# Command: tanh_rate

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Name:

    tanh_rate - rate model with hyperbolic tangent non-linearity

Description:

      tanh_rate is an implementation of a non-linear rate model with either
      input (tanh_rate_ipn) or output noise (tanh_rate_opn) and gain function
      Phi(h) = tanh(g * (h-theta)) and Psi(h) = h for linear_summation = True
      Phi(h) = h and Psi(h) = tanh(g * (h-theta)) for linear_summation = False.

      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.

Parameters:

      The following parameters can be set in the status dictionary.

      rate	 double	- Rate (unitless)
      tau	 double	- Time constant of rate dynamics in ms.
      mean	 double	- Mean of Gaussian white noise.
      std	 double	- Standard deviation of Gaussian white noise.
      g	 double	- Gain parameter
      theta	 double	- Inflection point
      linear_summation   boolean	- specifies type of non-linearity (see above)

      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.

Receives:

    InstantaneousRateConnectionEvent, DelayedRateConnectionEvent,
      DataLoggingRequest

Sends:

    InstantaneousRateConnectionEvent, DelayedRateConnectionEvent

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] 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:

    David Dahmen, Jan Hahne, Jannis Schuecker

SeeAlso:

- rate_connection_instantaneous
- rate_connection_delayed

Source:

    /opt/conda/conda-bld/nest_1531570948828/work/models/tanh_rate.h

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