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