A Comparative Study on the Performance and Security Evaluation of Spiking Neural Networks-Reference-Cited by-同舟云学术

A Comparative Study on the Performance and Security Evaluation of Spiking Neural Networks

Published:2022 Issue: Volume:10 Page:117572-117581
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Li Yanjie¹^ORCID,Cui Xiaoxin²^ORCID,Zhou Yihao¹,Li Ying¹^ORCID

Affiliation:

1. Institute of Microelectronics, Chinese Academy of Sciences, Beijing, China

2. School of Integrated Circuits, Peking University, Beijing, China

Funder

Science and Technology Innovation 2030-Major Project through the Brain Science and Brain-Like Intelligence Technology

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/9668973/09940625.pdf?arnumber=9940625

Reference22 articles.

1. Deep spiking networks;o’connor;arXiv 1602 08323,2016

2. Going Deeper in Spiking Neural Networks: VGG and Residual Architectures

3. Enabling deep spiking neural networks with hybrid conversion and spike timing dependent backpropagation;rathi;Proc 8th Int Conf Learn Represent (ICLR),2020

4. Unsupervised learning of digit recognition using spike-timing-dependent plasticity

5. Very deep convolutional networks for large-scale image recognition;simonyan;arXiv 1409 1556,2014

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