eprop_learning_signal_connection_bsshslm_2020 – Synapse model transmitting feedback learning signals for e-prop plasticity ========================================================================================================================== Description +++++++++++ ``eprop_learning_signal_connection_bsshslm_2020`` is an implementation of a feedback connector from ``eprop_readout_bsshslm_2020`` readout neurons to ``eprop_iaf_bsshslm_2020`` or ``eprop_iaf_adapt_bsshslm_2020`` 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]_. 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. 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_synapse_bsshslm_2020<../models/eprop_synapse_bsshslm_2020/>` Details on the event-based NEST implementation of e-prop can be found in [2]_. Parameters ++++++++++ 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 +++++++++++ The following variables can be recorded: - synaptic weight ``weight`` Usage +++++ 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 +++++++++ LearningSignalConnectionEvent References ++++++++++ .. [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 ++++++++ :doc:`Synapse `, :doc:`E-Prop Plasticity ` Examples using this model ++++++++++++++++++++++++++ .. listexamples:: eprop_learning_signal_connection_bsshslm_2020