# gauss_rate – Rate neuron model with Gaussian gain function¶

## Description¶

`gauss_rate`

is an implementation of a nonlinear rate model with input

It either models a rate neuron with input noise (see `rate_neuron_ipn`

)
or a rate transformer (see `rate_transformer_node`

).
Input transformation can either be applied to individual inputs
or to the sum of all inputs.

The model supports connections to other rate models with either zero or
non-zero delay, and uses the `secondary_event`

concept introduced with
the gap-junction framework.

Nonlinear rate neurons can be created by typing
`nest.Create("gauss_rate_ipn")`

. Nonlinear rate transformers can be
created by typing `nest.Create("rate_transformer_gauss")`

.

## Parameters¶

The following parameters can be set in the status dictionary. Note that some of the parameters only apply to rate neurons and not to rate transformers.

rate |
real |
Rate (unitless) |

tau |
ms |
Time constant of rate dynamics |

mu |
real |
Mean input |

sigma |
real |
Noise parameter |

g |
real |
Gain parameter |

mu |
real |
Mean of the Gaussian gain function |

sigma |
real |
Standard deviation of Gaussian gain function |

rectify_rate |
real |
Rectifying rate |

linear_summation |
boolean |
Specifies type of non-linearity (see above) |

rectify_output |
boolean |
Switch to restrict rate to values >= rectify_rate |

Note:

The boolean parameter `linear_summation`

determines whether the
input from different presynaptic neurons is first summed linearly and
then transformed by a nonlinearity (true), or if the input from
individual presynaptic neurons is first nonlinearly transformed and
then summed up (false). Default is true.

## References¶

## Sends¶

InstantaneousRateConnectionEvent, DelayedRateConnectionEvent

## Receives¶

InstantaneousRateConnectionEvent, DelayedRateConnectionEvent, DataLoggingRequest