Warning

This is A PREVIEW for NEST 3.0 and NOT an OFFICIAL RELEASE! Some functionality may not be available and information may be incomplete!

aeif_cond_exp – Conductance based exponential integrate-and-fire neuron model

Description

aeif_cond_exp is the adaptive exponential integrate and fire neuron according to Brette and Gerstner (2005), with postsynaptic conductances in the form of truncated exponentials.

This implementation uses the embedded 4th order Runge-Kutta-Fehlberg solver with adaptive stepsize to integrate the differential equation.

The membrane potential is given by the following differential equation:

\[\begin{split}C dV/dt= -g_L(V-E_L)+g_L*\Delta_T*\exp((V-V_T)/\Delta_T)-g_e(t)(V-E_e) \\ -g_i(t)(V-E_i)-w +I_e\end{split}\]

and

\[\tau_w * dw/dt= a(V-E_L) -W\]

Note that the spike detection threshold V_peak is automatically set to \(V_th+10 mV\) to avoid numerical instabilites that may result from setting V_peak too high.

For implementation details see the aeif_models_implementation notebook.

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

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

Exponential decay time constant of excitatory synaptic conductance kernel

E_in

mV

Inhibitory reversal potential

tau_syn_in

ms

Exponential decay time constant of inhibitory synaptic conductance kernel

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

1

Brette R and Gerstner W (2005). Adaptive Exponential Integrate-and-Fire Model as an Effective Description of Neuronal Activity. J Neurophysiol 94:3637-3642. DOI: https://doi.org/10.1152/jn.00686.2005