in dendrite regions (Ohm.cm)
cell.paint('"dendn"', rL=500)
cell.paint('"dendx"', rL=10000)
# Attach stimuli with duration of 2 ms and current of 0.8 nA.
# There are three stimuli, which activate at 10 ms, 50 ms and 80 ms.
cell.place('"stim_site"', arbor.iclamp(10, 2, 0.8))
cell.place('"stim_site"', arbor.iclamp(50, 2, 0.8))
cell.place('"stim_site"', arbor.iclamp(80, 2, 0.8))
# Add a spike detector with threshold of -10 mV.
cell.place('"root"', arbor.spike_detector(-10))
# Discretization: the default discretization in Arbor is 1 compartment per branch.
# Let's be a bit more precise and make that every 2 micron:
cell.compartments_length(2)
# Make single cell model.
m = arbor.single_cell_model(cell)
# Attach voltage probes, sampling at 10 kHz.
m.probe('voltage', '(location 0 0)', 10000) # at the soma.
m.probe('voltage', '"dtips"', 10000) # at the tips of the dendrites.
# Run simulation for 100 ms of simulated activity.
tfinal = 100
m.run(tfinal)
def run_arb(fit, swc, current, t_start, t_stop):
tree = arbor.load_swc_allen(swc, no_gaps=False)
# Load mechanism data
with open(fit) as fd:
fit = json.load(fd)
## collect parameters in dict
mechs = defaultdict(dict)
### Passive parameters
ra = float(fit['passive'][0]['ra'])
### Remaining parameters
for block in fit['genome']:
mech = block['mechanism'] or 'pas'
region = block['section']
name = block['name']
if name.endswith('_' + mech):
name = name[:-(len(mech) + 1)]
mechs[(mech, region)][name] = float(block['value'])
# Label regions
labels = arbor.label_dict({
'soma': '(tag 1)',
'axon': '(tag 2)',
'dend': '(tag 3)',
'apic': '(tag 4)',
'center': '(location 0 0.5)'
})
properties = fit['conditions'][0]
T = properties['celsius'] + 273.15
Vm = properties['v_init']
# Run simulation
morph = arbor.morphology(tree)
# Build cell and attach Clamp and Detector
cell = arbor.cable_cell(morph, labels)
cell.place('center', arbor.iclamp(t_start, t_stop - t_start, current))
cell.place('center', arbor.spike_detector(-40))
cell.compartments_length(20)
# read json file and proceed to set parameters and mechanisms
# set global values
print('Setting global parameters')
print(f" * T = {T}K = {T - 273.15}C")
print(f" * Vm = {Vm}mV")
cell.set_properties(tempK=T, Vm=Vm, rL=ra)
# Set reversal potentials
print("Setting reversal potential for")
for kv in properties['erev']:
region = kv['section']
for k, v in kv.items():
if k == 'section':
continue
ion = k[1:]
print(f' * region {region:6} species {ion:5}: {v:10}')
cell.paint(region, arbor.ion(ion, rev_pot=float(v)))
cell.set_ion('ca',
int_con=5e-5,
ext_con=2.0,
method=arbor.mechanism('default_nernst/x=ca'))
# Setup mechanisms and parameters
print('Setting up mechanisms')
## Now paint the cell using the dict
for (mech, region), vs in mechs.items():
print(f" * {region:10} -> {mech:10}: {str(vs):>60}", end=' ')
try:
if mech != 'pas':
m = arbor.mechanism(mech, vs)
cell.paint(region, m)
else:
m = arbor.mechanism('default_pas', {
'e': vs['e'],
'g': vs['g']
})
cell.paint(region, m)
cell.paint(region, cm=vs["cm"] / 100, rL=vs["Ra"])
print("OK")
except Exception as e:
print("ERROR")
print("When trying to set", mech, vs)
print(" ->", e)
exit()
# Run the simulation, collecting voltages
print('Simulation', end=' ')
default = arbor.default_catalogue()
catalogue = arbor.allen_catalogue()
catalogue.extend(default, 'default_')
model = arbor.single_cell_model(cell)
model.properties.catalogue = catalogue
model.probe('voltage', 'center', frequency=200000)
model.run(tfinal=t_start + t_stop, dt=1000 / 200000)
print('DONE')
for t in model.traces:
ts = t.time[:]
vs = t.value[:]
break
spikes = np.array(model.spikes)
count = len(spikes)
print('Counted spikes', count)
return np.array(ts), np.array(vs) + 14
decor.paint('"all"', 'ca_boyle')
# Set calcium ion concentration.
decor.set_ion('ca', int_con=0, ext_con=2.0, rev_pot = 40.0)
# Set potassium reversal potential
decor.set_ion('k', rev_pot=-60.0)
# Place stimulus and a spike detector.
# Stimulation at the center of the soma from t = 100 ms until t = 400 ms.
# offset current "value"
decor.place('"center"', arbor.iclamp(100, 300, value))
# Spike detection at center of soma.
decor.place('"center"', arbor.spike_detector(-26))
#define CV policy and add it to decor
#policy = arbor.cv_policy_every_segment('(all)')
policy = arbor.cv_policy_fixed_per_branch(1, '(all)')
#policy = arbor.cv_policy_max_extent(1.0)
decor.discretization(policy)
# (4) Create cell.
dd1_cell = arbor.cable_cell(morph, labels, decor)
# (5) Create probes for membrane voltage and calcium ion concentration.
voltage_probe = arbor.cable_probe_membrane_voltage('"center"')
ca_conc_probe = arbor.cable_probe_ion_int_concentration('"center"', "ca")
# Set initial membrane potential to -55 mV
cell.set_properties(Vm=-55)
# Use Nernst to calculate reversal potential for calcium.
cell.set_ion('ca', method=mech('nernst/x=ca'))
# hh mechanism on the soma and axon.
cell.paint('"soma"', 'hh')
cell.paint('"axon"', 'hh')
# pas mechanism the dendrites.
cell.paint('"dend"', 'pas')
# Increase resistivity on dendrites.
cell.paint('"dend"', rL=500)
# Attach stimuli that inject 0.8 nA currents for 1 ms, starting at 3 and 8 ms.
cell.place('"stim_site"', arbor.iclamp(3, 1, current=2))
cell.place('"stim_site"', arbor.iclamp(8, 1, current=4))
# Detect spikes at the soma with a voltage threshold of -10 mV.
cell.place('"axon_end"', arbor.spike_detector(-10))
# Have one compartment between each sample point.
cell.compartments_on_segments()
# Make single cell model.
m = arbor.single_cell_model(cell)
# Attach voltage probes that sample at 50 kHz.
m.probe('voltage', where='"root"', frequency=50000)
m.probe('voltage', where=loc(2, 1), frequency=50000)
m.probe('voltage', where='"stim_site"', frequency=50000)
m.probe('voltage', where='"axon_end"', frequency=50000)
# Simulate the cell for 15 ms.
tfinal = 15
'center': '(location 0 0.5)'})
cell = arb.cable_cell(morphology, labels) # see !\circled{3}!
cell.compartments_length(20) # discretisation strategy: max compartment length
# !\circled{4}! load and assign electro-physical parameters
defaults, regions, ions, mechanisms = utils.load_allen_fit('fit.json')
# set defaults and override by region
cell.set_properties(tempK=defaults.tempK, Vm=defaults.Vm,
cm=defaults.cm, rL=defaults.rL)
for region, vs in regions:
cell.paint('"'+region+'"', tempK=vs.tempK, Vm=vs.Vm, cm=vs.cm, rL=vs.rL)
# set reversal potentials
for region, ion, e in ions:
cell.paint('"'+region+'"', ion, rev_pot=e)
cell.set_ion('ca', int_con=5e-5, ext_con=2.0, method=arb.mechanism('nernst/x=ca'))
# assign ion dynamics
for region, mech, values in mechanisms:
cell.paint('"'+region+'"', arb.mechanism(mech, values))
print(mech)
# !\circled{5}! attach stimulus and spike detector
cell.place('"center"', arb.iclamp(200, 1000, 0.15))
cell.place('"center"', arb.spike_detector(-40))
# !\circled{6}! set up runnable simulation
model = arb.single_cell_model(cell)
model.probe('voltage', '"center"', frequency=200000) # see !\circled{5}!
# !\circled{7}! assign catalogues
model.properties.catalogue = arb.allen_catalogue()
model.properties.catalogue.extend(arb.default_catalogue(), '')
# !\circled{8}! run simulation and plot results
model.run(tfinal=1400, dt=0.005)
utils.plot_results(model)
# neuroML: <channelDensity id="ca_boyle_all" ionChannel="ca_boyle" condDensity="2 mS_per_cm2" erev="40 mV" ion="ca"/>
ca_boyle_all = arbor.mechanism("ca_boyle")
# put hh dynamics on soma and passive properties on the dendrites
#dd1_cell.paint('"soma"', 'Leak')
dd1_cell.paint('"soma"', 'k_slow')
dd1_cell.paint('"soma"', 'k_fast')
dd1_cell.paint('"soma"', 'CaPoolTH')
dd1_cell.paint('"soma"', 'ca_boyle')
#dd1_cell.paint('"soma"', 'hh')
dd1_cell.place('"center"', arbor.iclamp(100, 300, 0.0087))
dd1_cell.place('"center"', arbor.spike_detector(-26))
# (4) Make single cell model.
m = arbor.single_cell_model(dd1_cell)
# (5) Attach voltage probe sampling at 10 kHz (every 0.1 ms).
m.probe('voltage', '"center"', frequency=10000)
# (6) Run simulation for 30 ms of simulated activity.
m.run(tfinal=500)
# (7) Print spike times, if any.
if len(m.spikes)>0:
print('{} spikes:'.format(len(m.spikes)))
for s in m.spikes:
# Set initial membrane potential everywhere on the cell to -40 mV.
cell.set_properties(Vm=-40)
# Put hh dynamics on soma, and passive properties on the dendrites.
cell.paint('soma', 'hh')
cell.paint('dend', 'pas')
# Set axial resistivity in dendrite regions (Ohm.cm)
cell.paint('dendn', rL=500)
cell.paint('dendx', rL=10000)
# Attach stimuli with duration of 2 ms and current of 0.8 nA.
# There are three stimuli, which activate at 10 ms, 50 ms and 80 ms.
cell.place('stim_site', arbor.iclamp(10, 2, 0.8))
cell.place('stim_site', arbor.iclamp(50, 2, 0.8))
cell.place('stim_site', arbor.iclamp(80, 2, 0.8))
# Add a spike detector with threshold of -10 mV.
cell.place('root', arbor.spike_detector(-10))
# Make single cell model.
m = arbor.single_cell_model(cell)
# Attach voltage probes, sampling at 10 kHz.
m.probe('voltage', loc(0, 0), 10000) # at the soma.
m.probe('voltage', 'dtips', 10000) # at the tips of the dendrites.
# Run simulation for 100 ms of simulated activity.
tfinal = 100
m.run(tfinal)
# Print spike times.
if len(m.spikes) > 0:
print('{} spikes:'.format(len(m.spikes)))
# Create a sample tree with a single sample of radius 3 μm
tree = arbor.sample_tree()
tree.append(arbor.msample(x=0, y=0, z=0, radius=3, tag=2))
labels = arbor.label_dict({'soma': '(tag 2)', 'center': '(location 0 0.5)'})
cell = arbor.cable_cell(tree, labels)
# Set initial membrane potential everywhere on the cell to -40 mV.
cell.set_properties(Vm=-40)
# Put hh dynamics on soma, and passive properties on the dendrites.
cell.paint('soma', 'hh')
# Attach stimuli with duration of 2 ms and current of 0.8 nA.
cell.place('center', arbor.iclamp(10, 2, 0.8))
# Add a spike detector with threshold of -10 mV.
cell.place('center', arbor.spike_detector(-10))
# Make single cell model.
m = arbor.single_cell_model(cell)
# Attach voltage probes, sampling at 10 kHz.
m.probe('voltage', 'center', 10000)
# Run simulation for 100 ms of simulated activity.
tfinal = 30
m.run(tfinal)
# Print spike times.
if len(m.spikes) > 0:
print('{} spikes:'.format(len(m.spikes)))
for s in m.spikes:
def create_arbor_cell(self, cell, gid, pop_id, index):
if cell.arbor_cell == "cable_cell":
default_tree = arbor.segment_tree()
radius = (evaluate(cell.parameters["radius"],
self.nl_network.parameters)
if "radius" in cell.parameters else 3)
default_tree.append(
arbor.mnpos,
arbor.mpoint(-1 * radius, 0, 0, radius),
arbor.mpoint(radius, 0, 0, radius),
tag=1,
)
labels = arbor.label_dict({
"soma": "(tag 1)",
"center": "(location 0 0.5)"
})
labels["root"] = "(root)"
decor = arbor.decor()
v_init = (evaluate(cell.parameters["v_init"],
self.nl_network.parameters)
if "v_init" in cell.parameters else -70)
decor.set_property(Vm=v_init)
decor.paint('"soma"', arbor.density(cell.parameters["mechanism"]))
decor.place('"center"', arbor.spike_detector(0), "detector")
for ip in self.input_info:
if self.input_info[ip][0] == pop_id:
print_v("Stim: %s (%s) being placed on %s" %
(ip, self.input_info[ip], pop_id))
for il in self.input_lists[ip]:
cellId, segId, fract, weight = il
if cellId == index:
if self.input_info[ip][
1] == 'i_clamp': # TODO: remove hardcoding of this...
ic = arbor.iclamp(
self.nl_network.parameters["input_del"],
self.nl_network.parameters["input_dur"],
self.nl_network.parameters["input_amp"],
)
print_v("Stim: %s on %s" % (ic, gid))
decor.place('"center"', ic, "iclamp")
# (2) Mark location for synapse at the midpoint of branch 1 (the first dendrite).
labels["synapse_site"] = "(location 0 0.5)"
# (4) Attach a single synapse.
decor.place('"synapse_site"', arbor.synapse("expsyn"), "syn")
default_cell = arbor.cable_cell(default_tree, labels, decor)
print_v("Created a new cell for gid %i: %s" % (gid, cell))
print_v("%s" % (default_cell))
return default_cell
decor.paint('"custom"', tempK=270)
decor.paint('"soma"', Vm=-50)
# Paint density mechanisms.
decor.paint('"all"', 'pas')
decor.paint('"custom"', 'hh')
decor.paint('"dend"', mech('Ih', {'gbar': 0.001}))
# Place stimuli and spike detectors.
decor.place('"root"', arbor.iclamp(10, 1, current=2), "iclamp0")
decor.place('"root"', arbor.iclamp(30, 1, current=2), "iclamp1")
decor.place('"root"', arbor.iclamp(50, 1, current=2), "iclamp2")
decor.place('"axon_terminal"', arbor.spike_detector(-10), "detector")
# Set cv_policy
soma_policy = arbor.cv_policy_single('"soma"')
dflt_policy = arbor.cv_policy_max_extent(1.0)
policy = dflt_policy | soma_policy
decor.discretization(policy)
# Create a cell
cell = arbor.cable_cell(morph, labels, decor)
# (2) Declare a probe.
probe = arbor.cable_probe_membrane_voltage('"custom_terminal"')
tree = arbor.segment_tree()
tree.append(arbor.mnpos,
arbor.mpoint(-3, 0, 0, 3),
arbor.mpoint(3, 0, 0, 3),
tag=1)
# Label dictionary
labels = arbor.label_dict()
labels['centre'] = '(location 0 0.5)'
# Decorations
decor = arbor.decor()
decor.set_property(Vm=-40)
decor.paint('(all)', 'hh')
decor.place('"centre"', arbor.iclamp(10, 2, 0.8))
decor.place('"centre"', arbor.spike_detector(-10))
cell = arbor.cable_cell(tree, labels, decor)
# (3) Instantiate recipe with a voltage probe.
recipe = single_recipe(cell, [arbor.cable_probe_membrane_voltage('"centre"')])
# (4) Instantiate simulation and set up sampling on probe id (0, 0).
context = arbor.context()
domains = arbor.partition_load_balance(recipe, context)
sim = arbor.simulation(recipe, domains, context)
sim.record(arbor.spike_recording.all)
handle = sim.sample((0, 0), arbor.regular_schedule(0.1))