brainpy.dyn.rates.WilsonCowanModel#

class brainpy.dyn.rates.WilsonCowanModel(size, 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, seed=None), y_initializer=Uniform(min_val=0.0, max_val=0.05, seed=None), sde_method=None, keep_size=False, method='exp_euler_auto', name=None)[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].

__init__(size, 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, seed=None), y_initializer=Uniform(min_val=0.0, max_val=0.05, seed=None), sde_method=None, keep_size=False, method='exp_euler_auto', name=None)[source]#

Methods

F(x, a, theta)

__init__(size[, E_tau, E_a, E_theta, I_tau, ...])

dx(x, t, y, x_ext)

dy(y, t, x, y_ext)

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