Warning

This is A PREVIEW for NEST 3.0 and NOT an OFFICIAL RELEASE! Some functionality may not be available and information may be incomplete!

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

See also

Neuron, Rate