threshold_lin_rate – Rate model with threshold-linear gain function¶
Description¶
threshold_lin_rate
is an implementation of a nonlinear rate model with
input function \(input(h) = min( max( g \cdot ( h - \theta ), 0 ),
\alpha )\). 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.
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.
Nonlinear rate neuron instances can be obtained by creating models of
type threshold_lin_rate_ipn
for input noise or of type
threshold_lin_rate_opn
output noise. Nonlinear rate transformers
can be obtained by creating models of type
rate_transformer_threshold_lin
.
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 |
alpha |
real |
Second Threshold |
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 |
References¶
Sends¶
InstantaneousRateConnectionEvent, DelayedRateConnectionEvent
Receives¶
InstantaneousRateConnectionEvent, DelayedRateConnectionEvent, DataLoggingRequest