Enhancing temporal learning in recurrent spiking networks for neuromorphic applications

Training Recurrent Spiking Neural Networks (RSNNs) with binary spikes for tasks of extended time scales presents a challenge due to the amplified vanishing gradient problem during back propagation through time. This paper introduces three crucial elements that significantly enhance the memory and ca...

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書誌詳細

主要な著者:	Ismael Balafrej, Soufiyan Bahadi, Jean Rouat, Fabien Alibart
フォーマット:	Artigo
言語:	Inglês
出版事項:	IOP Publishing 2025-01-01
シリーズ:	Neuromorphic Computing and Engineering
主題:	spiking neural network synaptic delays surrogate gradient descent neural dynamics
オンライン･アクセス:	https://doi.org/10.1088/2634-4386/add293
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