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lin_rate – Linear rate model¶
Description¶
lin_rate is an implementation of a rate model with linear input function \(input(h) = g * h\). 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 \(mult\_coupling\_ex(rate) = g_{ex} * ( \theta_{ex} - rate )\) and the inhibitory input is multiplied with the function \(mult\_coupling\_in(rate) = g_{in} * ( \theta_{in} + rate )\).
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’).
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