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.

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