iaf_chs_2007 – Spike-response model used in Carandini et al. 2007
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Description
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The membrane potential is the sum of stereotyped events: the postsynaptic
potentials (``V_syn``), waveforms that include a spike and the subsequent
after-hyperpolarization (``V_spike``) and Gaussian-distributed white noise.

The postsynaptic potential is described by alpha function where
``U_epsp`` is the maximal amplitude of the EPSP and ``tau_epsp`` is the time to
peak of the EPSP.

The spike waveform is described as a delta peak followed by a membrane
potential reset and exponential decay. ``U_reset`` is the magnitude of the
reset/after-hyperpolarization and ``tau_reset`` is the time constant of
recovery from this hyperpolarization.

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.

.. note::

   The way the noise term was implemented in the original model makes
   it unsuitable for simulation in NEST. The workaround was to prepare
   the noise signal externally prior to simulation. The noise signal,
   if present, has to be at least as long as the simulation.

See also [2]_.

Parameters
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The following parameters can be set in the status dictionary.

========== ============== ==================================================
 tau_epsp  ms             Membrane time constant
 tau_reset ms             Refractory time constant
 U_epsp    real           Maximum amplitude of the EPSP, normalized
 U_reset   real           Reset value of the membrane potential, normalized
 U_noise   real           Noise scale, normalized
 noise     list of real   Noise signal
========== ============== ==================================================

References
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.. [1] Carandini M, Horton JC, Sincich LC (2007). Thalamic filtering of retinal
       spike trains by postsynaptic summation. Journal of Vision 7(14):20,1-11.
       DOI: https://doi.org/10.1167/7.14.20
.. [2] 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

Sends
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SpikeEvent

Receives
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SpikeEvent, DataLoggingRequest