izhikevich_cond_beta – Conductance-based Izhikevich neuron model
Description
Implementation of the simple spiking neuron model introduced by Izhikevich [1] with synaptic conductance modeled by a beta function, as described in [2]. The dynamics are given by:
This implementation uses the standard technique for forward Euler integration.
This model is multisynapse, so it allows an arbitrary number of synaptic
rise time and decay time constants. The number of receptor ports must be specified
at neuron creation (default value is 1) and the receptor index starts from 0
(and not from 1 as in NEST multisynapse models).
The time constants are supplied by by two arrays, tau_rise
and tau_decay
for
the synaptic rise time and decay time, respectively. The synaptic
reversal potentials are supplied by the array E_rev
. Port numbers
are automatically assigned in the range from 0 to n_receptors-1
.
During connection, the ports are selected with the synapse property receptor
.
Parameters
The following parameters can be set in the status dictionary.
V_m |
mV |
Membrane potential |
u |
mV |
Membrane potential recovery variable |
V_th |
mV |
Spike threshold |
a |
real |
Describes time scale of recovery variable |
b |
real |
Sensitivity of recovery variable |
c |
mV |
After-spike reset value of V_m |
d |
mV |
After-spike reset value of u |
I_e |
pA |
Constant input current |
t_ref |
ms |
Refractory time |
den_delay |
ms |
Dendritic delay |
E_rev |
mV |
Leak reversal potential |
tau_rise |
ms |
Rise time constant of synaptic conductance |
tau_decay |
ms |
Decay time constant of synaptic conductance |
h_min_rel |
real |
Starting step in ODE integration relative to time resolution |
h0_rel |
real |
Minimum step in ODE integration relative to time resolution |