lin_rate - Linear rate model
lin_rate is an implementation of a linear rate model with
input function \f$ input(h) = g * h \f$.
The model supports multiplicative coupling which can
be switched on and off via the boolean parameter mult_coupling
(default=false). In case multiplicative coupling is actived
the excitatory input of the model is multiplied with the function
\f$ mult\_coupling\_ex(rate) = g_{ex} * ( \theta_{ex} - rate ) \f$
and the inhibitory input is multiplied with the function
\f$ mult\_coupling\_in(rate) = g_{in} * ( \theta_{in} + rate ) \f$.
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
lambda real Passive decay rate
mu real Mean input
sigma real Noise parameter
g real Gain parameter
mult_coupling boolean Switch to enable/disable multiplicative coupling
g_ex real Linear factor in multiplicative coupling
g_in real Linear factor in multiplicative coupling
theta_ex real Shift in multiplicative coupling
theta_in real Shift in multiplicative coupling
rectify_output boolean Switch to restrict rate to values >= 0
=============== ======= ==================================================
\endverbatim
InstantaneousRateConnectionEvent, DelayedRateConnectionEvent,
DataLoggingRequest
InstantaneousRateConnectionEvent, DelayedRateConnectionEvent
\verbatim embed:rst
.. [1] 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
.. [2] 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 Neuroinformatics, 9:22.
DOI: https://doi.org/10.3389/fninf.2015.00022
\endverbatim
David Dahmen, Jan Hahne, Jannis Schuecker
/var/www/debian/nest/nest-simulator-2.20.0/models/lin_rate.h