sigmoid_rate_gg_1998 – rate model with sigmoidal gain function¶
Description¶
sigmoid_rate_gg_1998
is an implementation of a nonlinear rate model with
input function as in [1] \(input(h) = ( g \cdot h )^4 / ( .1^4 +( g \cdot h )^4 )\).
It either models a rate neuron with input noise (see rate_neuron_ipn
)
or a rate transformer (see rate_transformer_node
).
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.
Nonlinear rate neurons can be created by typing
nest.Create('sigmoid_rate_gg_1998_ipn')
. Nonlinear rate transformers can be
created by typing nest.Create('rate_transformer_sigmoid_rate_gg_1998')
.
Parameters¶
The following parameters can be set in the status dictionary. Note that some of the parameters only apply to rate neurons and not to rate transformers.
rate |
real |
Rate (unitless) |
tau |
ms |
Time constant of rate dynamics |
mu |
real |
Mean input |
sigma |
real |
Noise parameter |
g |
real |
Gain parameter |
rectify_rate |
real |
Rectifying rate |
linear_summation |
boolean |
Specifies type of non-linearity (see above) |
rectify_output |
boolean |
Switch to restrict rate to values >= rectify_rate |
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.
References¶
Sends¶
InstantaneousRateConnectionEvent, DelayedRateConnectionEvent
Receives¶
InstantaneousRateConnectionEvent, DelayedRateConnectionEvent, DataLoggingRequest