lin_rate – Linear rate model

Description

lin_rate is an implementation of a rate model with linear input function \(input(h) = g \cdot h\). 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).

Linear rate neurons support multiplicative coupling which can be switched on and off via the boolean parameter mult_coupling (default=false). In case multiplicative coupling is active, the excitatory input of the model is multiplied with the function \(mult\_coupling\_ex(rate) = g_{ex} \cdot ( \theta_{ex} - rate )\) and the inhibitory input is multiplied with the function \(mult\_coupling\_in(rate) = g_{in} \cdot ( \theta_{in} + rate )\).

The model supports connections to other rate models with either zero or non-zero delay, and it uses the secondary_event concept introduced with the gap-junction framework.

Linear rate neurons can be created by typing nest.Create("lin_rate_ipn") or nest.Create("lin_rate_opn") for input noise or output noise, respectively. Linear rate transformers can be created by typing nest.Create("rate_transformer_lin").

See also 1, 2.

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

lambda

real

Passive decay rate

mu

real

Mean input

sigma

real

Noise parameter

g

real

Gain parameter

mult_coupling

boolean

Switch to enable/disable multiplicative coupling

g_ex

real

Linear factor in multiplicative coupling

g_in

real

Linear factor in multiplicative coupling

theta_ex

real

Shift in multiplicative coupling

theta_in

real

Shift in multiplicative coupling

rectify_rate

real

Rectifying rate

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 Neuroinformatics, 9:22. DOI: https://doi.org/10.3389/fninf.2015.00022

Sends

InstantaneousRateConnectionEvent, DelayedRateConnectionEvent

Receives

InstantaneousRateConnectionEvent, DelayedRateConnectionEvent, DataLoggingRequest

See also

Neuron, Rate