iaf_cond_exp_sfa_rr – Conductance based leaky integrate-and-fire model with spike-frequency adaptation and relative refractory mechanisms
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Description
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``iaf_cond_exp_sfa_rr`` is an implementation of a spiking neuron using
integrate-and-fire dynamics with conductance-based synapses, with additional
spike-frequency adaptation and relative refractory mechanisms as described in
[2]_, page 166.
Incoming spike events induce a postsynaptic change of conductance modelled by
an exponential function. The exponential function is normalized such that an
event of weight 1.0 results in a peak conductance of 1 nS.
Outgoing spike events induce a change of the adaptation and relative refractory
conductances by ``q_sfa`` and ``q_rr``, respectively. Otherwise these conductances
decay exponentially with time constants ``tau_sfa`` and ``tau_rr``, respectively.
See also [1]_.
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
t_ref ms Duration of refractory period
V_th mV Spike threshold
V_reset mV Reset potential of the membrane
E_ex mV Excitatory reversal potential
E_in mV Inhibitory reversal potential
g_L nS Leak conductance
tau_syn_ex ms Exponential decay time constant of excitatory synaptic
conductance kernel
tau_syn_in ms Exponential decay time constant of inhibitory synaptic
conductance kernel
q_sfa nS Outgoing spike activated quantal spike-frequency adaptation
conductance increase in nS
q_rr nS Outgoing spike activated quantal relative refractory
conductance increase in nS
tau_sfa ms Time constant of spike-frequency adaptation in ms
tau_rr ms Time constant of the relative refractory mechanism in ms
E_sfa mV Spike-frequency adaptation conductance reversal potential
in mV
E_rr mV Relative refractory mechanism conductance reversal
potential in mV
I_e pA Constant input current
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Sends
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SpikeEvent
Receives
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SpikeEvent, CurrentEvent, DataLoggingRequest
References
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.. [1] Meffin H, Burkitt AN, Grayden DB (2004). An analytical
model for the large, fluctuating synaptic conductance state typical of
neocortical neurons in vivo. Journal of Computational Neuroscience,
16:159-175.
DOI: https://doi.org/10.1023/B:JCNS.0000014108.03012.81
.. [2] Dayan P, Abbott LF (2001). Theoretical neuroscience: Computational and
mathematical modeling of neural systems. Cambridge, MA: MIT Press.
https://pure.mpg.de/pubman/faces/ViewItemOverviewPage.jsp?itemId=item_3006127
See also
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aeif_cond_alpha, aeif_cond_exp, iaf_chxk_2008
Examples using this model
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.. listexamples:: iaf_cond_exp_sfa_rr