Warning

This is A PREVIEW for NEST 3.0 and NOT an OFFICIAL RELEASE! Some functionality may not be available and information may be incomplete!

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 \(input(h) = min( max( g * ( h - \theta ), 0 ), \alpha )\). 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

1

Hahne J, Dahmen D, Schuecker J, Frommer A, Bolten M, Helias M, Diesmann M (2017). Integration of continuous-time dynamics in a spiking neural network simulator. Frontiers in Neuroinformatics, 11:34. DOI: https://doi.org/10.3389/fninf.2017.00034

2

Hahne J, Helias M, Kunkel S, Igarashi J, Bolten M, Frommer A, Diesmann M (2015). A unified framework for spiking and gap-junction interactions in distributed neuronal network simulations. Frontiers in Neuroinformatics, 9:22. DOI: https://doi.org/10.3389/fninf.2015.00022

Sends

InstantaneousRateConnectionEvent, DelayedRateConnectionEvent

Receives

InstantaneousRateConnectionEvent, DelayedRateConnectionEvent, DataLoggingRequest

See also

Neuron, Rate