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.
\verbatim embed:rst
===== ====== ==============================
rate 1/s Rate (1/s)
tau ms Time constant
mean real Additional constant input
===== ====== ==============================
\endverbatim
The following parameters can be set in the status directory and are
used in the evaluation of the gain function. Parameters as in
iaf_psc_exp/delta.
\verbatim embed:rst
========= ====== =====================================================
tau_m ms Membrane time constant
tau_syn ms Time constant of postsynaptic currents
t_ref ms Duration of refractory period
theta mV Threshold relative to resting potential
V_reset mV Reset relative to resting membrane potential
========= ====== =====================================================
\endverbatim
DiffusionConnectionEvent, DataLoggingRequest
DiffusionConnectionEvent
\verbatim embed:rst
.. [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] Fourcaud N, Brunel N (2002). Dynamics of the firing
probability of noisy integrate-and-fire neurons, Neural Computation,
14(9):2057-2110
DOI: https://doi.org/10.1162/089976602320264015
.. [3] Schuecker J, Diesmann M, Helias M (2015). Modulated escape from a
metastable state driven by colored noise. Physical Review E 92:052119
DOI: https://doi.org/10.1103/PhysRevE.92.052119
.. [4] 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 in
Neuroinformatics, 9:22. DOI: https://doi.org/10.3389/fninf.2015.00022
\endverbatim
Jannis Schuecker, David Dahmen, Jan Hahne
/var/www/debian/nest/nest-simulator-2.20.0/models/siegert_neuron.h