# Command: gif_cond_exp

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Name:

    gif_cond_exp - Conductance-based generalized integrate-and-fire neuron
      model according to Mensi et al. (2012) and Pozzorini et al. (2015).

Description:

      gif_psc_exp is the generalized integrate-and-fire neuron according to
      Mensi et al. (2012) and Pozzorini et al. (2015), with post-synaptic
      conductances in the form of truncated exponentials.

      This model features both an adaptation current and a dynamic threshold for
      spike-frequency adaptation. The membrane potential (V) is described by the
      differential equation:

      C*dV(t)/dt = -g_L*(V(t)-E_L)	- eta_1(t)	- eta_2(t)	- ...	- eta_n(t) + I(t)

      where each eta_i is a spike-triggered current (stc), and the neuron model can
      have arbitrary number of them.
      Dynamic of each eta_i is described by:

      tau_eta_i*d{eta_i}/dt = -eta_i

      and in case of spike emission, its value increased by a constant (which can be
      positive or negative):

      eta_i = eta_i + q_eta_i  (in case of spike emission).

      Neuron produces spikes STOCHASTICALLY according to a point process with the
      firing intensity:

      lambda(t) = lambda_0 * exp[ (V(t)-V_T(t)) / Delta_V ]

      where V_T(t) is a time-dependent firing threshold:

      V_T(t) = V_T_star + gamma_1(t) + gamma_2(t) + ... + gamma_m(t)

      where gamma_i is a kernel of spike-frequency adaptation (sfa), and the neuron
      model can have arbitrary number of them.
      Dynamic of each gamma_i is described by:

      tau_gamma_i*d{gamma_i}/dt = -gamma_i

      and in case of spike emission, its value increased by a constant (which can be
      positive or negative):

      gamma_i = gamma_i + q_gamma_i  (in case of spike emission).

      Note that in the current implementation of the model (as described in [1] and
      [2]) the values of eta_i and gamma_i are affected immediately after spike
      emission. However, GIF toolbox (http://wiki.epfl.ch/giftoolbox) which fits
      the model using experimental data, requires a different set of eta_i and
      gamma_i. It applies the jump of eta_i and gamma_i after the refractory period.
      One can easily convert between q_eta/gamma of these two approaches:
      q_eta_giftoolbox = q_eta_NEST * (1	- exp( -tau_ref / tau_eta ))
      The same formula applies for q_gamma.

      The shape of synaptic conductance is exponential.

Parameters:

      C_m	 double	- Capacity of the membrane in pF
      t_ref	 double	- Duration of refractory period in ms.
      V_reset   double	- Reset value after a spike in mV.
      E_L	 double	- Leak reversal potential in mV.
      g_L	 double	- Leak conductance in nS.
      I_e	 double	- Constant external input current in pA.

      Spike adaptation and firing intensity parameters:
      q_stc	 vector of double	- Values added to spike-triggered currents (stc)
      after each spike emission in nA.
      tau_stc   vector of double	- Time constants of stc variables in ms.
      q_sfa	 vector of double	- Values added to spike-frequency adaptation
      (sfa) after each spike emission in mV.
      tau_sfa   vector of double	- Time constants of sfa variables in ms.
      Delta_V   double	- Stochasticity level in mV.
      lambda_0   double	- Stochastic intensity at firing threshold V_T in 1/s.
      V_T_star   double	- Base threshold in mV

      Synaptic parameters
      E_ex	 double	- Excitatory reversal potential in mV.
      tau_syn_ex double	- Decay time of excitatory synaptic conductance in ms.
      E_in	 double	- Inhibitory reversal potential in mV.
      tau_syn_in double	- Decay time of the inhibitory synaptic conductance in ms.

      Integration parameters
      gsl_error_tol  double	- This parameter controls the admissible error of the
      GSL integrator. Reduce it if NEST complains about
      numerical instabilities.

Require:

    HAVE_GSL

Receives:

    SpikeEvent, CurrentEvent, DataLoggingRequest

Sends:

    SpikeEvent

References:

      [1] Mensi S, Naud R, Pozzorini C, Avermann M, Petersen CC, Gerstner W (2012)
      Parameter extraction and classification of three cortical neuron types
      reveals two distinct adaptation mechanisms. J. Neurophysiol., 107(6),
      1756-1775.

      [2] Pozzorini C, Mensi S, Hagens O, Naud R, Koch C, Gerstner W (2015)
      Automated High-Throughput Characterization of Single Neurons by Means of
      Simplified Spiking Models. PLoS Comput. Biol., 11(6), e1004275.

Author:

    March 2016, Setareh

SeeAlso:

- pp_psc_delta
- gif_cond_exp_multisynapse
- gif_psc_exp
- gif_psc_exp_multisynapse

Source:

    /opt/conda/conda-bld/nest_1512421404550/work/nest-2.12.0/models/gif_cond_exp.h

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