Enhance e-prop plasticity with biologically inspired features

[JesusEV]

This PR extends the previous efforts (PR #2867 "Implement e-prop plasticity") in porting the eligibility propagation (e-prop) plasticity mechanism by Bellec et al. (2020) from TensorFlow to NEST by introducing several novel, bio-inspired enhancements to e-prop.

Changes

• Renamed Files: Files from PR Implement e-prop plasticity #2867 now include the suffix _bsshslm_2020, aligning with NEST's naming convention that reflects the first letters of the authors' last names and the publication year.
• New Neuron Models:
  • eprop_iaf
  • eprop_iaf_psc_delta
  • eprop_iaf_adapt
  • eprop_readout
• New Synapse Model: eprop_synapse
• New Connection Model: eprop_learning_signal_connection
• Refactored Node: eprop_archiving_node with added support for new models.
• New Unit Test: test_eprop_plasticity.py
• New Example Scripts:
  • eprop_supervised_regression_sine-waves.py
  • eprop_supervised_classification_neuromorphic_mnist.py
  • eprop_supervised_classification_evidence-accumulation.py

A manuscript by Korcsak-Gorzo, Stapmanns, and Espinoza Valverde et al. detailing these enhancements is in preparation.

References

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. DOI

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

Co-authored-by: Agnes Korcsak-Gorzo [email protected]

[heplesser]

@akorgor @JesusEV Could you merge the newest master? That should solve the Pylint errors about "too many positional arguments".