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.

See also [1].

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

See also

Neuron, Integrate-And-Fire, Conductance-Based

Examples using this model

• Initial membrane voltage