Power law decay of stored pattern stability in sparse Hopfield neural networks

Fei Fang,Zhou Yang,Sheng-Jun Wang

Communications in Theoretical Physics ›› 2021, Vol. 73 ›› Issue (2) : 25601.

PDF(974 KB)
Welcome to visit Communications in Theoretical Physics,
PDF(974 KB)
Communications in Theoretical Physics ›› 2021, Vol. 73 ›› Issue (2) : 25601. DOI: 10.1088/1572-9494/abcfb0
Statistical Physics, Soft Matter and Biophysics

Power law decay of stored pattern stability in sparse Hopfield neural networks

    {{javascript:window.custom_author_en_index=0;}}
  • {{article.zuoZhe_EN}}
Author information +
History +

HeighLight

{{article.keyPoints_en}}

Abstract

{{article.zhaiyao_en}}

Key words

QR code of this article

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations
{{article.zuoZheEn_L}}. {{article.title_en}}[J]. {{journal.qiKanMingCheng_EN}}, 2021, 73(2): 25601 https://doi.org/10.1088/1572-9494/abcfb0

References

List( Publishing order | Descend order by publishing year | Descend order by cited within ) Chart analysis

References

{{article.reference}}

Funding

RIGHTS & PERMISSIONS

{{article.copyrightStatement_en}}
{{article.copyrightLicense_en}}
PDF(974 KB)

Accesses

Citation

Detail
Sections
Recommended
Copyright © Editorial Office of Communications in Theoretical Physics
Address: Institute of Theoretical Physics, Chinese Academy of Sciences,
Beijing 100190, China　
Tel: 86-10-62551495; 62541813; 62550630　
Fax: 86-10-62551495　
E-mail：ctp@itp.ac.cn
Website: https://ctp.itp.ac.cn　https://iopscience.org/ctp
Powered by Beijing Magtech Co., Ltd.

/

〈 〉