sigmoid_rate_gg_1998 – rate model with sigmoidal gain function

Description

sigmoid_rate_gg_1998 is an implementation of a nonlinear rate model with input function as in [1] \(input(h) = ( g \cdot h )^4 / ( .1^4 +( g \cdot h )^4 )\).

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('sigmoid_rate_gg_1998_ipn'). Nonlinear rate transformers can be created by typing nest.Create('rate_transformer_sigmoid_rate_gg_1998').

See also [2], [3].

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
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

[1]

Gancarz G, Grossberg S (1998). A neural model of the saccade generator in the reticular formation. Neural Networks, 11(7):1159–1174. DOI: https://doi.org/10.1016/S0893-6080(98)00096-3

[2]

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

[3]

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