A biologically plausible supervised learning method for spiking neural networks using the symmetric STDP rule

Yunzhe Hao; Xuhui Huang; Meng Dong; Bo Xu

doi:10.1016/j.neunet.2019.09.007

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A biologically plausible supervised learning method for spiking neural networks using the symmetric STDP rule

Yunzhe Hao Xuhui Huang Meng Dong Bo Xu

Year: 2019 Journal: Neural Networks Vol: 121 Pages: 387-395 Publisher: Elsevier BV

DOI: 10.1016/j.neunet.2019.09.007

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Keywords:

MNIST database Spiking neural network Computer science Artificial intelligence Learning rule Machine learning Artificial neural network Embedding Robustness (evolution) Neuromorphic engineering Pattern recognition (psychology)

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Advanced Memory and Neural Computing

Physical Sciences → Engineering → Electrical and Electronic Engineering

Neural dynamics and brain function

Life Sciences → Neuroscience → Cognitive Neuroscience

Neural Networks and Reservoir Computing

Physical Sciences → Computer Science → Artificial Intelligence

A biologically plausible supervised learning method for spiking neural networks using the symmetric STDP rule

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Citation History

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