gauss_rate – Rate neuron model with Gaussian gain function¶
Description¶
gauss_rate
is an implementation of a nonlinear rate model with input
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("gauss_rate_ipn")
. Nonlinear rate transformers can be
created by typing nest.Create("rate_transformer_gauss")
.
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 |
mu |
real |
Mean of the Gaussian gain function |
sigma |
real |
Standard deviation of Gaussian gain function |
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