iaf_chxk_2008 – Conductance-based leaky integrate-and-fire neuron model supporting precise spike times used in Casti et al. 2008
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Description
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``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
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The following parameters can be set in the status dictionary.
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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
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.. [1] 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
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SpikeEvent
Receives
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SpikeEvent, CurrentEvent
See also
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:doc:`Neuron `, :doc:`Integrate-And-Fire `, :doc:`Conductance-Based `, :doc:`Precise `
Examples using this model
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.. listexamples:: iaf_chxk_2008