def test_pynn_neuron_std(testable_component):
t = testable_component
flg = 'supports_test_pynn_neuron_std'
assert t.metadata.__dict__.get(flg, False)
std_pynn_simulation(
test_component=testable_component(),
parameters=t.metadata.parameters,
initial_values=t.metadata.initial_values,
synapse_components=t.metadata.synapse_components,
records=t.metadata.records
)
def functest(self):
cc = ComponentClass(
name='PulsingCurrentClamp',
parameters=['i', 'cycle_length'],
analog_ports=[SendPort('I')],
regimes=[
Regime(
name='off',
transitions=On('t > tchange + cycle_length',
do=['tchange = t', 'I = 0'],
to='on'),
),
Regime(
name='on',
transitions=On('t > tchange + cycle_length',
do=['tchange = t', 'I = i'],
to='off'),
),
])
nrn = ComponentClass(
name='LeakyNeuron',
parameters=['Cm', 'gL', 'E'],
regimes=[
Regime('dV/dt = (iInj + (E-V)*gL )/Cm'),
],
analog_ports=[SendPort('V'),
ReducePort('iInj', reduce_op='+')],
)
combined_comp = ComponentClass(name='Comp1',
subnodes={
'nrn': nrn,
'cc1': cc
},
portconnections=[('cc1.I', 'nrn.iInj')])
records = [
RecordValue(what='cc1_I', tag='Current', label='Current Clamp 1'),
RecordValue(what='nrn_V', tag='Voltage', label='Neuron Voltage'),
RecordValue(what='cc1_tchange', tag='Tchange', label='tChange'),
RecordValue(what='regime', tag='Regime', label='Regime'),
]
parameters = {
'cc1_i': 13.8,
'cc1_cycle_length': 20,
'nrn_gL': 2,
'nrn_E': -70
}
results = std_pynn_simulation(test_component=combined_comp,
parameters=parameters,
initial_values={},
synapse_components=[],
records=records,
plot=True)
def functest(self):
cc = ComponentClass(
name='PulsingCurrentClamp',
parameters=['i', 'cycle_length'],
analog_ports=[SendPort('I')],
regimes=[
Regime(name='off',
transitions=On(
't > tchange + cycle_length', do=['tchange = t', 'I = 0'], to='on'),
),
Regime(name='on',
transitions=On(
't > tchange + cycle_length', do=['tchange = t', 'I = i'], to='off'),
),
]
)
nrn = ComponentClass(
name='LeakyNeuron',
parameters=['Cm', 'gL', 'E'],
regimes=[Regime('dV/dt = (iInj + (E-V)*gL )/Cm'), ],
analog_ports=[SendPort('V'),
ReducePort('iInj', reduce_op='+')],
)
combined_comp = ComponentClass(name='Comp1',
subnodes={'nrn': nrn, 'cc1': cc},
portconnections=[('cc1.I', 'nrn.iInj')])
from nineml.abstraction_layer.testing_utils import std_pynn_simulation
from nineml.abstraction_layer.testing_utils import RecordValue
records = [
RecordValue(what='cc1_I', tag='Current', label='Current Clamp 1'),
RecordValue(what='nrn_V', tag='Voltage', label='Neuron Voltage'),
RecordValue(what='cc1_tchange', tag='Tchange', label='tChange'),
RecordValue(what='regime', tag='Regime', label='Regime'),
]
parameters = {
'cc1_i': 13.8,
'cc1_cycle_length': 20,
'nrn_gL': 2,
'nrn_E': -70}
results = std_pynn_simulation(test_component=combined_comp,
parameters=parameters,
initial_values={},
synapse_components=[],
records=records,
plot=True
)
def test1(self):
cc = ComponentClass(
name='SimpleCurrentClamp',
parameters=['i'],
analog_ports=[SendPort('I')],
aliases='I:=i',
)
nrn = ComponentClass(
name='LeakyNeuron',
parameters=['Cm', 'gL', 'E'],
regimes=[
Regime('dV/dt = (iInj + (E-V)*gL )/Cm'),
],
analog_ports=[SendPort('V'),
ReducePort('iInj', reduce_op='+')],
)
combined_comp = ComponentClass(name='Comp1',
subnodes={
'nrn': nrn,
'cc1': cc
},
portconnections=[('cc1.I', 'nrn.iInj')])
records = [
RecordValue(what='cc1_I', tag='Current', label='Current Clamp 1'),
RecordValue(what='nrn_V', tag='Voltage', label='Neuron Voltage'),
]
res = std_pynn_simulation(test_component=combined_comp,
parameters={
'cc1_i': 13.8,
'nrn_gL': 2,
'nrn_E': -70
},
initial_values={},
synapse_components=[],
records=records,
plot=False)
t, records = res
self.assertAlmostEqual(records['cc1_I'][t > 10].mean(), 13.8)
self.assertAlmostEqual(records['cc1_I'][t > 10].std(), 0.0)
self.assertAlmostEqual(records['nrn_V'][t > 10].mean(), -63.1)
self.assertAlmostEqual(records['nrn_V'][t > 10].std(), 0.0)
def test1(self):
cc = ComponentClass(
name='SimpleCurrentClamp',
parameters=['i'],
analog_ports=[SendPort('I')],
aliases='I:=i',
)
nrn = ComponentClass(
name='LeakyNeuron',
parameters=['Cm', 'gL', 'E'],
regimes=[Regime('dV/dt = (iInj + (E-V)*gL )/Cm'), ],
analog_ports=[SendPort('V'),
ReducePort('iInj', reduce_op='+')],
)
combined_comp = ComponentClass(name='Comp1',
subnodes={'nrn': nrn, 'cc1': cc},
portconnections=[('cc1.I', 'nrn.iInj')])
from nineml.abstraction_layer.testing_utils import std_pynn_simulation
from nineml.abstraction_layer.testing_utils import RecordValue
records = [
RecordValue(what='cc1_I', tag='Current', label='Current Clamp 1'),
RecordValue(what='nrn_V', tag='Voltage', label='Neuron Voltage'),
]
res = std_pynn_simulation(test_component=combined_comp,
parameters={'cc1_i': 13.8, 'nrn_gL': 2, 'nrn_E': -70},
initial_values={},
synapse_components=[],
records=records,
plot=False
)
t, records = res
self.assertAlmostEqual(records['cc1_I'][t > 10].mean(), 13.8)
self.assertAlmostEqual(records['cc1_I'][t > 10].std(), 0.0)
self.assertAlmostEqual(records['nrn_V'][t > 10].mean(), -63.1)
self.assertAlmostEqual(records['nrn_V'][t > 10].std(), 0.0)
writers.DotWriter.write(iz, 'TestOut_Iz.dot')
writers.DotWriter.build('TestOut_Iz.dot')
# Simulate the Neuron:
records = [
RecordValue(what='V', tag='V', label='V'),
# RecordValue(what='U', tag='U', label='U'),
# RecordValue( what='regime', tag='Regime', label='Regime' ),
]
parameters = flattening.ComponentFlattener.flatten_namespace_dict({
'a':
0.02,
'b':
0.2,
'c':
-65,
'd':
8,
'iinj_constant':
50.0,
})
res = std_pynn_simulation(
test_component=iz,
parameters=parameters,
initial_values={},
synapse_components=[],
records=records,
)
def func_test(self):
emitter = ComponentClass(
name='EventEmitter',
parameters=['cyclelength'],
regimes=[
Regime(transitions=On('t > tchange + cyclelength',
do=[OutputEvent('emit'), 'tchange=t'])),
])
ev_based_cc = ComponentClass(
name='EventBasedCurrentClass',
parameters=['dur', 'i'],
analog_ports=[SendPort('I')],
regimes=[
Regime(transitions=[
On('inputevent', do=['I=i', 'tchange = t']),
On('t>tchange + dur', do=['I=0', 'tchange=t'])
])
])
pulsing_emitter = ComponentClass(name='pulsing_cc',
subnodes={
'evs': emitter,
'cc': ev_based_cc
},
portconnections=[('evs.emit',
'cc.inputevent')])
nrn = ComponentClass(
name='LeakyNeuron',
parameters=['Cm', 'gL', 'E'],
regimes=[
Regime('dV/dt = (iInj + (E-V)*gL )/Cm'),
],
aliases=['iIn := iInj'],
analog_ports=[SendPort('V'),
ReducePort('iInj', reduce_op='+')],
)
combined_comp = ComponentClass(name='Comp1',
subnodes={
'nrn': nrn,
'cc1': pulsing_emitter,
'cc2': pulsing_emitter
},
portconnections=[
('cc1.cc.I', 'nrn.iInj'),
('cc2.cc.I', 'nrn.iInj')
])
combined_comp = flattening.flatten(combined_comp)
records = [
RecordValue(what='cc1_cc_I',
tag='Current',
label='Current Clamp 1'),
RecordValue(what='cc2_cc_I',
tag='Current',
label='Current Clamp 2'),
RecordValue(what='nrn_iIn',
tag='Current',
label='Total Input Current'),
RecordValue(what='nrn_V', tag='Voltage', label='Neuron Voltage'),
RecordValue(what='cc1_cc_tchange',
tag='Tchange',
label='tChange CC1'),
RecordValue(what='cc2_cc_tchange',
tag='Tchange',
label='tChange CC2'),
RecordValue(what='regime', tag='Regime', label='Regime'),
]
parameters = flattening.ComponentFlattener.flatten_namespace_dict({
'cc1.cc.i':
13.8,
'cc1.cc.dur':
10,
'cc1.evs.cyclelength':
30,
'cc2.cc.i':
20.8,
'cc2.cc.dur':
5.0,
'cc2.evs.cyclelength':
20,
'nrn.gL':
4.3,
'nrn.E':
-70
})
res = std_pynn_simulation(
test_component=combined_comp,
parameters=parameters,
initial_values={},
synapse_components=[],
records=records,
# plot = False,
)
return
t, records = res
def check_trace(trace_name, time_period, exp_mean, exp_std=0):
t_indices = (t > time_period[0] + 1) & (t < time_period[1] - 1)
self.assertAlmostEqual(records[trace_name][t_indices].mean(),
exp_mean,
places=3)
self.assertAlmostEqual(records[trace_name][t_indices].std(),
exp_std,
places=3)
check_trace('cc1_cc_I', (00, 30), exp_mean=0.0)
check_trace('cc1_cc_I', (30, 40), exp_mean=13.8)
check_trace('cc1_cc_I', (40, 60), exp_mean=0.0)
check_trace('cc1_cc_I', (60, 70), exp_mean=13.8)
check_trace('cc1_cc_I', (70, 90), exp_mean=0.0)
check_trace('cc1_cc_I', (90, 100), exp_mean=13.8)
check_trace('cc2_cc_I', (00, 20), exp_mean=0.0)
check_trace('cc2_cc_I', (20, 25), exp_mean=20.8)
check_trace('cc2_cc_I', (25, 40), exp_mean=0.0)
check_trace('cc2_cc_I', (40, 45), exp_mean=20.8)
check_trace('cc2_cc_I', (45, 60), exp_mean=0.0)
check_trace('cc2_cc_I', (60, 65), exp_mean=20.8)
check_trace('cc2_cc_I', (65, 80), exp_mean=0.0)
check_trace('cc2_cc_I', (80, 85), exp_mean=20.8)
check_trace('cc2_cc_I', (85, 100), exp_mean=0.0)
check_trace('nrn_iIn', (00, 20), exp_mean=0.0)
check_trace('nrn_iIn', (20, 25), exp_mean=20.8)
check_trace('nrn_iIn', (25, 30), exp_mean=0.0)
check_trace('nrn_iIn', (30, 40), exp_mean=13.8)
check_trace('nrn_iIn', (40, 45), exp_mean=20.8)
check_trace('nrn_iIn', (45, 60), exp_mean=0.0)
check_trace('nrn_iIn', (60, 65), exp_mean=34.6)
check_trace('nrn_iIn', (65, 70), exp_mean=13.8)
check_trace('nrn_iIn', (70, 80), exp_mean=0.0)
check_trace('nrn_iIn', (80, 85), exp_mean=20.8)
check_trace('nrn_iIn', (85, 90), exp_mean=0.0)
check_trace('nrn_iIn', (90, 100), exp_mean=13.8)
check_trace('nrn_V', (00 + 2, 20), exp_mean=(0.0 / 4.3) - 70)
check_trace('nrn_V', (20 + 2, 25), exp_mean=(20.8 / 4.3) - 70)
check_trace('nrn_V', (25 + 2, 30), exp_mean=(0.0 / 4.3) - 70)
check_trace('nrn_V', (30 + 2, 40), exp_mean=(13.8 / 4.3) - 70)
check_trace('nrn_V', (40 + 2, 45), exp_mean=(20.8 / 4.3) - 70)
check_trace('nrn_V', (45 + 2, 60), exp_mean=(0.0 / 4.3) - 70)
check_trace('nrn_V', (60 + 2, 65), exp_mean=(34.6 / 4.3) - 70)
check_trace('nrn_V', (65 + 2, 70), exp_mean=(13.8 / 4.3) - 70)
check_trace('nrn_V', (70 + 2, 80), exp_mean=(0.0 / 4.3) - 70)
check_trace('nrn_V', (80 + 2, 85), exp_mean=(20.8 / 4.3) - 70)
check_trace('nrn_V', (85 + 2, 90), exp_mean=(0.0 / 4.3) - 70)
check_trace('nrn_V', (90 + 2, 100), exp_mean=(13.8 / 4.3) - 70)
def func_test(self):
emitter = ComponentClass(
name='EventEmitter',
parameters=['cyclelength'],
regimes=[
Regime(
transitions=On(
't > tchange + cyclelength', do=[OutputEvent('emit'), 'tchange=t'])),
])
ev_based_cc = ComponentClass(
name='EventBasedCurrentClass',
parameters=['dur', 'i'],
analog_ports=[SendPort('I')],
regimes=[
Regime(
transitions=[
On('inputevent', do=['I=i', 'tchange = t']),
On('t>tchange + dur', do=['I=0', 'tchange=t'])
]
)
]
)
pulsing_emitter = ComponentClass(name='pulsing_cc',
subnodes={'evs': emitter, 'cc': ev_based_cc},
portconnections=[('evs.emit', 'cc.inputevent')]
)
nrn = ComponentClass(
name='LeakyNeuron',
parameters=['Cm', 'gL', 'E'],
regimes=[Regime('dV/dt = (iInj + (E-V)*gL )/Cm'), ],
aliases=['iIn := iInj'],
analog_ports=[SendPort('V'),
ReducePort('iInj', reduce_op='+')],
)
combined_comp = ComponentClass(name='Comp1',
subnodes={
'nrn': nrn, 'cc1': pulsing_emitter, 'cc2': pulsing_emitter},
portconnections=[('cc1.cc.I', 'nrn.iInj'),
('cc2.cc.I', 'nrn.iInj')]
)
combined_comp = nineml.al.flattening.flatten(combined_comp)
records = [
RecordValue(what='cc1_cc_I', tag='Current', label='Current Clamp 1'),
RecordValue(what='cc2_cc_I', tag='Current', label='Current Clamp 2'),
RecordValue(what='nrn_iIn', tag='Current', label='Total Input Current'),
RecordValue(what='nrn_V', tag='Voltage', label='Neuron Voltage'),
RecordValue(what='cc1_cc_tchange', tag='Tchange', label='tChange CC1'),
RecordValue(what='cc2_cc_tchange', tag='Tchange', label='tChange CC2'),
RecordValue(what='regime', tag='Regime', label='Regime'),
]
parameters = nineml.al.flattening.ComponentFlattener.flatten_namespace_dict({
'cc1.cc.i': 13.8,
'cc1.cc.dur': 10,
'cc1.evs.cyclelength': 30,
'cc2.cc.i': 20.8,
'cc2.cc.dur': 5.0,
'cc2.evs.cyclelength': 20,
'nrn.gL': 4.3,
'nrn.E': -70})
res = std_pynn_simulation(test_component=combined_comp,
parameters=parameters,
initial_values={},
synapse_components=[],
records=records,
# plot = False,
)
return
t, records = res
def check_trace(trace_name, time_period, exp_mean, exp_std=0):
t_indices = (t > time_period[0] + 1) & (t < time_period[1] - 1)
self.assertAlmostEqual(records[trace_name][t_indices].mean(), exp_mean, places=3)
self.assertAlmostEqual(records[trace_name][t_indices].std(), exp_std, places=3)
check_trace('cc1_cc_I', (00, 30), exp_mean=0.0)
check_trace('cc1_cc_I', (30, 40), exp_mean=13.8)
check_trace('cc1_cc_I', (40, 60), exp_mean=0.0)
check_trace('cc1_cc_I', (60, 70), exp_mean=13.8)
check_trace('cc1_cc_I', (70, 90), exp_mean=0.0)
check_trace('cc1_cc_I', (90, 100), exp_mean=13.8)
check_trace('cc2_cc_I', (00, 20), exp_mean=0.0)
check_trace('cc2_cc_I', (20, 25), exp_mean=20.8)
check_trace('cc2_cc_I', (25, 40), exp_mean=0.0)
check_trace('cc2_cc_I', (40, 45), exp_mean=20.8)
check_trace('cc2_cc_I', (45, 60), exp_mean=0.0)
check_trace('cc2_cc_I', (60, 65), exp_mean=20.8)
check_trace('cc2_cc_I', (65, 80), exp_mean=0.0)
check_trace('cc2_cc_I', (80, 85), exp_mean=20.8)
check_trace('cc2_cc_I', (85, 100), exp_mean=0.0)
check_trace('nrn_iIn', (00, 20), exp_mean=0.0)
check_trace('nrn_iIn', (20, 25), exp_mean=20.8)
check_trace('nrn_iIn', (25, 30), exp_mean=0.0)
check_trace('nrn_iIn', (30, 40), exp_mean=13.8)
check_trace('nrn_iIn', (40, 45), exp_mean=20.8)
check_trace('nrn_iIn', (45, 60), exp_mean=0.0)
check_trace('nrn_iIn', (60, 65), exp_mean=34.6)
check_trace('nrn_iIn', (65, 70), exp_mean=13.8)
check_trace('nrn_iIn', (70, 80), exp_mean=0.0)
check_trace('nrn_iIn', (80, 85), exp_mean=20.8)
check_trace('nrn_iIn', (85, 90), exp_mean=0.0)
check_trace('nrn_iIn', (90, 100), exp_mean=13.8)
check_trace('nrn_V', (00 + 2, 20), exp_mean=(0.0 / 4.3) - 70)
check_trace('nrn_V', (20 + 2, 25), exp_mean=(20.8 / 4.3) - 70)
check_trace('nrn_V', (25 + 2, 30), exp_mean=(0.0 / 4.3) - 70)
check_trace('nrn_V', (30 + 2, 40), exp_mean=(13.8 / 4.3) - 70)
check_trace('nrn_V', (40 + 2, 45), exp_mean=(20.8 / 4.3) - 70)
check_trace('nrn_V', (45 + 2, 60), exp_mean=(0.0 / 4.3) - 70)
check_trace('nrn_V', (60 + 2, 65), exp_mean=(34.6 / 4.3) - 70)
check_trace('nrn_V', (65 + 2, 70), exp_mean=(13.8 / 4.3) - 70)
check_trace('nrn_V', (70 + 2, 80), exp_mean=(0.0 / 4.3) - 70)
check_trace('nrn_V', (80 + 2, 85), exp_mean=(20.8 / 4.3) - 70)
check_trace('nrn_V', (85 + 2, 90), exp_mean=(0.0 / 4.3) - 70)
check_trace('nrn_V', (90 + 2, 100), exp_mean=(13.8 / 4.3) - 70)
'tspike': -1e99,
'regime': 1002,
}
synapse_components = [
( 'nmda', 'weight' ),
( 'cobaExcit', 'q' ),
]
records = [
RecordValue( what='iaf_V', tag='Voltage [mV]', label='Membrane Voltage' ),
RecordValue( what='nmda_g', tag='Conductance [ns]', label='NMDA-g' ),
RecordValue( what='cobaExcit_g', tag='Conductance [ns]', label='cobaexcit-g' ),
RecordValue( what='regime', tag='Regime', label='Regime' ),
]
std_pynn_simulation( test_component = test_component,
parameters = parameters,
initial_values = initial_values,
synapse_components = synapse_components,
records = records
)
def func_test(self):
emitter = ComponentClass(
name="EventEmitter",
parameters=["cyclelength"],
regimes=[Regime(transitions=On("t > tchange + cyclelength", do=[OutputEvent("emit"), "tchange=t"]))],
)
ev_based_cc = ComponentClass(
name="EventBasedCurrentClass",
parameters=["dur", "i"],
analog_ports=[SendPort("I")],
regimes=[
Regime(
transitions=[
On("inputevent", do=["I=i", "tchange = t"]),
On("t>tchange + dur", do=["I=0", "tchange=t"]),
]
)
],
)
pulsing_emitter = ComponentClass(
name="pulsing_cc",
subnodes={"evs": emitter, "cc": ev_based_cc},
portconnections=[("evs.emit", "cc.inputevent")],
)
nrn = ComponentClass(
name="LeakyNeuron",
parameters=["Cm", "gL", "E"],
regimes=[Regime("dV/dt = (iInj + (E-V)*gL )/Cm")],
aliases=["iIn := iInj"],
analog_ports=[SendPort("V"), ReducePort("iInj", reduce_op="+")],
)
combined_comp = ComponentClass(
name="Comp1",
subnodes={"nrn": nrn, "cc1": pulsing_emitter, "cc2": pulsing_emitter},
portconnections=[("cc1.cc.I", "nrn.iInj"), ("cc2.cc.I", "nrn.iInj")],
)
combined_comp = flattening.flatten(combined_comp)
records = [
RecordValue(what="cc1_cc_I", tag="Current", label="Current Clamp 1"),
RecordValue(what="cc2_cc_I", tag="Current", label="Current Clamp 2"),
RecordValue(what="nrn_iIn", tag="Current", label="Total Input Current"),
RecordValue(what="nrn_V", tag="Voltage", label="Neuron Voltage"),
RecordValue(what="cc1_cc_tchange", tag="Tchange", label="tChange CC1"),
RecordValue(what="cc2_cc_tchange", tag="Tchange", label="tChange CC2"),
RecordValue(what="regime", tag="Regime", label="Regime"),
]
parameters = flattening.ComponentFlattener.flatten_namespace_dict(
{
"cc1.cc.i": 13.8,
"cc1.cc.dur": 10,
"cc1.evs.cyclelength": 30,
"cc2.cc.i": 20.8,
"cc2.cc.dur": 5.0,
"cc2.evs.cyclelength": 20,
"nrn.gL": 4.3,
"nrn.E": -70,
}
)
res = std_pynn_simulation(
test_component=combined_comp,
parameters=parameters,
initial_values={},
synapse_components=[],
records=records,
# plot = False,
)
return
t, records = res
def check_trace(trace_name, time_period, exp_mean, exp_std=0):
t_indices = (t > time_period[0] + 1) & (t < time_period[1] - 1)
self.assertAlmostEqual(records[trace_name][t_indices].mean(), exp_mean, places=3)
self.assertAlmostEqual(records[trace_name][t_indices].std(), exp_std, places=3)
check_trace("cc1_cc_I", (00, 30), exp_mean=0.0)
check_trace("cc1_cc_I", (30, 40), exp_mean=13.8)
check_trace("cc1_cc_I", (40, 60), exp_mean=0.0)
check_trace("cc1_cc_I", (60, 70), exp_mean=13.8)
check_trace("cc1_cc_I", (70, 90), exp_mean=0.0)
check_trace("cc1_cc_I", (90, 100), exp_mean=13.8)
check_trace("cc2_cc_I", (00, 20), exp_mean=0.0)
check_trace("cc2_cc_I", (20, 25), exp_mean=20.8)
check_trace("cc2_cc_I", (25, 40), exp_mean=0.0)
check_trace("cc2_cc_I", (40, 45), exp_mean=20.8)
check_trace("cc2_cc_I", (45, 60), exp_mean=0.0)
check_trace("cc2_cc_I", (60, 65), exp_mean=20.8)
check_trace("cc2_cc_I", (65, 80), exp_mean=0.0)
check_trace("cc2_cc_I", (80, 85), exp_mean=20.8)
check_trace("cc2_cc_I", (85, 100), exp_mean=0.0)
check_trace("nrn_iIn", (00, 20), exp_mean=0.0)
check_trace("nrn_iIn", (20, 25), exp_mean=20.8)
check_trace("nrn_iIn", (25, 30), exp_mean=0.0)
check_trace("nrn_iIn", (30, 40), exp_mean=13.8)
check_trace("nrn_iIn", (40, 45), exp_mean=20.8)
check_trace("nrn_iIn", (45, 60), exp_mean=0.0)
check_trace("nrn_iIn", (60, 65), exp_mean=34.6)
check_trace("nrn_iIn", (65, 70), exp_mean=13.8)
check_trace("nrn_iIn", (70, 80), exp_mean=0.0)
check_trace("nrn_iIn", (80, 85), exp_mean=20.8)
check_trace("nrn_iIn", (85, 90), exp_mean=0.0)
check_trace("nrn_iIn", (90, 100), exp_mean=13.8)
check_trace("nrn_V", (00 + 2, 20), exp_mean=(0.0 / 4.3) - 70)
check_trace("nrn_V", (20 + 2, 25), exp_mean=(20.8 / 4.3) - 70)
check_trace("nrn_V", (25 + 2, 30), exp_mean=(0.0 / 4.3) - 70)
check_trace("nrn_V", (30 + 2, 40), exp_mean=(13.8 / 4.3) - 70)
check_trace("nrn_V", (40 + 2, 45), exp_mean=(20.8 / 4.3) - 70)
check_trace("nrn_V", (45 + 2, 60), exp_mean=(0.0 / 4.3) - 70)
check_trace("nrn_V", (60 + 2, 65), exp_mean=(34.6 / 4.3) - 70)
check_trace("nrn_V", (65 + 2, 70), exp_mean=(13.8 / 4.3) - 70)
check_trace("nrn_V", (70 + 2, 80), exp_mean=(0.0 / 4.3) - 70)
check_trace("nrn_V", (80 + 2, 85), exp_mean=(20.8 / 4.3) - 70)
check_trace("nrn_V", (85 + 2, 90), exp_mean=(0.0 / 4.3) - 70)
check_trace("nrn_V", (90 + 2, 100), exp_mean=(13.8 / 4.3) - 70)
iz_file = '../../../../../../catalog/sample_xml_files/PostTF_izhikevich.xml'
iz = nineml.al.parse(iz_file)
# Write the component back out to XML
nineml.al.writers.XMLWriter.write(iz, 'TestOut_Iz.xml')
nineml.al.writers.DotWriter.write(iz, 'TestOut_Iz.dot')
nineml.al.writers.DotWriter.build('TestOut_Iz.dot')
# Simulate the Neuron:
records = [
RecordValue(what='V', tag='V', label='V'),
# RecordValue(what='U', tag='U', label='U'),
# RecordValue( what='regime', tag='Regime', label='Regime' ),
]
parameters = nineml.al.flattening.ComponentFlattener.flatten_namespace_dict({
'a': 0.02,
'b': 0.2,
'c': -65,
'd': 8,
'iinj_constant': 50.0,
})
res = std_pynn_simulation(test_component=iz,
parameters=parameters,
initial_values={},
synapse_components=[],
records=records,
)
'cobaInhib.vrev': -70.0,
}
initial_values = {
'iaf_V': parameters['iaf.vrest'],
'tspike': -1e99,
'regime': 1002,
}
synapse_components = [
('cobaInhib', 'q'),
('cobaExcit', 'q'),
]
records = [
RecordValue(what='iaf_V', tag='Voltage [mV]', label='Membrane Voltage'),
RecordValue(what='cobaInhib_g',
tag='Conductance [ns]',
label='cobaInhib-g'),
RecordValue(what='cobaExcit_g',
tag='Conductance [ns]',
label='cobaExcit-g'),
RecordValue(what='regime', tag='Regime', label='Regime'),
]
std_pynn_simulation(test_component=test_component,
parameters=parameters,
initial_values=initial_values,
synapse_components=synapse_components,
records=records)
RecordValue(what='iaf_V', tag='V', label='V'),
RecordValue(what='regime', tag='Regime', label='Regime'),
]
parameters = flattening.ComponentFlattener.flatten_namespace_dict({
'cobaExcit_tau': 5.0,
'cobaExcit_vrev': 0,
'iaf_cm': 1,
'iaf_gl': 50,
'iaf_taurefrac': 8,
'iaf_vreset': -60,
'iaf_vrest': -60,
'iaf_vthresh': -40
})
initial_values = {
'iaf_V': parameters['iaf_vrest'],
'tspike': -1e99,
'regime': 1002,
}
res = std_pynn_simulation(test_component=coba1,
parameters=parameters,
initial_values=initial_values,
synapse_components=[('cobaExcit', 'q')],
synapse_weights=15.0,
records=records,
sim_time=250,
syn_input_rate=100
)
'cobaExcit_vrev':
0,
'iaf_cm':
1,
'iaf_gl':
50,
'iaf_taurefrac':
8,
'iaf_vreset':
-60,
'iaf_vrest':
-60,
'iaf_vthresh':
-40
})
initial_values = {
'iaf_V': parameters['iaf_vrest'],
'tspike': -1e99,
'regime': 1002,
}
res = std_pynn_simulation(test_component=coba1,
parameters=parameters,
initial_values=initial_values,
synapse_components=[('cobaExcit', 'q')],
synapse_weights=15.0,
records=records,
sim_time=250,
syn_input_rate=100)
