iaf_cond_alpha – Simple conductance based leaky integrate-and-fire neuron model
===============================================================================

Description
+++++++++++

iaf_cond_alpha is an implementation of a spiking neuron using IAF dynamics with
conductance-based synapses. Incoming spike events induce a postsynaptic change
of conductance modelled by an alpha function. The alpha function
is normalized such that an event of weight 1.0 results in a peak current of 1 nS
at :math:`t = \tau_{syn}`.

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


=========== ======= ===========================================================
 V_m        mV      Membrane potential
 E_L        mV      Leak reversal potential
 C_m        pF      Capacity of the membrane
 t_ref      ms      Duration of refractory period
 V_th       mV      Spike threshold
 V_reset    mV      Reset potential of the membrane
 E_ex       mV      Excitatory reversal potential
 E_in       mV      Inhibitory reversal potential
 g_L        nS      Leak conductance
 tau_syn_ex ms      Rise time of the excitatory synaptic alpha function
 tau_syn_in ms      Rise time of the inhibitory synaptic alpha function
 I_e        pA      Constant input current
=========== ======= ===========================================================


Sends
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SpikeEvent

Receives
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SpikeEvent, CurrentEvent, DataLoggingRequest

Remarks:

 @note Per 2009-04-17, this class has been revised to our newest
        insights into class design. Please use THIS CLASS as a reference
        when designing your own models with nonlinear dynamics.
        One weakness of this class is that it distinguishes between
        inputs to the two synapses by the sign of the synaptic weight.
        It would be better to use receptor_types, cf iaf_cond_alpha_mc.


References
++++++++++

.. [1] Meffin H, Burkitt AN, Grayden DB (2004). An analytical
       model for the large, fluctuating synaptic conductance state typical of
       neocortical neurons in vivo. Journal of Computational Neuroscience,
       16:159-175.
       DOI: https://doi.org/10.1023/B:JCNS.0000014108.03012.81
.. [2] Bernander O, Douglas RJ, Martin KAC, Koch C (1991). Synaptic background
       activity influences spatiotemporal integration in single pyramidal
       cells.  Proceedings of the National Academy of Science USA,
       88(24):11569-11573.
       DOI: https://doi.org/10.1073/pnas.88.24.11569
.. [3] Kuhn A, Rotter S (2004) Neuronal integration of synaptic input in
       the fluctuation- driven regime. Journal of Neuroscience,
       24(10):2345-2356
       DOI: https://doi.org/10.1523/JNEUROSCI.3349-03.2004

See also
++++++++
:doc:`Neuron <index_neuron>`, :doc:`Integrate-And-Fire <index_integrate-and-fire>`, :doc:`Conductance-Based <index_conductance-based>`