gauss_rate – Rate neuron model with Gaussian gain function¶
Description¶
gauss_rate is an implementation of a nonlinear rate model with input
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(‘gauss_rate_ipn’). Nonlinear rate transformers can be created by typing nest.Create(‘rate_transformer_gauss’).
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 |
mu |
real |
Mean of the Gaussian gain function |
sigma |
real |
Standard deviation of Gaussian gain function |
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
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 Mi (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