brainpy.dyn.rates.WilsonCowanModel
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.
reset_delay
(name, delay_target)Reset the delay variable.
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.
update_delay
(name, delay_data)Update the delay according to the delay data.
vars
([method, level, include_self])Collect all variables in this node and the children nodes.
Attributes
global_delay_vars
name
steps