eprop_learning_signal_connection – Synapse model transmitting feedback learning signals for e-prop plasticity
=============================================================================================================

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

``eprop_learning_signal_connection`` is an implementation of a feedback connector from
``eprop_readout`` readout neurons to ``eprop_iaf`` or ``eprop_iaf_adapt``
recurrent neurons that transmits the learning signals :math:`L_j^t` for eligibility propagation (e-prop) plasticity and
has a static weight :math:`B_{jk}`.

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

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

 * :doc:`eprop_iaf<../models/eprop_iaf/>`
 * :doc:`eprop_iaf_adapt<../models/eprop_iaf_adapt/>`
 * :doc:`eprop_readout<../models/eprop_readout/>`
 * :doc:`eprop_synapse<../models/eprop_synapse/>`

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

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

========== ===== ================ ======= ===============
**Individual synapse parameters**
---------------------------------------------------------
Parameter  Unit  Math equivalent  Default Description
========== ===== ================ ======= ===============
``delay``  ms    :math:`d_{jk}`       1.0 Dendritic delay
``weight`` pA    :math:`B_{jk}`       1.0 Synaptic weight
========== ===== ================ ======= ===============

Recordables
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The following variables can be recorded. Note that since this connection lacks
a plasticity mechanism the weight does not evolve over time.

============== ==== =============== ============= ===============
**Synapse recordables**
-----------------------------------------------------------------
State variable Unit Math equivalent Initial value Description
============== ==== =============== ============= ===============
``weight``     pA   :math:`B_{jk}`            1.0 Synaptic weight
============== ==== =============== ============= ===============

Usage
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This model can only be used in combination with the other e-prop models
and 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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LearningSignalConnectionEvent

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, Plesser HE,
       Dahmen D, Bolten M, Van Albada SJ, Diesmann M. Event-based
       implementation of eligibility propagation (in preparation)

See also
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Examples using this model
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.. listexamples:: eprop_learning_signal_connection