# Command: iaf_cond_exp

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Name:

    iaf_cond_exp - Simple conductance based leaky integrate-and-fire neuron
      model.

Description:

      iaf_cond_exp is an implementation of a spiking neuron using IAF dynamics with
      conductance-based synapses. Incoming spike events induce a post-synaptic change
      of conductance modelled by an exponential function. The exponential function
      is normalised such that an event of weight 1.0 results in a peak conductance of
      1 nS.

Parameters:

      The following parameters can be set in the status dictionary.

      V_m	 double	- Membrane potential in mV
      E_L	 double	- Leak reversal potential in mV.
      C_m	 double	- Capacity of the membrane in pF
      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.
      E_ex	 double	- Excitatory reversal potential in mV.
      E_in	 double	- Inhibitory reversal potential in mV.
      g_L	 double	- Leak conductance in nS;
      tau_syn_ex double	- Time constant of the excitatory synaptic exponential
      function in ms.
      tau_syn_in double	- Time constant of the inhibitory synaptic exponential
      function in ms.
      I_e	 double	- Constant external input current in pA.

Require:

    HAVE_GSL

Receives:

    SpikeEvent, CurrentEvent, DataLoggingRequest

Sends:

    SpikeEvent

References:

      Meffin, H., Burkitt, A. N., & Grayden, D. B. (2004). An analytical
      model for the large, fluctuating synaptic conductance state typical of
      neocortical neurons in vivo. J.  Comput. Neurosci., 16, 159-175.

Author:

    Sven Schrader

SeeAlso:

- iaf_psc_delta
- iaf_psc_exp
- iaf_cond_exp

Source:

    /opt/conda/conda-bld/nest_1524088722951/work/nest-2.14.0/models/iaf_cond_exp.h

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