Memristive circuit of emotion with negative feedback based on three primary color model

IF 6 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Juntao Han , Gang Liu , Zhang Zhang
{"title":"Memristive circuit of emotion with negative feedback based on three primary color model","authors":"Juntao Han ,&nbsp;Gang Liu ,&nbsp;Zhang Zhang","doi":"10.1016/j.neunet.2025.107385","DOIUrl":null,"url":null,"abstract":"<div><div>Many memristive circuits tend to oversimplify the process of emotion generation as a linear event, disregarding crucial factors such as negative feedback and other regulatory mechanisms. In this paper, a memristive circuit of emotion with negative feedback based on three primary color model is proposed to solve the above problems. The designed circuit is composed of perception modules, synapse modules, central nervous system modules and overt behavior module. It realizes emotion generation, emotion evolution and long-term memory functions based on the neural network circuit with behavioral homeostatic negative feedback function. Meanwhile, the three primary color model of basic emotions is discussed and realized. Any two basic emotions can be mixed to produce a higher order emotion. The memristive circuit, based on the three primary color model as a theoretical foundation, offers valuable insights for the further advancement of neural networks.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"187 ","pages":"Article 107385"},"PeriodicalIF":6.0000,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Neural Networks","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0893608025002643","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

Abstract

Many memristive circuits tend to oversimplify the process of emotion generation as a linear event, disregarding crucial factors such as negative feedback and other regulatory mechanisms. In this paper, a memristive circuit of emotion with negative feedback based on three primary color model is proposed to solve the above problems. The designed circuit is composed of perception modules, synapse modules, central nervous system modules and overt behavior module. It realizes emotion generation, emotion evolution and long-term memory functions based on the neural network circuit with behavioral homeostatic negative feedback function. Meanwhile, the three primary color model of basic emotions is discussed and realized. Any two basic emotions can be mixed to produce a higher order emotion. The memristive circuit, based on the three primary color model as a theoretical foundation, offers valuable insights for the further advancement of neural networks.
求助全文
约1分钟内获得全文 求助全文
来源期刊
Neural Networks
Neural Networks 工程技术-计算机:人工智能
CiteScore
13.90
自引率
7.70%
发文量
425
审稿时长
67 days
期刊介绍: Neural Networks is a platform that aims to foster an international community of scholars and practitioners interested in neural networks, deep learning, and other approaches to artificial intelligence and machine learning. Our journal invites submissions covering various aspects of neural networks research, from computational neuroscience and cognitive modeling to mathematical analyses and engineering applications. By providing a forum for interdisciplinary discussions between biology and technology, we aim to encourage the development of biologically-inspired artificial intelligence.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信