# Command: iaf_chs_2007

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Name:

    iaf_chs_2007 - Spike-response model used in Carandini et al 2007.

Description:

      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 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 subthresold 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.

Parameters:

      The following parameters can be set in the status dictionary.

      tau_epsp	 double	- Membrane time constant in ms.
      tau_reset	 double	- Refractory time constant in ms.
      U_epsp	 double	- Maximum amplitude of the EPSP. Normalized.
      U_reset	 double	- Reset value of the membrane potential. Normalized.
      U_noise	 double	- Noise scale. Normalized.
      noise   vector- Noise signal.

Receives:

    SpikeEvent, DataLoggingRequest

Sends:

    SpikeEvent

Remarks:

      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.

References:

      [1] Carandini M, Horton JC, Sincich LC (2007) Thalamic filtering of retinal
      spike trains by postsynaptic summation. J Vis 7(14):20,1-11.
      [2] Rotter S & Diesmann M (1999) Exact simulation of time-invariant linear
      systems with applications to neuronal modeling. Biologial Cybernetics
      81:381-402.

Author:

    Thomas Heiberg, Birgit Kriener

FirstVersion:

    May 2012

Source:

    /opt/conda/conda-bld/nest_1512418852064/work/nest-2.12.0/models/iaf_chs_2007.h

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