gif_pop_psc_exp - Population of generalized integrate-and-fire neuronsDescription:
with exponential postsynaptic currents and adaptation
This model simulates a population of spike-response model neurons with
multi-timescale adaptation and exponential postsynaptic currents, as
described in .
The single neuron model is defined by the hazard function
lambda_0 * exp[ ( V_m - E_sfa ) / Delta_V ]
After each spike the membrane potential V_m is reset to V_reset. Spike
adaptation is implemented by a set of exponentially decaying traces, the
sum of which is E_sfa. Upon a spike, all adaptation traces are incremented
by the respective q_sfa each and decay with the respective time constant
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 due to historical reasons. See below for the parameter
As gif_pop_psc_exp represents many neurons in one node, it may send a lot
of spikes. In each time step, it sends at most one spike though, the
multiplicity of which is set to the number of emitted spikes. Postsynaptic
neurons and devices in NEST understand this as several spikes, but
communication effort is reduced in simulations.
This model uses a new algorithm to directly simulate the population activity
(sum of all spikes) of the population of neurons, without explicitly
representing each single neuron (see ). The computational cost is largely
independent of the number N of neurons represented. The algorithm used
here is fundamentally different from and likely much faster than the one
used in the previously added population model pp_pop_psc_delta.
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.
The following parameters can be set in the status dictionary.
V_reset double - Membrane potential is reset to this value in mV after a
V_T_star double - Threshold level of the membrane potential in mV.
E_L double - Resting potential in mV
Delta_V double - Noise level of escape rate in mV.
C_m double - Capacitance of the membrane in pF.
tau_m double - Membrane time constant in ms.
t_ref double - Duration of refractory period in ms.
I_e double - Constant input current in pA.
N long - Number of neurons in the population.
len_kernel long - Refractory effects are accounted for up to len_kernel
lambda_0 double - Firing rate at threshold in 1/s.
tau_syn_ex double - Time constant for excitatory synaptic currents in ms.
tau_syn_in double - Time constant for inhibitory synaptic currents in ms.
tau_sfa double vector - Adaptation time constants in ms.
q_sfa double vector - Adaptation kernel amplitudes in ms.
BinoRand bool - 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
SpikeEvent, CurrentEvent, DataLoggingRequestSends:
Authors: Nov 2016, Moritz Deger, Tilo Schwalger, Hesam Setareh
 Towards a theory of cortical columns: From spiking neurons to
interacting neural populations of finite size
Tilo Schwalger, Moritz Deger, Wulfram Gerstner
PLoS Comput Biol 2017