brainpy.dyn.rates.StuartLandauOscillator#

class brainpy.dyn.rates.StuartLandauOscillator(size, 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, seed=None), y_initializer=Uniform(min_val=0, max_val=0.5, seed=None), method='exp_auto', keep_size=False, sde_method=None, name=None)[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].

__init__(size, 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, seed=None), y_initializer=Uniform(min_val=0, max_val=0.5, seed=None), method='exp_auto', keep_size=False, sde_method=None, name=None)[source]#

Methods

 __init__(size[, a, w, x_ou_mean, ...]) dx(x, t, y, x_ext, a, w) dy(y, t, x, y_ext, a, w) get_delay_data(name, delay_step, *indices) Get delay data according to the provided delay steps. ints([method]) Collect all integrators in this node and the children nodes. load_states(filename[, verbose]) Load the model states. nodes([method, level, include_self]) Collect all children nodes. register_delay(name, delay_step, delay_target) Register delay variable. register_implicit_nodes(nodes) register_implicit_vars(variables) reset() Reset function which reset the whole variables in the model. save_states(filename[, variables]) Save the model states. train_vars([method, level, include_self]) The shortcut for retrieving all trainable variables. unique_name([name, type_]) Get the unique name for this object. update(t, dt) The function to specify the updating rule. vars([method, level, include_self]) Collect all variables in this node and the children nodes.

Attributes

 global_delay_targets global_delay_vars name steps