OUProcess#
- class brainmass.OUProcess(in_size, mean=None, sigma=Quantity(1., 'nA'), tau=Quantity(10., 'ms'), init=None)#
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:
in_size (
int|Sequence[int] |integer|Sequence[integer]) – The model size.mean (
Callable|Array|ndarray|bool|number|bool|int|float|complex|Quantity|Param) – The noise mean value. Default is 0 nA.sigma (
Callable|Array|ndarray|bool|number|bool|int|float|complex|Quantity|Param) – The noise amplitude. Defualt is 1 nA.tau (
Callable|Array|ndarray|bool|number|bool|int|float|complex|Quantity|Param) – The decay time constant. The larger the value, the slower the decay. Default is 10 ms.
- Return type:
Any