def __init__(self, name, compartment,
g_ca3kahp = None,
E_ca3kahp = None):
super(Ca3KahpChannel, self).__init__(name, compartment)
# Kahp requires Calcium compartment
from compartment import CalciumCompartment
# TODO: Or observed calcium?
assert isinstance(compartment, CalciumCompartment)
self._latent_dtype = [('q', np.float64)]
self._state_dtype = [('I', np.float64)]
self._input_dtype = None
self._latent_lb = np.array([0])
self._latent_ub = np.array([1])
self._calcium_dependent = True
self._set_defaults(g_ca3kahp, Parameter('g_ca3kahp', distribution=
GammaDistribution(
hypers['a_g_ca3kahp'].value,
hypers['b_g_ca3kahp'].value
),
lb=0.0),
E_ca3kahp, hypers['E_Kahp'])
def __init__(self, name, compartment,
g_ca3ka = None,
E_ca3ka = None):
super(Ca3KaChannel, self).__init__(name, compartment)
self._latent_dtype = [('a', np.float64), ('b', np.float64)]
self._state_dtype = [('I', np.float64)]
self._input_dtype = None
self._latent_lb = np.array([0, 0])
self._latent_ub = np.array([1, 1])
self._set_defaults(g_ca3ka, Parameter('g_ca3ka', distribution=
GammaDistribution(
hypers['a_g_ca3ka'].value,
hypers['b_g_ca3ka'].value
),
lb=0.0),
E_ca3ka, hypers['E_K'])
def __init__(self, name, compartment,
g_ca3kc = None,
E_ca3kc = None):
super(Ca3KcChannel, self).__init__(name, compartment)
self._latent_dtype = [('c', np.float64)]
self._state_dtype = [('I', np.float64)]
self._input_dtype = None
self._latent_lb = np.array([0])
self._latent_ub = np.array([1])
self._calcium_dependent = True
self._set_defaults(g_ca3kc, Parameter('g_ca3kc', distribution=
GammaDistribution(
hypers['a_g_ca3kc'].value,
hypers['b_g_ca3kc'].value
),
lb=0.0),
E_ca3kc, hypers['E_Ca3Kc'])
def __init__(self, name, compartment,
g_chr2 = None,
E_chr2 = None):
super(ChR2Channel, self).__init__(name, compartment)
self._latent_dtype = [('O1', np.float64),
('O2', np.float64),
('C1', np.float64),
('C2', np.float64),
('p', np.float64)]
self._state_dtype = [('I', np.float64)]
# self._input_dtype = []
self._latent_lb = np.array([0, 0, 0, 0, 0])
self._latent_ub = np.array([1, 1, 1, 1, 1])
self._set_defaults(g_chr2, Parameter('g_chr2', distribution=
GammaDistribution(
hypers['a_g_chr2'].value,
hypers['b_g_chr2'].value
),
lb=0.0),
E_chr2, hypers['E_ChR2'])
def __init__(self, name, compartment,
g_kdr=None, E_kdr=None):
super(KdrChannel, self).__init__(name, compartment)
self.latent_dtype = [('n', np.float64)]
self.state_dtype = [('I', np.float64)]
self.latent_lb = np.array([0])
self.latent_ub = np.array([1])
# By default, g is gamma distributed
if g_kdr is None:
self.g = Parameter('g_kdr',
distribution=GammaDistribution(hypers['a_g_kdr'].value,
hypers['b_g_kdr'].value),
lb=0.0)
else:
self.g = g_kdr
# By default, E is a hyperparameter
if E_kdr is None:
self.E = hypers['E_K']
else:
self.E = E_kdr
def __init__(self, name, compartment,
g_na=None, E_na=None):
super(NaChannel, self).__init__(name, compartment)
self.latent_dtype = [('m', np.float64), ('h', np.float64)]
self.latent_lb = np.array([0,0])
self.latent_ub = np.array([1,1])
self.state_dtype = [('I', np.float64)]
# By default, g is gamma distributed
if g_na is None:
self.g = Parameter('g_na',
distribution=GammaDistribution(hypers['a_g_na'].value,
hypers['b_g_na'].value),
lb=0.0)
else:
self.g = g_na
# By default, E is a hyperparameter
if E_na is None:
self.E = hypers['E_Na']
else:
self.E = E_na
def __init__(self, name, compartment,
g_leak=None, E_leak=None):
super(LeakChannel, self).__init__(name, compartment)
self.state_dtype = [('I', np.float64)]
# By default, g is gamma distributed
if g_leak is None:
self.g = Parameter('g_leak',
distribution=GammaDistribution(hypers['a_g_leak'].value,
hypers['b_g_leak'].value),
lb=0.0)
else:
assert isinstance(g_leak, Parameter)
self.g = g_leak
# By default, E is a hyperparameter
if E_leak is None:
self.E = hypers['E_leak']
else:
assert isinstance(E_leak, Parameter)
self.E = E_leak