# Command: siegert_neuron

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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.

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

Parameters:

      The following parameters can be set in the status dictionary.

      rate	 double	- Rate (1/s)
      tau	 double	- Time constant in ms.
      mean	 double	- 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	 double	- Membrane time constant in ms.
      tau_syn	 double	- Time constant of postsynaptic currents in ms.
      t_ref	 double	- Duration of refractory period in ms.
      theta	 double	- Threshold relative to resting potential in mV.
      V_reset	 double	- Reset relative to resting membrane potential in
      mV.

      Notes:

Require:

    HAVE_GSL

Receives:

    DiffusionConnectionEvent, DataLoggingRequest

Sends:

    DiffusionConnectionEvent

References:

      [1] Hahne, J., Dahmen, D., Schuecker, J., Frommer, A.,
      Bolten, M., Helias, M. and Diesmann, M. (2017).
      Integration of Continuous-Time Dynamics in a
      Spiking Neural Network Simulator.
      Front. Neuroinform. 11:34. doi: 10.3389/fninf.2017.00034

      [2] Fourcaud, N and Brunel, N. (2002). Dynamics of the firing
      probability of noisy integrate-and-fire neurons, Neural computation,
      14:9, pp 2057--2110

      [3] Schuecker, J., Diesmann, M. and Helias, M. (2015).
      Modulated escape from a metastable state driven by colored noise.
      Physical Review E 92:052119

      [4] Hahne, J., Helias, M., Kunkel, S., Igarashi, J.,
      Bolten, M., Frommer, A. and Diesmann, M. (2015).
      A unified framework for spiking and gap-junction interactions
      in distributed neuronal network simulations.
      Front. Neuroinform. 9:22. doi: 10.3389/fninf.2015.00022

Author:

    Jannis Schuecker, David Dahmen, Jan Hahne

SeeAlso:

- diffusion_connection

Source:

    /opt/conda/conda-bld/nest_1512418773968/work/nest-2.14.0/models/siegert_neuron.h

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