# 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=[323203819 330710379]), y_initializer=Uniform(min_val=0, max_val=0.5, rng=[323203819 330710379]), 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 (Parameter) – The noise mean of the $$x$$ variable, [mV/ms]

• y_ou_mean (Parameter) – The noise mean of the $$y$$ variable, [mV/ms].

• x_ou_sigma (Parameter) – The noise intensity of the $$x$$ variable, [mV/ms/sqrt(ms)].

• y_ou_sigma (Parameter) – The noise intensity of the $$y$$ variable, [mV/ms/sqrt(ms)].

• x_ou_tau (Parameter) – The timescale of the Ornstein-Uhlenbeck noise process of $$x$$ variable, [ms].

• y_ou_tau (Parameter) – The timescale of the Ornstein-Uhlenbeck noise process of $$y$$ variable, [ms].

clear_input()[source]#

Empty function of clearing inputs.

reset_state(batch_or_mode=None, **kwargs)[source]#

Reset function which resets local states in this model.

Simply speaking, this function should implement the logic of resetting of local variables in this node.

update(inp_x=None, inp_y=None)[source]#

The function to specify the updating rule.