# 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¶

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 handwritten text with e-prop

Tutorial on learning to accumulate evidence with e-prop