论文标题
包括在多层复发峰值神经网络中的STDP到资格传播
Including STDP to eligibility propagation in multi-layer recurrent spiking neural networks
论文作者
论文摘要
与基于深度学习的方法相比，神经形态系统中的尖峰神经网络（SNN）更节能，但是培训此类SNN的竞争性学习算法没有明确的竞争学习算法。资格传播（E-Prop）提供了一种有效且具有生物学上合理的方式，可以在低功率神经形态硬件中训练竞争性复发性SNN。在本报告中，E-Prop在语音分类任务上的先前表现得到了复制，并分析了包括STDP的行为的影响。包括ALIF神经元模型的STDP改善了分类性能，但对于Izhikevich E-Prop Neuron并非如此。最后，发现在单层复发中实现的E-Prop始终优于多层变体。
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.
