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# Probability Distributions

A certain number of example probability distributions are provided. Some are *non-stationary*, i.e. they change over time (time is not measured in seconds but in number of input signals presented).
## Stationary Distributions

Most of the distributions are *stationary* i.e. they do not change over time. Examples are "Circle", "Corners" or "Cactus".
## Non-Stationary Distributions

The non-stationary distributions are indicated by a name ending with "-N" like "Circle-N", "Corners-N" or "PulsingCircle-N".
Only models with constant parameters, e,g, Growing Neural Gas, Growing Grid, or Hard Competitive Learning are able to handle non-stationary parameters. Models with decaying parameters, e.g. Self-Organizing Map or Neural Gas on purpose loose there adaptivity after a finite number of steps. Therefore the "Next PD" or "Prev PD" Buttons select only stationary distributions for these models.

One distribution - "DragMe" - allows the non-stationarity to be user-controlled using the mouse.
## Rotation

Each stationary distribution (unless it is rotation-symmetric) can be turned into a non-stationary one by activating rotation