WilsonCowanModel#

class brainpy.dyn.WilsonCowanModel(size, keep_size=False, E_tau=1.0, E_a=1.2, E_theta=2.8, I_tau=1.0, I_a=1.0, I_theta=4.0, wEE=12.0, wIE=4.0, wEI=13.0, wII=11.0, r=1.0, 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=0.0, max_val=0.05, rng=[2459466628 2755113083]), y_initializer=Uniform(min_val=0.0, max_val=0.05, rng=[2459466628 2755113083]), method='exp_euler_auto', name=None, mode=None, input_var=True)[source]#

Wilson-Cowan population model.

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.

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

The function to specify the updating rule.