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iaf_chxk_2008 – Conductance-based leaky integrate-and-fire neuron model supporting precise spike times used in Casti et al. 2008

Description

iaf_chxk_2008 is an implementation of a spiking neuron using IAF dynamics with conductance-based synapses 1. A spike is emitted when the membrane potential is crossed from below. After a spike, an afterhyperpolarizing (AHP) conductance is activated which repolarizes the neuron over time. Membrane potential is not reset explicitly and the model also has no explicit refractory time.

The AHP conductance and excitatory and inhibitory synaptic input conductances follow alpha-function time courses as in the iaf_cond_alpha model.

Note

In accordance with the original Fortran implementation of the model used in 1, the activation time point for the AHP following a spike is determined by linear interpolation within the time step during which the threshold was crossed.

iaf_chxk_2008 neurons therefore emit spikes with precise spike time information, but they ignore precise spike times when handling synaptic input.

Note

In the original Fortran implementation underlying 1, all previous AHP activation was discarded when a new spike occurred, leading to reduced AHP currents in particular during periods of high spiking activity. Set ahp_bug to true to obtain this behavior in the model.

Parameters

The following parameters can be set in the status dictionary.

V_m

mV

Membrane potential

E_L

mV

Leak reversal potential

C_m

pF

Capacity of the membrane

V_th

mV

Spike threshold

E_ex

mV

Excitatory reversal potential

E_in

mV

Inhibitory reversal potential

g_L

nS

Leak conductance

tau_ex

ms

Rise time of the excitatory synaptic alpha function

tau_in

ms

Rise time of the inhibitory synaptic alpha function

I_e

pA

Constant input current

tau_ahp

ms

Afterhyperpolarization (AHP) time constant

E_ahp

mV

AHP potential

g_ahp

nS

AHP conductance

ahp_bug

boolean

Defaults to false. If true, behaves like original model implementation

References

1(1,2,3)

Casti A, Hayot F, Xiao Y, Kaplan E (2008) A simple model of retina-LGN transmission. Journal of Computational Neuroscience 24:235-252. DOI: https://doi.org/10.1007/s10827-007-0053-7

Sends

SpikeEvent

Receives

SpikeEvent, CurrentEvent