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=[2459466628 2755113083]), y_initializer=Uniform(min_val=0, max_val=0.05, rng=[2459466628 2755113083]), 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 (Shape) – The model size.

  • 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].

References

clear_input()[source]#

Empty function of clearing inputs.

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

The function to specify the updating rule.