Progressive Tandem Learning for Pattern Recognition With Deep Spiking Neural Networks

Spiking neural networks (SNNs) have shown clear advantages over traditional artificial neural networks (ANNs) for low latency and high computational efficiency, due to their event-driven nature and sparse communication. However, the training of deep SNNs is not straightforward. In this paper, we pro...

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Bibliographic Details
Published in:	IEEE transactions on pattern analysis and machine intelligence Vol. 44; no. 11; pp. 7824 - 7840
Main Authors:	Wu, Jibin, Xu, Chenglin, Han, Xiao, Zhou, Daquan, Zhang, Malu, Li, Haizhou, Tan, Kay Chen
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.11.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	ANN-to-SNN conversion Artificial neural networks Biological neural networks Conversion Deep spiking neural network efficient neuromorphic inference Electronic devices Encoding Image classification Image reconstruction large-scale object recognition Learning Learning systems Network latency Neural networks Neurons Object recognition Pattern recognition Speech recognition speech separation spike-based learning Spiking Task analysis Training
ISSN:	0162-8828, 1939-3539, 2160-9292, 1939-3539
Online Access:	Get full text
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