threshold_lin_rate - rate model with threshold-linear gain function
threshold_lin_rate is an implementation of a nonlinear rate model with input
function input(h) = min( max( g * ( h - theta ), 0 ), alpha ).
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.
mu double - Mean input.
sigma double - Noise parameter.
g double - Gain parameter
theta double - First Threshold
alpha double - Second Threshold
linear_summation bool - Specifies type of non-linearity (see above)
rectify_output bool - Switch to restrict rate to values >= 0
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
[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
/usr/src/packages/BUILD/models/threshold_lin_rate.h