E-prop plasticity examples

Eligibility propagation (e-prop) [1] is a three-factor learning rule for spiking neural networks that approaches the performance of backpropagation through time (BPTT). 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.

The tutorials labeled “after Bellec et al. (2020)” use the original e-prop model [1], while the other tutorials use a version of e-prop that includes additional biological features as described in [3].

See below for a diagram that describes the relationships between the different models for e-prop.

Users interested in endowing an existing model with e-prop plasticity, may compare the .cpp and .h files of the iaf_psc_delta and eprop_iaf_psc_delta model. Parameters to run the eprop_iaf_psc_delta model are provided in eprop_supervised_regression_sine-waves.py.

e-prop model map

References

[1] (1,2)

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] (1,2)

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)

Tutorial on learning to generate sine waves with e-prop after Bellec et al. (2020)

Tutorial on learning to generate sine waves with e-prop after Bellec et al. (2020)

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 a lemniscate with e-prop after Bellec et al. (2020)

Tutorial on learning to generate a lemniscate with e-prop after Bellec et al. (2020)

Tutorial on learning to generate handwritten text with e-prop after Bellec et al. (2020)

Tutorial on learning to generate handwritten text with e-prop after Bellec et al. (2020)

Tutorial on learning to accumulate evidence with e-prop after Bellec et al. (2020)

Tutorial on learning to accumulate evidence with e-prop after Bellec et al. (2020)

Tutorial on learning N-MNIST classification with e-prop

Tutorial on learning N-MNIST classification with e-prop