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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Autors principals:	Ismael Balafrej, Soufiyan Bahadi, Jean Rouat, Fabien Alibart
Format:	Artigo
Idioma:	Inglês
Publicat:	IOP Publishing 2025-01-01
Col·lecció:	Neuromorphic Computing and Engineering
Matèries:	spiking neural network synaptic delays surrogate gradient descent neural dynamics
Accés en línia:	https://doi.org/10.1088/2634-4386/add293
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