gauss_rate - rate model with Gaussian gain function

gauss_rate is an implementation of a nonlinear rate model with input
function input(h) = g * exp( -( x - mu )^2 / ( 2 * sigma^2 ) ).
Input transformation can either be applied to individual inputs
or to the sum of all inputs.

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.
mu double - Mean of the Gaussian gain function.
sigma double - Standard deviation of Gaussian gain function.
linear_summation bool - Specifies type of non-linearity (see above).
rectify_output bool - Switch to restrict rate to values >= 0.

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

Mario Senden, Jan Hahne, Jannis Schuecker  
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