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