**Name:**

lin_rate - Linear rate model

**Description:**

lin_rate is an implementation of a linear rate model with

input function input(h) = g * h.

The model supports multiplicative coupling which can

be switched on and off via the boolean parameter mult_coupling

(default=false). In case multiplicative coupling is actived

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 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.

lambda double - Passive decay rate.

mean double - Mean of Gaussian white noise.

std double - Standard deviation of Gaussian white noise.

g double - Gain parameter

mult_coupling bool - Switch to enable/disable multiplicative coupling.

g_ex double - Linear factor in multiplicative coupling.

g_in double - Linear factor in multiplicative coupling.

theta_ex double - Shift in multiplicative coupling.

theta_in double - Shift in multiplicative coupling.

rectify_output bool - Switch to restrict rate to values >= 0

**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/graber/work-nest/nest-git/nest-simulator/models/lin_rate.h