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siegert_neuron – model for mean-field analysis of spiking networks¶
Description¶
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.
Remarks:
For details on the numerical solution of the Siegert integral, you can check out the Siegert_neuron_integration notebook in the NEST source code.
Parameters¶
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¶
- 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¶
DiffusionConnectionEvent
Receives¶
DiffusionConnectionEvent, DataLoggingRequest