  Spiking neural networks (SNNs) in neuromorphic systems are more energy
efficient compared to deep learning-based methods, but there is no clear
competitive learning algorithm for training such SNNs. Eligibility propagation
(e-prop) offers an efficient and biologically plausible way to train
competitive recurrent SNNs in low-power neuromorphic hardware. In this report,
previous performance of e-prop on a speech classification task is reproduced,
and the effects of including STDP-like behavior are analyzed. Including STDP to
the ALIF neuron model improves the classification performance, but this is not
the case for the Izhikevich e-prop neuron. Finally, it was found that e-prop
implemented in a single-layer recurrent SNN consistently outperforms a
multi-layer variant.
