siegert_neuron – model for mean-field analysis of spiking networks
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Description
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``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. A constant mean input can be
provided to create neurons with a target rate, e.g. to model a constant
external input.
The model supports connections to other rate models with zero
delay, and uses the secondary_event concept introduced with the
gap-junction framework.
For details on the numerical solution of the Siegert integral, you can
check out the `Siegert_neuron_integration
<../model_details/siegert_neuron_integration.ipynb>`_
notebook in the NEST source code.
See also [1]_, [4]_.
Parameters
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The following parameters can be set in the status dictionary.
===== ====== ==============================
rate 1/s Rate (1/s)
tau ms Time constant
mean 1/s 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
iaf_psc_exp/delta.
========= ====== ================================================
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 potential
========= ====== ================================================
References
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.. [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
Sends
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DiffusionConnectionEvent
Receives
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DiffusionConnectionEvent, DataLoggingRequest
See also
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:doc:`Neuron `, :doc:`Rate `