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

• URL: http://arxiv.org/abs/2201.07602v1
• Date: Wed, 5 Jan 2022 05:51:18 GMT
• Title: Including STDP to eligibility propagation in multi-layer recurrent spiking neural networks
• Authors: Werner van der Veen
• Abstract summary: Spiking neural networks (SNNs) in neuromorphic systems are more energy efficient compared to deep learning-based methods. There is no clear competitive learning algorithm for training such SNNs. E-prop offers an efficient and biologically plausible way to train competitive recurrent SNNs in low-power neuromorphic hardware.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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