tanh_rate - rate model with hyperbolic tangent non-linearity

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.


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)

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.

InstantaneousRateConnectionEvent, DelayedRateConnectionEvent,  

InstantaneousRateConnectionEvent, DelayedRateConnectionEvent  


[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

David Dahmen, Jan Hahne, Jannis Schuecker  
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