stdp_synapse_hom – Synapse type for spike-timing dependent plasticity using homogeneous parameters
==================================================================================================

Description
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``stdp_synapse_hom`` is a connector to create synapses with spike time
dependent plasticity (as defined in [1]_). Here the weight dependence
exponent can be set separately for potentiation and depression.

 Parameters controlling plasticity are identical for all synapses of the
 model, reducing the memory required per synapse considerably.

Examples:

* multiplicative STDP [2]_  mu_plus = mu_minus = 1.0
* additive STDP       [3]_  mu_plus = mu_minus = 0.0
* Guetig STDP         [1]_  mu_plus = mu_minus = [0.0,1.0]
* van Rossum STDP     [4]_  mu_plus = 0.0 mu_minus = 1.0

.. 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.

Parameters
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========= =======  ======================================================
 tau_plus ms       Time constant of STDP window, potentiation
                   (tau_minus defined in postsynaptic neuron)
 lambda   real     Step size
 alpha    real     Asymmetry parameter (scales depressing increments as
                   alpha*lambda)
 mu_plus  real     Weight dependence exponent, potentiation
 mu_minus real     Weight dependence exponent, depression
 Wmax     real     Maximum allowed weight
========= =======  ======================================================

The parameters are common to all synapses of the model and must be set using
SetDefaults on the synapse model.

Transmits
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SpikeEvent

References
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.. [1] Guetig et al. (2003). Learning input correlations through nonlinear
       temporally asymmetric hebbian plasticity. Journal of Neuroscience,
       23:3697-3714 DOI: https://doi.org/10.1523/JNEUROSCI.23-09-03697.2003
.. [2] Rubin J, Lee D, Sompolinsky H (2001). Equilibrium
       properties of temporally asymmetric Hebbian plasticity. Physical Review
       Letters, 86:364-367. DOI: https://doi.org/10.1103/PhysRevLett.86.364
.. [3] Song S, Miller KD, Abbott LF (2000). Competitive Hebbian learning
       through spike-timing-dependent synaptic plasticity. Nature Neuroscience
       3(9):919-926.
       DOI: https://doi.org/10.1038/78829
.. [4] van Rossum MCW, Bi G-Q, Turrigiano GG (2000). Stable Hebbian learning
       from spike timing-dependent plasticity. Journal of Neuroscience,
       20(23):8812-8821.
       DOI: https://doi.org/10.1523/JNEUROSCI.20-23-08812.2000

See also
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:doc:`Synapse <index_synapse>`, :doc:`Spike-Timing-Dependent Plasticity <index_spike-timing-dependent plasticity>`