SSTDP: Supervised Spike Timing Dependent Plasticity for Efficient Spiking Neural Network Training

Spiking Neural Networks (SNNs) are a pathway that could potentially empower low-power event-driven neuromorphic hardware due to their spatio-temporal information processing capability and high biological plausibility. Although SNNs are currently more efficient than artificial neural networks (ANNs),...

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Detalles Bibliográficos
Autores principales:	Fangxin Liu, Wenbo Zhao, Yongbiao Chen, Zongwu Wang, Tao Yang, Li Jiang
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	spiking neural network gradient descent backpropagation neuromorphic computing spike-time-dependent plasticity deep learning efficient training Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/f6a5d3a0ea4e4e48a939a0bc137d6667
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SSTDP: Supervised Spike Timing Dependent Plasticity for Efficient Spiking Neural Network Training

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