iaf_psc_delta – Current-based leaky integrate-and-fire neuron model with delta-shaped postsynaptic currents
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Description
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``iaf_psc_delta`` is an implementation of a leaky integrate-and-fire model
where the potential jumps on each spike arrival.
The threshold crossing is followed by an absolute refractory period
during which the membrane potential is clamped to the resting potential.
Spikes arriving while the neuron is refractory, are discarded by
default. If the property ``refractory_input`` is set to true, such
spikes are added to the membrane potential at the end of the
refractory period, dampened according to the interval between
arrival and end of refractoriness.
The linear subthreshold dynamics is integrated by the Exact
Integration scheme [1]_. The neuron dynamics is solved on the time
grid given by the computation step size. Incoming as well as emitted
spikes are forced to that grid.
An additional state variable and the corresponding differential
equation represents a piecewise constant external current.
The general framework for the consistent formulation of systems with
neuron like dynamics interacting by point events is described in
[1]_. A flow chart can be found in [2]_.
Critical tests for the formulation of the neuron model are the
comparisons of simulation results for different computation step
sizes. sli/testsuite/nest contains a number of such tests.
The iaf_psc_delta is the standard model used to check the consistency
of the nest simulation kernel because it is at the same time complex
enough to exhibit non-trivial dynamics and simple enough compute
relevant measures analytically.
Parameters
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The following parameters can be set in the status dictionary.
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V_m mV Membrane potential
E_L mV Resting membrane potential
C_m pF Capacity of the membrane
tau_m ms Membrane time constant
t_ref ms Duration of refractory period
V_th mV Spike threshold
V_reset mV Reset potential of the membrane
I_e pA Constant input current
V_min mV Absolute lower value for the membrane potential
refractory_input boolean If true, do not discard input during
refractory period. Default: false
================= ======= ======================================================
References
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.. [1] Rotter S, Diesmann M (1999). Exact simulation of
time-invariant linear systems with applications to neuronal
modeling. Biologial Cybernetics 81:381-402.
DOI: https://doi.org/10.1007/s004220050570
.. [2] Diesmann M, Gewaltig M-O, Rotter S, & Aertsen A (2001). State
space analysis of synchronous spiking in cortical neural
networks. Neurocomputing 38-40:565-571.
DOI: https://doi.org/10.1016/S0925-2312(01)00409-X
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 `