def parameterise_leaky_integrate_and_fire(definition=None):
if definition is None:
definition = create_leaky_integrate_and_fire()
comp = ul.DynamicsProperties(
name='SampleLeakyIntegrateAndFire',
definition=create_leaky_integrate_and_fire(),
properties=[ul.Property('tau', 20.0 * un.ms),
ul.Property('v_threshold', 20.0 * un.mV),
ul.Property('refractory_period', 2.0 * un.ms),
ul.Property('v_reset', 10.0 * un.mV),
ul.Property('R', 1.5 * un.Mohm)],
initial_values=[ul.Initial('V', -70 * un.mV)])
return comp
def parameterise_static():
comp = ul.DynamicsProperties(
name='SampleAlpha',
definition=create_static(),
properties=[ul.Property('weight', 10.0 * un.nA)])
return comp
def build_parameter_set(parameters, shape=None, dimensionless=False):
parameter_list = []
for name, value in parameters.items():
if isinstance(value, larray):
value.shape = shape
if value.is_homogeneous:
value = value.evaluate(simplify=True)
if isinstance(value, Sequence):
value = value.value.tolist()
elif isinstance(value, bool):
value = int(value)
elif isinstance(value, random.RandomDistribution):
rand_distr = value
value = nineml.RandomDistributionComponent(
name="%s(%s)" % (rand_distr.name, ",".join(str(p) for p in rand_distr.parameters)),
definition=nineml.Definition(random_distribution_url_map[rand_distr.name],
"random"),
parameters=build_parameter_set(map_random_distribution_parameters(rand_distr.name, rand_distr.parameters),
dimensionless=True))
else:
raise Exception("not supported")
else:
if isinstance(value, bool):
value = int(value)
if dimensionless:
unit = "dimensionless"
elif isinstance(value, basestring):
unit = None
else:
unit = infer_units(name)
parameter_list.append(nineml.Property(name, value, unit))
return nineml.PropertySet(*parameter_list)
def parameterise_alpha():
comp = ul.DynamicsProperties(name='SampleAlpha',
definition=create_alpha(),
properties=[ul.Property('tau', 20.0 * un.ms)],
initial_values=[
ul.Initial('a', 0.0 * un.pA),
ul.Initial('b', 0.0 * un.pA)
])
return comp
def parameterise_izhikevich_fast_spiking(definition=None):
if definition is None:
definition = create_izhikevich_fast_spiking()
comp = ul.DynamicsProperties(name='SampleIzhikevichFastSpiking',
definition=create_izhikevich_fast_spiking(),
properties=[
ul.Property('a', 0.2 * un.per_ms),
ul.Property('b',
0.025 * un.nS / un.mV**2),
ul.Property('c', -45 * un.mV),
ul.Property('k', 1 * un.nS / un.mV),
ul.Property('Vpeak', 25 * un.mV),
ul.Property('Vb', -55 * un.mV),
ul.Property('Cm', 20 * un.pF),
ul.Property('Vr', -55 * un.mV),
ul.Property('Vt', -40 * un.mV)
],
initial_values=[
ul.Initial('V', -70 * un.mV),
ul.Initial('U', -1.625 * un.mV / un.ms)
])
return comp
def parameterise_izhikevich(definition=None):
if definition is None:
definition = create_izhikevich()
comp = ul.DynamicsProperties(
name='SampleIzhikevich',
definition=create_izhikevich(),
properties=[
ul.Property('a', 0.2 * un.per_ms),
ul.Property('b', 0.025 * un.per_ms),
ul.Property('c', -75 * un.mV),
ul.Property('d', 0.2 * un.mV / un.ms),
ul.Property('theta', -50 * un.mV),
ul.Property('alpha', 0.04 * un.unitless / (un.mV * un.ms)),
ul.Property('beta', 5 * un.per_ms),
ul.Property('zeta', 140.0 * un.mV / un.ms),
ul.Property('C_m', 1.0 * un.pF)
],
initial_values=[
ul.Initial('V', -70 * un.mV),
ul.Initial('U', -1.625 * un.mV / un.ms)
])
return comp
def parameterise_hodgkin_huxley(definition=None):
if definition is None:
definition = create_hodgkin_huxley()
comp = ul.DynamicsProperties(
name='SampleHodgkinHuxley',
definition=create_hodgkin_huxley(),
properties=[
ul.Property('C', 1.0 * un.pF),
ul.Property('celsius', 20.0 * un.degC),
ul.Property('ek', -90 * un.mV),
ul.Property('el', -65 * un.mV),
ul.Property('ena', 80 * un.mV),
ul.Property('gkbar', 30.0 * un.nS),
ul.Property('gl', 0.3 * un.nS),
ul.Property('gnabar', 130.0 * un.nS),
ul.Property('v_threshold', -40.0 * un.mV),
ul.Property('qfactor', 6.3 * un.degC),
ul.Property('tendegrees', 10.0 * un.degC),
ul.Property('m_alpha_A', -0.1, old_div(un.unitless,
(un.ms * un.mV))),
ul.Property('m_alpha_V0', -40.0 * un.mV),
ul.Property('m_alpha_K', 10.0 * un.mV),
ul.Property('m_beta_A', 4.0 * un.per_ms),
ul.Property('m_beta_V0', -65.0 * un.mV),
ul.Property('m_beta_K', 18.0 * un.mV),
ul.Property('h_alpha_A', 0.07 * un.per_ms),
ul.Property('h_alpha_V0', -65.0 * un.mV),
ul.Property('h_alpha_K', 20.0 * un.mV),
ul.Property('h_beta_A', 1.0 * un.per_ms),
ul.Property('h_beta_V0', -35.0 * un.mV),
ul.Property('h_beta_K', 10.0 * un.mV),
ul.Property('n_alpha_A', -0.01,
old_div(un.unitless, (un.ms * un.mV))),
ul.Property('n_alpha_V0', -55.0 * un.mV),
ul.Property('n_alpha_K', 10.0 * un.mV),
ul.Property('n_beta_A', 0.125 * un.per_ms),
ul.Property('n_beta_V0', -65.0 * un.mV),
ul.Property('n_beta_K', 80.0 * un.mV)
],
initial_values=[
ul.Initial('V', -70 * un.mV),
ul.Initial('m', 0.1),
ul.Initial('n', 0),
ul.Initial('h', 0.9)
])
return comp
def parameterise_adaptive_exponential(definition=None):
if definition is None:
definition = create_adaptive_exponential()
comp = ul.DynamicsProperties(name='SampleAdaptiveExpIntegrateAndFire',
definition=definition,
properties=[
ul.Property('C_m', 1 * un.pF),
ul.Property('g_L', 0.1 * un.nS),
ul.Property('E_L', -65 * un.mV),
ul.Property('Delta', 1 * un.mV),
ul.Property('V_T', -58 * un.mV),
ul.Property('S', 0.1),
ul.Property('tspike', 0.5 * un.ms),
ul.Property('trefractory', 0.25 * un.ms),
ul.Property('tau_w', 4 * un.ms),
ul.Property('a', 1 * un.per_mV),
ul.Property('b', 2)
],
initial_values=[
ul.Initial('V', -70 * un.mV),
ul.Initial('w', 0.1 * un.mV)
])
return comp