E-prop plasticity examples

Eligibility propagation (e-prop) [1] is a three-factor learning rule for spiking neural networks that approximates backpropagation through time. The original TensorFlow implementation of e-prop was demonstrated, among others, on a supervised regression task to generate temporal patterns and a supervised classification task to accumulate evidence [2]. Here, you find tutorials on how to reproduce these two tasks as well as two more advanced regression tasks using the NEST implementation of e-prop [3] and how to visualize the simulation recordings.

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]

https://github.com/IGITUGraz/eligibility_propagation/blob/master/Figure_3_and_S7_e_prop_tutorials/

[3]

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)

Tutorial on learning to generate sine waves with e-prop

Tutorial on learning to generate sine waves with e-prop

Tutorial on learning to generate an infinite loop with e-prop

Tutorial on learning to generate an infinite loop with e-prop

Tutorial on learning to generate handwritten text with e-prop

Tutorial on learning to generate handwritten text with e-prop

Tutorial on learning to accumulate evidence with e-prop

Tutorial on learning to accumulate evidence with e-prop