eprop_synapse_bsshslm_2020 – Synapse type for e-prop plasticity
===============================================================

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

``eprop_synapse_bsshslm_2020`` is an implementation of a connector model to create synapses between postsynaptic
neurons :math:`j` and presynaptic neurons :math:`i` for eligibility propagation (e-prop) plasticity.

E-prop plasticity was originally introduced and implemented in TensorFlow in [1]_.

The suffix ``_bsshslm_2020`` follows the NEST convention to indicate in the
model name the paper that introduced it by the first letter of the authors' last
names and the publication year.

The e-prop synapse collects the presynaptic spikes needed for calculating the
weight update. When it is time to update, it triggers the calculation of the
gradient which is specific to the post-synaptic neuron and is thus defined there.

Eventually, it optimizes the weight with the specified optimizer.

E-prop synapses require archiving of continuous quantities. Therefore e-prop
synapses can only be connected to neuron models that are capable of
archiving. So far, compatible models are ``eprop_iaf_bsshslm_2020``,
``eprop_iaf_adapt_bsshslm_2020``, and ``eprop_readout_bsshslm_2020``.

For more information on e-prop plasticity, see the documentation on the other e-prop models:

 * :doc:`eprop_iaf_bsshslm_2020<../models/eprop_iaf_bsshslm_2020/>`
 * :doc:`eprop_iaf_adapt_bsshslm_2020<../models/eprop_iaf_adapt_bsshslm_2020/>`
 * :doc:`eprop_readout_bsshslm_2020<../models/eprop_readout_bsshslm_2020/>`
 * :doc:`eprop_learning_signal_connection_bsshslm_2020<../models/eprop_learning_signal_connection_bsshslm_2020/>`

For more information on the optimizers, see the documentation of the weight optimizer:

 * :doc:`weight_optimizer<../models/weight_optimizer/>`

Details on the event-based NEST implementation of e-prop can be found in [2]_.

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

================ ======= =============== ======= ======================================================
**Common synapse parameters**
-------------------------------------------------------------------------------------------------------
Parameter        Unit    Math equivalent Default Description
================ ======= =============== ======= ======================================================
average_gradient Boolean                   False If True, average the gradient over the learning window
optimizer                                     {} Dictionary of optimizer parameters
================ ======= =============== ======= ======================================================

============= ==== ========================= ======= =========================================================
**Individual synapse parameters**
--------------------------------------------------------------------------------------------------------------
Parameter     Unit Math equivalent           Default Description
============= ==== ========================= ======= =========================================================
delay         ms   :math:`d_{ji}`                1.0 Dendritic delay
tau_m_readout ms   :math:`\tau_\text{m,out}`    10.0 Time constant for low-pass filtering of eligibility trace
weight        pA   :math:`W_{ji}`                1.0 Initial value of synaptic weight
============= ==== ========================= ======= =========================================================

Recordables
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The following variables can be recorded.

  - synaptic weight ``weight``

Usage
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This model can only be used in combination with the other e-prop models,
whereby the network architecture requires specific wiring, input, and output.
The usage is demonstrated in several
:doc:`supervised regression and classification tasks <../auto_examples/eprop_plasticity/index>`
reproducing among others the original proof-of-concept tasks in [1]_.

Transmits
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SpikeEvent, DSSpikeEvent

References
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.. [1] Bellec G, Scherr F, Subramoney F, Hajek E, Salaj D, Legenstein R,
       Maass W (2020). A solution to the learning dilemma for recurrent
       networks of spiking neurons. Nature Communications, 11:3625.
       https://doi.org/10.1038/s41467-020-17236-y

.. [2] Korcsak-Gorzo A, Stapmanns J, Espinoza Valverde JA, Dahmen D,
       van Albada SJ, Bolten M, Diesmann M. Event-based implementation of
       eligibility propagation (in preparation)

See also
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:doc:`Synapse <index_synapse>`, :doc:`E-Prop Plasticity <index_e-prop plasticity>`

Examples using this model
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.. listexamples:: eprop_synapse_bsshslm_2020