aeif_cond_alpha – Conductance-based adaptive exponential integrate-and-fire neuron model
Description
aeif_cond_alpha
is a conductance-based adaptive exponential
integrate-and-fire neuron model according to [1] with synaptic
conductance modeled by an alpha function, as described in [2]
This implementation uses the 5th order Runge-Kutta solver with adaptive step size to integrate the differential equation.
The membrane potential is given by the following differential equation:
The differential equation for the spike-adaptation current w is
When the neuron fires a spike, the adaptation current \(w <- w + b\).
Note
Although this is not multisynapse, the port (excitatory or inhibitory)
to be chosen must be specified using the synapse property receptor
.
The excitatory port has index 0, whereas the inhibitory one has index 1. Differently from
NEST, the connection weights related to the inhibitory port must be positive.
Parameters
The following parameters can be set in the status dictionary.
Dynamic state variables: |
||
V_m |
mV |
Membrane potential |
g_ex |
nS |
Excitatory synaptic conductance |
g_in |
nS |
Inhibitory synaptic conductance |
w |
pA |
Spike-adaptation current |
Membrane Parameters |
||
V_th |
mV |
Spike initiation threshold |
Delta_T |
mV |
Slope factor |
g_L |
nS |
Leak conductance |
E_L |
mV |
Leak reversal potential |
C_m |
pF |
Capacity of the membrane |
I_e |
pA |
Constant external input current |
V_peak |
mV |
Spike detection threshold |
V_reset |
mV |
Reset value for V_m after a spike |
t_ref |
ms |
Duration of refractory period |
den_delay |
ms |
Dendritic delay |
Spike adaptation parameters |
||
a |
ns |
Subthreshold adaptation |
b |
pA |
Spike-triggered adaptation |
tau_w |
ms |
Adaptation time constant |
Synaptic parameters |
||
E_rev_ex |
mV |
Excitatory reversal potential |
E_rev_in |
mV |
Inhibitory reversal potential |
tau_syn_ex |
ms |
Time constant of excitatory synaptic conductance |
tau_syn_in |
ms |
Time constant of inhibitory synaptic conductance |
Integration parameters |
||
h0_rel |
real |
Starting step in ODE integration relative to time resolution |
h_min_rel |
real |
Minimum step in ODE integration relative to time resolution |
References
See also
Neuron, Integrate-And-Fire, Adaptive Threshold, Conductance-Based