gif_pop_psc_exp – Population of generalized integrate-and-fire neurons with exponential postsynaptic currents and adaptation¶
Description¶
This model simulates a population of spike-response model neurons with multi-timescale adaptation and exponential postsynaptic currents, as described by Schwalger et al. (2017) 1.
The single neuron model is defined by the hazard function
After each spike, the membrane potential \(V_m\) is reset to \(V_{\text{reset}}\). Spike frequency adaptation is implemented by a set of exponentially decaying traces, the sum of which is \(E_{\text{sfa}}\). Upon a spike, each of the adaptation traces is incremented by the respective \(q_{\text{sfa}}\) and decays with the respective time constant \(\tau_{\text{sfa}}\).
The corresponding single neuron model is available in NEST as gif_psc_exp
.
The default parameters, although some are named slightly different, are not
matched in both models for historical reasons. See below for the parameter
translation.
Connecting two population models corresponds to full connectivity of every
neuron in each population. An approximation of random connectivity can be
implemented by connecting populations through a spike_dilutor
.
Parameters¶
The following parameters can be set in the status dictionary.
V_reset |
mV |
Membrane potential is reset to this value after a spike |
V_T_star |
mV |
Threshold level of the membrane potential |
E_L |
mV |
Resting potential |
Delta_V |
mV |
Noise level of escape rate |
C_m |
pF |
Capacitance of the membrane |
tau_m |
ms |
Membrane time constant |
t_ref |
ms |
Duration of refractory period |
I_e |
pA |
Constant input current |
N |
integer |
Number of neurons in the population |
len_kernel |
integer |
Refractory effects are accounted for up to len_kernel time steps |
lambda_0 |
1/s |
Firing rate at threshold |
tau_syn_ex |
ms |
Time constant for excitatory synaptic currents |
tau_syn_in |
ms |
Time constant for inhibitory synaptic currents |
tau_sfa |
list of ms |
vector Adaptation time constants |
q_sfa |
list of ms |
Adaptation kernel amplitudes |
BinoRand |
boolean |
If True, binomial random numbers are used, otherwise we use Poisson distributed spike counts |
Parameter translation to gif_psc_exp |
||
gif_pop_psc_exp |
gif_psc_exp |
relation |
tau_m |
g_L |
tau_m = C_m / g_L |
N |
— |
use N gif_psc_exp neurons |
References¶
- 1
Schwalger T, Deger M, Gerstner W (2017). Towards a theory of cortical columns: From spiking neurons to interacting neural populations of finite size. PLoS Computational Biology. https://doi.org/10.1371/journal.pcbi.1005507
Sends¶
SpikeEvent
Receives¶
SpikeEvent, CurrentEvent, DataLoggingRequest