# Command: iaf_psc_alpha_presc

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Name:

    iaf_psc_alpha_presc - Leaky integrate-and-fire neuron
      with alpha-shape postsynaptic currents; prescient implementation.

Description:

      iaf_psc_alpha_presc is the "prescient" implementation of the leaky
      integrate-and-fire model neuron with alpha-shaped postsynaptic
      currents in the sense of [1].

      PSCs are normalized to an amplitude of 1pA.

      The prescient implementation predicts the effect of spikes arriving
      during a time step by exactly integrating their effect from the
      precise time of spike arrival to the end of the time step.  This is
      exact if the neuron was not refractory at the beginning of the
      interval and remains subthreshold throughout the
      interval. Subthreshold dynamics are integrated using exact integration
      between events [2].

Parameters:

      The following parameters can be set in the status dictionary.

      V_m	 double	- Membrane potential in mV
      E_L	 double	- Resting membrane potential in mV.
      V_min	 double	- Absolute lower value for the membrane potential.
      C_m	 double	- Capacity of the membrane in pF
      tau_m	 double	- Membrane time constant in ms.
      t_ref	 double	- Duration of refractory period in ms.
      V_th	 double	- Spike threshold in mV.
      V_reset	 double	- Reset potential of the membrane in mV.
      tau_syn	 double	- Rise time of the synaptic alpha function in ms.
      I_e	 double	- Constant external input current in pA.
      Interpol_Order  int	- Interpolation order for spike time:
      0-none, 1-linear, 2-quadratic, 3-cubic

Receives:

    SpikeEvent, CurrentEvent, DataLoggingRequest

Sends:

    SpikeEvent

Remarks:

      If tau_m is very close to tau_syn_ex or tau_syn_in, the model
      will numerically behave as if tau_m is equal to tau_syn_ex or
      tau_syn_in, respectively, to avoid numerical instabilities.
      For details, please see IAF_Neruons_Singularity.ipynb in
      the NEST source code (docs/model_details).

References:

      [1] Morrison A, Straube S, Plesser H E, & Diesmann M (2006) Exact Subthreshold
      Integration with Continuous Spike Times in Discrete Time Neural Network
      Simulations. To appear in Neural Computation.
      [2] Rotter S & Diesmann M (1999) Exact simulation of time-invariant linear
      systems with applications to neuronal modeling. Biologial Cybernetics
      81:381-402.

Author:

    Diesmann, Eppler, Morrison, Plesser, Straube

SeeAlso:

- iaf_psc_alpha
- iaf_psc_alpha_canon
- iaf_psc_delta_canon

Source:

    /opt/conda/conda-bld/nest_1510212701067/work/nest-2.14.0/precise/iaf_psc_alpha_presc.h

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