lin_rate


Name:
lin_rate - Linear rate model
Description:
 

lin_rate is an implementation of a linear rate model with either
input (lin_rate_ipn) or output noise (lin_rate_opn) and gain function
Phi(h) = g * h.

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.

Parameters:
 

The following parameters can be set in the status dictionary.

rate double - Rate (unitless)
tau double - Time constant of rate dynamics in ms.
mean double - Mean of Gaussian white noise.
std double - Standard deviation of Gaussian white noise.
g double - Gain parameter

Receives:
InstantaneousRateConnectionEvent, DelayedRateConnectionEvent,  
DataLoggingRequest

Sends:
InstantaneousRateConnectionEvent, DelayedRateConnectionEvent  

References:
 

[1] Hahne, J., Dahmen, D., Schuecker, J., Frommer, A.,
Bolten, M., Helias, M. and Diesmann, M. (2017).
Integration of Continuous-Time Dynamics in a
Spiking Neural Network Simulator.
Front. Neuroinform. 11:34. doi: 10.3389/fninf.2017.00034

[2] Hahne, J., Helias, M., Kunkel, S., Igarashi, J.,
Bolten, M., Frommer, A. and Diesmann, M. (2015).
A unified framework for spiking and gap-junction interactions
in distributed neuronal network simulations.
Front. Neuroinform. 9:22. doi: 10.3389/fninf.2015.00022

Author:
David Dahmen, Jan Hahne, Jannis Schuecker  
SeeAlso: Source:
/home/nest/work/nest-2.14.0/models/lin_rate.h