sigmoid_rate_gg_1998 - rate model with sigmoidal gain function
as defined in [1].
sigmoid_rate_gg_1998 is an implementation of a nonlinear rate model with
input function \f$ input(h) = ( g * h )^4 / ( .1^4 + ( g * h )^4 ) \f$.
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.
\verbatim embed:rst
================== ======= ==============================================
rate real Rate (unitless)
tau ms Time constant of rate dynamics
mu real Mean input
sigma real Noise parameter
g real Gain parameter
linear_summation boolean Specifies type of non-linearity (see above)
rectify_output boolean Switch to restrict rate to values >= 0
================== ======= ==============================================
\endverbatim
-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.
InstantaneousRateConnectionEvent, DelayedRateConnectionEvent,
DataLoggingRequest
InstantaneousRateConnectionEvent, DelayedRateConnectionEvent
\verbatim embed:rst
.. [1] Gancarz G, Grossberg S (1998). A neural model of the saccade generator
in the reticular formation. Neural Networks, 11(7):1159–1174.
DOI: https://doi.org/10.1016/S0893-6080(98)00096-3
.. [2] Hahne J, Dahmen D, Schuecker J, Frommer A, Bolten M, Helias M,
Diesmann M (2017). Integration of continuous-time dynamics in a
spiking neural network simulator. Frontiers in Neuroinformatics, 11:34.
DOI: https://doi.org/10.3389/fninf.2017.00034
.. [3] Hahne J, Helias M, Kunkel S, Igarashi J, Bolten M, Frommer A, Diesmann M
(2015). A unified framework for spiking and gap-junction interactions
in distributed neuronal network simulations. Frontiers in
Neuroinformatics, 9:22. DOI: https://doi/org/10.3389/fninf.2015.00022
\endverbatim
Mario Senden, Jan Hahne, Jannis Schuecker
/var/www/debian/nest/nest-simulator-2.18.0/models/sigmoid_rate_gg_1998.h