gif_pop_psc_exp – Population of generalized integrate-and-fire neurons (GIF) with exponential postsynaptic currents and adaptation (from the Gerstner lab)
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Description
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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
.. math::
h(t) = \lambda_0 \exp\frac{V_m(t) - E_{\text{sfa}}(t)}{\Delta_V}
After each spike, the membrane potential :math:`V_m` is reset to
:math:`V_{\text{reset}}`. Spike frequency
adaptation is implemented by a set of exponentially decaying traces, the
sum of which is :math:`E_{\text{sfa}}`. Upon a spike, each of the adaptation traces is
incremented by the respective :math:`q_{\text{sfa}}` and decays with the respective time constant
:math:`\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 using a ``bernoulli_synapse``.
Parameters
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The following parameters can be set in the status dictionary.
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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
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**Parameter translation to gif_psc_exp**
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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
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References
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.. [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
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SpikeEvent
Receives
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SpikeEvent, CurrentEvent, DataLoggingRequest
See also
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:doc:`Neuron `, :doc:`Integrate-And-Fire `, :doc:`Current-Based `