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

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

\[h(t) = \lambda_0 \exp\frac{V_m(t) - E_{\text{sfa}}(t)}{\Delta_V}\]

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