brainpy.dyn.others.OUProcess#

class brainpy.dyn.others.OUProcess(size, mean=0.0, sigma=1.0, tau=10.0, method='euler', name=None)[source]#

The Ornstein–Uhlenbeck process.

The Ornstein–Uhlenbeck process \(x_{t}\) is defined by the following stochastic differential equation:

\[\tau dx_{t}=-\theta \,x_{t}\,dt+\sigma \,dW_{t}\]

where \(\theta >0\) and \(\sigma >0\) are parameters and \(W_{t}\) denotes the Wiener process.

Parameters

size (int, sequence of int) – The model size.
mean (Parameter) – The noise mean value.
sigma (Parameter) – The noise amplitude.
tau (Parameter) – The decay time constant.
method (str) – The numerical integration method for stochastic differential equation.
name (str) – The model name.

__init__(size, mean=0.0, sigma=1.0, tau=10.0, method='euler', name=None)[source]#

Methods

`__init__`(size[, mean, sigma, tau, method, name])
`df`(x, t)
`dg`(x, t)
`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