FHN#
- class brainpy.dyn.FHN(size, keep_size=False, alpha=3.0, beta=4.0, gamma=-1.5, delta=0.0, epsilon=0.5, tau=20.0, x_ou_mean=0.0, x_ou_sigma=0.0, x_ou_tau=5.0, y_ou_mean=0.0, y_ou_sigma=0.0, y_ou_tau=5.0, x_initializer=Uniform(min_val=0, max_val=0.05, rng=RandomState(Array((), dtype=key<fry>) overlaying: [2160587856 1292162787])), y_initializer=Uniform(min_val=0, max_val=0.05, rng=RandomState(Array((), dtype=key<fry>) overlaying: [2160587856 1292162787])), method='exp_auto', name=None, mode=None, input_var=True)[source]#
FitzHugh-Nagumo system used in [1].
\[\begin{split}\frac{dx}{dt} = -\alpha V^3 + \beta V^2 + \gamma V - w + I_{ext}\\ \tau \frac{dy}{dt} = (V - \delta - \epsilon w)\end{split}\]- Parameters:
size (
TypeVar(Shape,int,Tuple[int,...])) – The model size.x_ou_mean (
Union[float,TypeVar(ArrayType,Array,Variable,TrainVar,Array,ndarray),Initializer,Callable]) – The noise mean of the \(x\) variable, [mV/ms]y_ou_mean (
Union[float,TypeVar(ArrayType,Array,Variable,TrainVar,Array,ndarray),Initializer,Callable]) – The noise mean of the \(y\) variable, [mV/ms].x_ou_sigma (
Union[float,TypeVar(ArrayType,Array,Variable,TrainVar,Array,ndarray),Initializer,Callable]) – The noise intensity of the \(x\) variable, [mV/ms/sqrt(ms)].y_ou_sigma (
Union[float,TypeVar(ArrayType,Array,Variable,TrainVar,Array,ndarray),Initializer,Callable]) – The noise intensity of the \(y\) variable, [mV/ms/sqrt(ms)].x_ou_tau (
Union[float,TypeVar(ArrayType,Array,Variable,TrainVar,Array,ndarray),Initializer,Callable]) – The timescale of the Ornstein-Uhlenbeck noise process of \(x\) variable, [ms].y_ou_tau (
Union[float,TypeVar(ArrayType,Array,Variable,TrainVar,Array,ndarray),Initializer,Callable]) – The timescale of the Ornstein-Uhlenbeck noise process of \(y\) variable, [ms].
References