tsodyks2_synapse – Synapse type with short term plasticity
==========================================================

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

This synapse model implements synaptic short-term depression and short-term
facilitation according to [1]_ and [2]_. It solves Eq (2) from [1]_ and
modulates U according to eq. (2) of [2]_.

This connection merely scales the synaptic weight, based on the spike history
and the parameters of the kinetic model. Thus, it is suitable for all types
of synaptic dynamics, that is current or conductance based.

The parameter ``A_se`` from the publications is represented by the
synaptic weight. The variable x in the synapse properties is the
factor that scales the synaptic weight.

.. warning::

   This synaptic plasticity rule does not take
   :ref:`precise spike timing <sim_precise_spike_times>` into
   account. When calculating the weight update, the precise spike time part
   of the timestamp is ignored.

See also [3]_.

Under identical conditions, the tsodyks2_synapse produces slightly
lower peak amplitudes than the tsodyks_synapse. However, the
qualitative behavior is identical. The script
:doc:`../auto_examples/evaluate_tsodyks2_synapse` compares the two
synapse models.

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

========  ======  ========================================================
 U        real    Parameter determining the increase in u with each spike
                  (U1) [0,1], default=0.5
 u        real    The probability of release (U_se) [0,1],
                  default=0.5
 x        real    Current scaling factor of the weight, default=U
 tau_fac  ms      Time constant for facilitation, default = 0(off)
 tau_rec  ms      Time constant for depression, default = 800ms
========  ======  ========================================================

References
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.. [1] Tsodyks MV,  Markram H (1997). The neural code between neocortical
       pyramidal neurons depends on neurotransmitter release probability.
       PNAS, 94(2):719-23.
       DOI: https://doi.org/10.1073/pnas.94.2.719
.. [2] Fuhrman, G, Segev I, Markram H, Tsodyks MV (2002). Coding of
       temporal information by activity-dependent synapses. Journal of
       Neurophysiology, 87(1):140-8.
       DOI: https://doi.org/10.1152/jn.00258.2001
.. [3] Maass W, Markram H (2002). Synapses as dynamic memory buffers.
       Neural Networks, 15(2):155-61.
       DOI: https://doi.org/10.1016/S0893-6080(01)00144-7

Transmits
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SpikeEvent

See also
++++++++
:doc:`Synapse <index_synapse>`, :doc:`Short-Term Plasticity <index_short-term plasticity>`