Dendritic Convolutional Neural Network

Rong Long Wang; Zhenyu Lei; Zhiming Zhang; Shangce Gao

doi:10.1002/tee.23513

Dendritic Convolutional Neural Network

Rong Long Wang, Zhenyu Lei, Zhiming Zhang, Shangce Gao^*

^*この論文の責任著者

工学科　知能情報工学コース

研究成果: ジャーナルへの寄稿 › Letter › 査読

20 被引用数 (Scopus)

抄録

Convolutional neural network (CNN), as one of the mainstream deep learning models, has achieved great success in image recognition. All neurons used in CNN are based on the McCulloch-Pitts model, which is over-simplified. To further improve CNN's learning capacity, this paper proposes a novel dendritic CNN (DCNN), which considers the nonlinear information processing functions of dendrites in a single neuron. The superiority of DCNN is confirmed based on four widely used image recognition tasks.

本文言語	英語
ページ（範囲）	302-304
ページ数	3
ジャーナル	IEEJ Transactions on Electrical and Electronic Engineering
巻	17
号	2
DOI	https://doi.org/10.1002/tee.23513
出版ステータス	出版済み - 2022/02

ASJC Scopus 主題領域

電子工学および電気工学

文献へのアクセス

10.1002/tee.23513

引用スタイル

@article{21bb996f0ef04193bbe1b4e95f3b3e28,

title = "Dendritic Convolutional Neural Network",

abstract = "Convolutional neural network (CNN), as one of the mainstream deep learning models, has achieved great success in image recognition. All neurons used in CNN are based on the McCulloch-Pitts model, which is over-simplified. To further improve CNN's learning capacity, this paper proposes a novel dendritic CNN (DCNN), which considers the nonlinear information processing functions of dendrites in a single neuron. The superiority of DCNN is confirmed based on four widely used image recognition tasks.",

author = "Wang, {Rong Long} and Zhenyu Lei and Zhiming Zhang and Shangce Gao",

note = "Publisher Copyright: {\textcopyright} 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.",

year = "2022",

month = feb,

doi = "10.1002/tee.23513",

language = "英語",

volume = "17",

pages = "302--304",

journal = "IEEJ Transactions on Electrical and Electronic Engineering",

issn = "1931-4973",

publisher = "John Wiley & Sons Inc.",

number = "2",

}

TY - JOUR

T1 - Dendritic Convolutional Neural Network

AU - Wang, Rong Long

AU - Lei, Zhenyu

AU - Zhang, Zhiming

AU - Gao, Shangce

PY - 2022/2

Y1 - 2022/2

N2 - Convolutional neural network (CNN), as one of the mainstream deep learning models, has achieved great success in image recognition. All neurons used in CNN are based on the McCulloch-Pitts model, which is over-simplified. To further improve CNN's learning capacity, this paper proposes a novel dendritic CNN (DCNN), which considers the nonlinear information processing functions of dendrites in a single neuron. The superiority of DCNN is confirmed based on four widely used image recognition tasks.

AB - Convolutional neural network (CNN), as one of the mainstream deep learning models, has achieved great success in image recognition. All neurons used in CNN are based on the McCulloch-Pitts model, which is over-simplified. To further improve CNN's learning capacity, this paper proposes a novel dendritic CNN (DCNN), which considers the nonlinear information processing functions of dendrites in a single neuron. The superiority of DCNN is confirmed based on four widely used image recognition tasks.

UR - http://www.scopus.com/inward/record.url?scp=85117252497&partnerID=8YFLogxK

U2 - 10.1002/tee.23513

DO - 10.1002/tee.23513

M3 - Letter

AN - SCOPUS:85117252497

SN - 1931-4973

VL - 17

SP - 302

EP - 304

JO - IEEJ Transactions on Electrical and Electronic Engineering

JF - IEEJ Transactions on Electrical and Electronic Engineering

IS - 2

ER -

Dendritic Convolutional Neural Network

抄録

ASJC Scopus 主題領域

文献へのアクセス

フィンガープリント

引用スタイル