lin_rate - Linear rate model

lin_rate is an implementation of a linear rate model with
input function input(h) = g * h.
The model supports multiplicative coupling which can
be switched on and off via the boolean parameter mult_coupling
(default=false). In case multiplicative coupling is actived
the excitatory input of the model is multiplied with the function
mult_coupling_ex(rate) = g_ex_ * ( theta_ex_ - rate )
and the inhibitory input is multiplied with the function
mult_coupling_in(rate) = g_in_ * ( theta_in_ + rate ).

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.
lambda double - Passive decay rate.
mean double - Mean of Gaussian white noise.
std double - Standard deviation of Gaussian white noise.
g double - Gain parameter
mult_coupling bool - Switch to enable/disable multiplicative coupling.
g_ex double - Linear factor in multiplicative coupling.
g_in double - Linear factor in multiplicative coupling.
theta_ex double - Shift in multiplicative coupling.
theta_in double - Shift in multiplicative coupling.
rectify_output bool - Switch to restrict rate to values >= 0

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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