# siegert_neuron – model for mean-field analysis of spiking networks¶

## Description¶

`siegert_neuron`

is an implementation of a rate model with the
non-linearity given by the gain function of the
leaky-integrate-and-fire neuron with delta or exponentially decaying
synapses [2] and [3] (their eq. 25). The model can be used for a
mean-field analysis of spiking networks. A constant mean input can be
provided to create neurons with a target rate, e.g. to model a constant
external input.

The model supports connections to other rate models with zero delay, and uses the secondary_event concept introduced with the gap-junction framework.

For details on the numerical solution of the Siegert integral, you can check out the Siegert_neuron_integration notebook in the NEST source code.

## Parameters¶

The following parameters can be set in the status dictionary.

rate |
1/s |
Rate (1/s) |

tau |
ms |
Time constant |

mean |
1/s |
Additional constant input |

The following parameters can be set in the status directory and are used in the evaluation of the gain function. Parameters as in iaf_psc_exp/delta.

tau_m |
ms |
Membrane time constant |

tau_syn |
ms |
Time constant of postsynaptic currents |

t_ref |
ms |
Duration of refractory period |

theta |
mV |
Threshold relative to resting potential |

V_reset |
mV |
Reset relative to resting potential |

## References¶

## Sends¶

DiffusionConnectionEvent

## Receives¶

DiffusionConnectionEvent, DataLoggingRequest