Training Spiking Neural Networks Using Lessons From Deep Learning-Reference-Cited by-同舟云学术

Training Spiking Neural Networks Using Lessons From Deep Learning

Published:2023-09 Issue:9 Volume:111 Page:1016-1054
ISSN:0018-9219
Container-title:Proceedings of the IEEE
language:
Short-container-title:Proc. IEEE

Author:

Eshraghian Jason K.¹^ORCID,Ward Max²,Neftci Emre O.³^ORCID,Wang Xinxin¹^ORCID,Lenz Gregor⁴^ORCID,Dwivedi Girish⁵^ORCID,Bennamoun Mohammed⁶^ORCID,Jeong Doo Seok⁷^ORCID,Lu Wei D.¹^ORCID

Affiliation:

1. Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA

2. Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA

3. Department of Computer Science and the Department of Cognitive Sciences, University of California at Irvine (UC Irvine), Irvine, CA, USA

4. SynSense AG, Zürich, Switzerland

5. School of Medicine, The University of Western Australia, Crawley, WA, Australia

6. Department of Computer Science and Software Engineering, The University of Western Australia, Crawley, WA, Australia

7. Division of Materials Science and Engineering, Hanyang University, Seoul, South Korea

Funder

National Science Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering

Link

https://ieeexplore.ieee.org/ielam/5/10251702/10242251-aam.pdf

Reference237 articles.

1. Neural heterogeneity promotes robust learning

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3. Memory consolidation and improvement by synaptic tagging and capture in recurrent neural networks

4. Feedforward Categorization on AER Motion Events Using Cortex-Like Features in a Spiking Neural Network

5. Receptive fields, binocular interaction and functional architecture in the cat's visual cortex

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