StuartLandauOscillator#
- class brainpy.dyn.StuartLandauOscillator(size, keep_size=False, a=0.25, w=0.2, 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=0.5, rng=RandomState(Array((), dtype=key<fry>) overlaying: [ 216744582 1008666480])), y_initializer=Uniform(min_val=0, max_val=0.5, rng=RandomState(Array((), dtype=key<fry>) overlaying: [ 216744582 1008666480])), method='exp_auto', name=None, mode=None, input_var=True)[source]#
Stuart-Landau model with Hopf bifurcation.
\[\begin{split}\frac{dx}{dt} = (a - x^2 - y^2) * x - w*y + I^x_{ext} \\ \frac{dy}{dt} = (a - x^2 - y^2) * y + w*x + I^y_{ext}\end{split}\]- Parameters:
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].