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