aeif_cond_alpha – Conductance based exponential integrate-and-fire neuron model¶
Description¶
aeif_cond_alpha
is the adaptive exponential integrate and fire neuron according
to Brette and Gerstner (2005).
Synaptic conductances are modelled as alpha-functions.
This implementation uses the embedded 4th order Runge-Kutta-Fehlberg solver with adaptive step size to integrate the differential equation.
The membrane potential is given by the following differential equation:
and
For implementation details see the aeif_models_implementation notebook.
See also [1].
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 |
dg_ex |
nS/ms |
First derivative of g_ex |
g_in |
nS |
Inhibitory synaptic conductance |
dg_in |
nS/ms |
First derivative of g_in |
w |
pA |
Spike-adaptation current |
Membrane Parameters |
||
C_m |
pF |
Capacity of the membrane |
t_ref |
ms |
Duration of refractory period |
V_reset |
mV |
Reset value for V_m after a spike |
E_L |
mV |
Leak reversal potential |
g_L |
nS |
Leak conductance |
I_e |
pA |
Constant external input current |
Spike adaptation parameters |
||
a |
ns |
Subthreshold adaptation |
b |
pA |
Spike-triggered adaptation |
Delta_T |
mV |
Slope factor |
tau_w |
ms |
Adaptation time constant |
V_th |
mV |
Spike initiation threshold |
V_peak |
mV |
Spike detection threshold |
Synaptic parameters |
||
E_ex |
mV |
Excitatory reversal potential |
tau_syn_ex |
ms |
Rise time of excitatory synaptic conductance (alpha function) |
E_in |
mV |
Inhibitory reversal potential |
tau_syn_in |
ms |
Rise time of the inhibitory synaptic conductance (alpha function) |
Integration parameters |
||
gsl_error_tol |
real |
This parameter controls the admissible error of the GSL integrator. Reduce it if NEST complains about numerical instabilities. |
Sends¶
SpikeEvent
Receives¶
SpikeEvent, CurrentEvent, DataLoggingRequest
References¶
See also¶
Neuron, Integrate-And-Fire, Adaptive Threshold, Conductance-Based