def test_Izh():
"""Basic Izhikevich hybrid model"""
cc = ninemlcatalog.load('/neuron/Izhikevich', 'Izhikevich')
comp = ninemlcatalog.load('/neuron/Izhikevich', 'SampleIzhikevich')
# Convert to PyDSTool.ModelSpec and create HybridModel object
# Provide extra parameter Isyn which is missing from component definition
# in absence of any synaptic inputs coupled to the model membrane
izh = get_nineml_model(cc, 'izh_9ML', extra_args=[Par('Isyn')], max_t=100)
pars = dict((p.name, p.value) for p in comp.properties)
pars['Isyn'] = 20.0
ics = dict((i.name, i.value) for i in comp.initial_values)
ics['regime_'] = 0
izh.set(pars=pars, ics=ics, tdata=[0, 80], algparams={'init_step': 0.04})
izh.compute('test', verboselevel=0)
pts = izh.sample('test')
evs = izh.getTrajEventTimes('test')['spike']
theta = izh.query('pars')['theta']
plt.figure(4)
plt.plot(pts['t'], pts['V'], 'k')
plt.plot(evs, [theta] * len(evs), 'ko')
plt.title('Izhikevich model')
plt.xlabel('t')
plt.ylabel('V')
plt.figure(5)
plt.plot(pts['t'], pts['U'])
plt.xlabel('t')
plt.ylabel('U')
plt.title('Izhikevich model')
def test_HH():
cc = ninemlcatalog.load('/neuron/HodgkinHuxley', 'HodgkinHuxley')
comp = ninemlcatalog.load('neuron/HodgkinHuxley', 'SampleHodgkinHuxley')
# Convert to PyDSTool.ModelSpec and create NonHybridModel object
# Provide extra parameter Isyn which is missing from component definition
# in absence of any synaptic inputs coupled to the model membrane
HHmodel = get_nineml_model(cc, 'HH_9ML', extra_args=[Par('isyn')])
pars = dict((p.name, p.value) for p in comp.properties)
pars['isyn'] = 20.0
ics = dict((i.name, i.value) for i in comp.initial_values)
HHmodel.set(pars=pars, ics=ics, tdata=[0, 15])
HHmodel.compute('HHtest', force=True)
pts = HHmodel.sample('HHtest')
plt.figure(1)
plt.plot(pts['t'], pts['V'], 'k')
plt.title('Hodgkin-Huxley membrane potential')
ev_info = pts.labels.by_label['Event:spikeoutput']
for ev_ix, ev_tdata in list(ev_info.items()):
plt.plot(ev_tdata['t'], pts[ev_ix]['V'], 'ko')
plt.xlabel('t')
plt.ylabel('V')
def test_aeIF():
"""Adaptive Integrate and Fire"""
cc = ninemlcatalog.load('/neuron/AdaptiveExpIntegrateAndFire',
'AdaptiveExpIntegrateAndFire')
comp = ninemlcatalog.load('/neuron/AdaptiveExpIntegrateAndFire',
'SampleAdaptiveExpIntegrateAndFire')
# Convert to PyDSTool.ModelSpec and create HybridModel object
# Provide extra parameter Isyn which is missing from component definition
# in absence of any synaptic inputs coupled to the model membrane
aeIF = get_nineml_model(cc,
'aeIF_9ML',
extra_args=[Par('Isyn')],
max_t=100)
pars = dict((p.name, p.value) for p in comp.properties)
pars['Isyn'] = 5.0
ics = dict((i.name, i.value) for i in comp.initial_values)
ics['regime_'] = 0
aeIF.set(pars=pars, ics=ics, tdata=[0, 30], algparams={'init_step': 0.04})
aeIF.compute('test', verboselevel=0)
pts = aeIF.sample('test', dt=0.1)
plt.figure(2)
plt.plot(pts['t'], pts['V'])
plt.xlabel('t')
plt.ylabel('V')
plt.title('adaptive IF model')
plt.figure(3)
plt.plot(pts['t'], pts['w'])
plt.xlabel('t')
plt.ylabel('w')
plt.title('adaptive IF model')
def test_aeIF():
"""Adaptive Integrate and Fire"""
cc = ninemlcatalog.load('/neuron/AdaptiveExpIntegrateAndFire',
'AdaptiveExpIntegrateAndFire')
comp = ninemlcatalog.load('/neuron/AdaptiveExpIntegrateAndFire',
'SampleAdaptiveExpIntegrateAndFire')
# Convert to PyDSTool.ModelSpec and create HybridModel object
# Provide extra parameter Isyn which is missing from component definition
# in absence of any synaptic inputs coupled to the model membrane
aeIF = get_nineml_model(cc, 'aeIF_9ML', extra_args=[Par('Isyn')],
max_t=100)
pars = dict((p.name, p.value) for p in comp.properties)
pars['Isyn'] = 5.0
ics = dict((i.name, i.value) for i in comp.initial_values)
ics['regime_'] = 0
aeIF.set(pars=pars, ics=ics, tdata=[0, 30], algparams={'init_step': 0.04})
aeIF.compute('test', verboselevel=0)
pts = aeIF.sample('test', dt=0.1)
plt.figure(2)
plt.plot(pts['t'], pts['V'])
plt.xlabel('t')
plt.ylabel('V')
plt.title('adaptive IF model')
plt.figure(3)
plt.plot(pts['t'], pts['w'])
plt.xlabel('t')
plt.ylabel('w')
plt.title('adaptive IF model')
def test_HH():
cc = ninemlcatalog.load('/neuron/HodgkinHuxley', 'HodgkinHuxley')
comp = ninemlcatalog.load('neuron/HodgkinHuxley', 'SampleHodgkinHuxley')
# Convert to PyDSTool.ModelSpec and create NonHybridModel object
# Provide extra parameter Isyn which is missing from component definition
# in absence of any synaptic inputs coupled to the model membrane
HHmodel = get_nineml_model(cc, 'HH_9ML', extra_args=[Par('isyn')])
pars = dict((p.name, p.value) for p in comp.properties)
pars['isyn'] = 20.0
ics = dict((i.name, i.value) for i in comp.initial_values)
HHmodel.set(pars=pars, ics=ics, tdata=[0, 15])
HHmodel.compute('HHtest', force=True)
pts = HHmodel.sample('HHtest')
plt.figure(1)
plt.plot(pts['t'], pts['V'], 'k')
plt.title('Hodgkin-Huxley membrane potential')
ev_info = pts.labels.by_label['Event:spikeoutput']
for ev_ix, ev_tdata in list(ev_info.items()):
plt.plot(ev_tdata['t'], pts[ev_ix]['V'], 'ko')
plt.xlabel('t')
plt.ylabel('V')
def test_Izh_FS(iSyns=None):
"""Izhikevich Fast Spiker model"""
if iSyns is None:
iSyns = [200]
cc = ninemlcatalog.load('/neuron/Izhikevich', 'IzhikevichFastSpiking')
comp = ninemlcatalog.load('/neuron/Izhikevich',
'SampleIzhikevichFastSpiking')
# Convert to PyDSTool.ModelSpec and create HybridModel object
# Provide extra parameter Isyn which is missing from component definition
# in absence of any synaptic inputs coupled to the model membrane
izh = get_nineml_model(cc,
'izh_FS_9ML',
extra_args=[Par('iSyn')],
max_t=100)
pars = dict((p.name, p.value) for p in comp.properties)
ics = dict((i.name, i.value) for i in comp.initial_values)
ics['regime_'] = 0
# set starting regime to be sub-threshold (PyDSTool will check consistency
# with V initial condition)
izh.set(pars=pars, ics=ics, tdata=[0, 80], algparams={'init_step': 0.03})
for iSyn in iSyns:
izh.set(pars={'iSyn': iSyn})
name = 'iSyn=%.1f' % (float(iSyn))
izh.compute(name, verboselevel=0)
pts = izh.sample(name)
evs = izh.getTrajEventTimes(name)['spikeOutput']
ISIs = np.diff(evs)
print("iSyn ={}:\n Mean ISI = {}, variance = {}".format(
iSyn, np.mean(ISIs), np.var(ISIs)))
Vp = izh.query('pars')['Vpeak']
plt.figure(6)
plt.plot(pts['t'], pts['V'], label=name)
plt.plot(evs, [Vp] * len(evs), 'ko')
plt.title('Izhikevich fast spiking model')
plt.xlabel('t')
plt.ylabel('V')
plt.legend()
plt.figure(7)
plt.plot(pts['t'], pts['U'], label=name)
plt.xlabel('t')
plt.ylabel('U')
plt.legend()
plt.title('Izhikevich FS model')
def test_Izh_FS(iSyns=None):
"""Izhikevich Fast Spiker model"""
if iSyns is None:
iSyns = [200]
cc = ninemlcatalog.load('/neuron/Izhikevich', 'IzhikevichFastSpiking')
comp = ninemlcatalog.load('/neuron/Izhikevich',
'SampleIzhikevichFastSpiking')
# Convert to PyDSTool.ModelSpec and create HybridModel object
# Provide extra parameter Isyn which is missing from component definition
# in absence of any synaptic inputs coupled to the model membrane
izh = get_nineml_model(cc, 'izh_FS_9ML', extra_args=[Par('iSyn')],
max_t=100)
pars = dict((p.name, p.value) for p in comp.properties)
ics = dict((i.name, i.value) for i in comp.initial_values)
ics['regime_'] = 0
# set starting regime to be sub-threshold (PyDSTool will check consistency
# with V initial condition)
izh.set(pars=pars, ics=ics, tdata=[0, 80], algparams={'init_step': 0.03})
for iSyn in iSyns:
izh.set(pars={'iSyn': iSyn})
name = 'iSyn=%.1f' % (float(iSyn))
izh.compute(name, verboselevel=0)
pts = izh.sample(name)
evs = izh.getTrajEventTimes(name)['spikeOutput']
ISIs = np.diff(evs)
print("iSyn ={}:\n Mean ISI = {}, variance = {}"
.format(iSyn, np.mean(ISIs), np.var(ISIs)))
Vp = izh.query('pars')['Vpeak']
plt.figure(6)
plt.plot(pts['t'], pts['V'], label=name)
plt.plot(evs, [Vp] * len(evs), 'ko')
plt.title('Izhikevich fast spiking model')
plt.xlabel('t')
plt.ylabel('V')
plt.legend()
plt.figure(7)
plt.plot(pts['t'], pts['U'], label=name)
plt.xlabel('t')
plt.ylabel('U')
plt.legend()
plt.title('Izhikevich FS model')
def test_Izh():
"""Basic Izhikevich hybrid model"""
cc = ninemlcatalog.load('/neuron/Izhikevich', 'Izhikevich')
comp = ninemlcatalog.load('/neuron/Izhikevich', 'SampleIzhikevich')
# Convert to PyDSTool.ModelSpec and create HybridModel object
# Provide extra parameter Isyn which is missing from component definition
# in absence of any synaptic inputs coupled to the model membrane
izh = get_nineml_model(cc, 'izh_9ML', extra_args=[Par('Isyn')],
max_t=100)
pars = dict((p.name, p.value) for p in comp.properties)
pars['Isyn'] = 20.0
ics = dict((i.name, i.value) for i in comp.initial_values)
ics['regime_'] = 0
izh.set(pars=pars, ics=ics, tdata=[0, 80], algparams={'init_step': 0.04})
izh.compute('test', verboselevel=0)
pts = izh.sample('test')
evs = izh.getTrajEventTimes('test')['spike']
theta = izh.query('pars')['theta']
plt.figure(4)
plt.plot(pts['t'], pts['V'], 'k')
plt.plot(evs, [theta] * len(evs), 'ko')
plt.title('Izhikevich model')
plt.xlabel('t')
plt.ylabel('V')
plt.figure(5)
plt.plot(pts['t'], pts['U'])
plt.xlabel('t')
plt.ylabel('U')
plt.title('Izhikevich model')
