Including STDP to eligibility propagation in multi-layer recurrent spiking neural networks

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 singlelayer recurrent SNN consistently outperforms a multi-layer variant.