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=RandomState(Array((), dtype=key<fry>) overlaying: [ 216744582 1008666480])), y_initializer=Uniform(min_val=0.0, max_val=0.05, rng=RandomState(Array((), dtype=key<fry>) overlaying: [ 216744582 1008666480])), method='exp_euler_auto', name=None, mode=None, input_var=True)[source]#
Wilson-Cowan population model.
- 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].