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.

For details on the numerical solution of the Siegert integral, you can check out the Siegert_neuron_integration notebook in the NEST source code.

See also [1], [4].

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

Sends

DiffusionConnectionEvent

Receives

DiffusionConnectionEvent, DataLoggingRequest

See also

Neuron, Rate

Examples using this model