gif_pop_psc_exp - Population of generalized integrate-and-fire neurons
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 [1].
The single neuron model is defined by the hazard function
@f[ lambda_0 * exp[ ( V_m - E_sfa ) / Delta_V ] @f]
After each spike the membrane potential V_m is reset to V_reset. Spike
frequency
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
tau_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 due to historical reasons. See below for the parameter
translation.
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 [1]). 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.
\verbatim embed:rst
=========== ============= =====================================================
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 \f$ tau_m = C_m / g_L \f$
N --- use N gif_psc_exp
=============== ============ =============================
\endverbatim
SpikeEvent, CurrentEvent, DataLoggingRequest
SpikeEvent
\verbatim embed:rst
.. [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
\endverbatim
Nov 2016, Moritz Deger, Tilo Schwalger, Hesam Setareh
/var/www/debian/nest/nest-simulator-2.20.0/models/gif_pop_psc_exp.h