siegert_neuron is an implementation of a rate model with the
non-linearity given by the gain function of the
leaky-integrate-and-fire neuron with delta or exponentially decaying
synapses [2] and [3, their eq. 25]. The model can be used for a
mean-field analysis of spiking networks.

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


The following parameters can be set in the status dictionary.

rate double - Rate (1/s)
tau double - Time constant in ms.
mean double - Additional constant input

The following parameters can be set in the status directory and are
used in the evaluation of the gain function. Parameters as in

tau_m double - Membrane time constant in ms.
tau_syn double - Time constant of postsynaptic currents in ms.
t_ref double - Duration of refractory period in ms.
theta double - Threshold relative to resting potential in mV.
V_reset double - Reset relative to resting membrane potential in


DiffusionConnectionEvent, DataLoggingRequest  



[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] Fourcaud, N and Brunel, N. (2002). Dynamics of the firing
probability of noisy integrate-and-fire neurons, Neural computation,
14:9, pp 2057--2110

[3] Schuecker, J., Diesmann, M. and Helias, M. (2015).
Modulated escape from a metastable state driven by colored noise.
Physical Review E 92:052119

[4] 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

Jannis Schuecker, David Dahmen, Jan Hahne  
SeeAlso: Source: