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iaf_cond_exp_sfa_rr – Conductance based leaky integrate-and-fire model with spike-frequency adaptation and relative refractory mechanisms

Description

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 current 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.

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

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

Sends

SpikeEvent

Receives

SpikeEvent, CurrentEvent, DataLoggingRequest

References

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