Enhance e-prop plasticity with biologically inspired features #3207
Conversation
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
* Updates the file names of tutorials that utilize the bsshslm 2020 models to ensure consistency with recent naming conventions
Increase default cutoff value
Co-authored-by: JesusEV <43375826+JesusEV@users.noreply.github.com>
Co-authored-by: JesusEV <43375826+JesusEV@users.noreply.github.com>
Co-authored-by: JesusEV <43375826+JesusEV@users.noreply.github.com>
Co-authored-by: JesusEV <43375826+JesusEV@users.noreply.github.com>
Co-authored-by: JesusEV <43375826+JesusEV@users.noreply.github.com>
Co-authored-by: JesusEV <43375826+JesusEV@users.noreply.github.com>
Co-authored-by: JesusEV <43375826+JesusEV@users.noreply.github.com>
Co-authored-by: JesusEV <43375826+JesusEV@users.noreply.github.com>
Co-authored-by: JesusEV <43375826+JesusEV@users.noreply.github.com>
Co-authored-by: JesusEV <43375826+JesusEV@users.noreply.github.com>
Co-authored-by: JesusEV <43375826+JesusEV@users.noreply.github.com>
Co-authored-by: JesusEV <43375826+JesusEV@users.noreply.github.com>
Co-authored-by: JesusEV <43375826+JesusEV@users.noreply.github.com>
Co-authored-by: JesusEV <43375826+JesusEV@users.noreply.github.com>
Co-authored-by: JesusEV <43375826+JesusEV@users.noreply.github.com>
Co-authored-by: JesusEV <43375826+JesusEV@users.noreply.github.com>
akorgor
force-pushed
the
eprop_bio_feature
branch
from
154d9fd
to
fdd2533
Compare
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
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
_bsshslm_2020, aligning with NEST's naming convention that reflects the first letters of the authors' last names and the publication year.eprop_iafeprop_iaf_psc_deltaeprop_iaf_adapteprop_readouteprop_synapseeprop_learning_signal_connectioneprop_archiving_nodewith added support for new models.test_eprop_plasticity.pyeprop_supervised_regression_sine-waves.pyeprop_supervised_classification_neuromorphic_mnist.pyeprop_supervised_classification_evidence-accumulation.pyA 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 40828647+akorgor@users.noreply.github.com