tanh_rate – rate model with hyperbolic tangent non-linearity¶
Description¶
tanh_rate
is an implementation of a nonlinear rate model with input
function \(input(h) = \tanh(g \cdot (h-\theta))\). It either models a
rate neuron with input noise (see rate_neuron_ipn
), a rate neuron with
output noise (see rate_neuron_opn
) 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("tanh_rate_ipn")
or nest.Create("tanh_rate_opn")
for input
noise or output noise, respectively. Nonlinear rate transformers can
be created by typing nest.Create("rate_transformer_tanh")
.
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 |
theta |
real |
Threshold |
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