Memcapacitive Spiking Neurons and Associative Memory Application

IF 3.4 3区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS IEEE Access Pub Date : 2025-03-06 DOI:10.1109/ACCESS.2025.3549357
S. J. Dat Tran
{"title":"Memcapacitive Spiking Neurons and Associative Memory Application","authors":"S. J. Dat Tran","doi":"10.1109/ACCESS.2025.3549357","DOIUrl":null,"url":null,"abstract":"The Hodgkin and Huxley neuron model describes the complex behavior of biological neurons. However, due to the complexity of these computations, the Hodgkin and Huxley models are impractical for use in large-scale networks. In contrast, Izhikevich introduced a simpler model capable of producing various firing patterns typical of cortical neurons. This study proposes a novel model of memcapacitive-based neurons that offers a potential implementation of spiking neurons with energy efficiency due to the inherent storage nature of memcapacitive devices. The findings demonstrate that memcapacitive neurons can produce 23 firing patterns similar to Izhikevich neurons but at significantly higher firing rates. Memcapacitive neurons exhibit firing patterns associated with excitatory, inhibitory, and thalamocortical neurons. Similar to Izhikevich neurons, pulse-coupled neural networks of memcapacitive neurons display collective behaviors, such as synchronous and asynchronous responses, which are common in the biological brain. Compared to Hopfield and Izhikevich networks in content-addressable memory applications, memcapacitive networks successfully retrieved correct memory patterns with high accuracy, even for distorted inputs of up to 40%. The simulation results illustrate that the novel model of the memcapacitive spiking neuron offers a potential advancement in implementing artificial spiking neurons with high energy efficiency, bringing a step closer to mimicking biological neurons.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"43933-43946"},"PeriodicalIF":3.4000,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10916667","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Access","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10916667/","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0

Abstract

The Hodgkin and Huxley neuron model describes the complex behavior of biological neurons. However, due to the complexity of these computations, the Hodgkin and Huxley models are impractical for use in large-scale networks. In contrast, Izhikevich introduced a simpler model capable of producing various firing patterns typical of cortical neurons. This study proposes a novel model of memcapacitive-based neurons that offers a potential implementation of spiking neurons with energy efficiency due to the inherent storage nature of memcapacitive devices. The findings demonstrate that memcapacitive neurons can produce 23 firing patterns similar to Izhikevich neurons but at significantly higher firing rates. Memcapacitive neurons exhibit firing patterns associated with excitatory, inhibitory, and thalamocortical neurons. Similar to Izhikevich neurons, pulse-coupled neural networks of memcapacitive neurons display collective behaviors, such as synchronous and asynchronous responses, which are common in the biological brain. Compared to Hopfield and Izhikevich networks in content-addressable memory applications, memcapacitive networks successfully retrieved correct memory patterns with high accuracy, even for distorted inputs of up to 40%. The simulation results illustrate that the novel model of the memcapacitive spiking neuron offers a potential advancement in implementing artificial spiking neurons with high energy efficiency, bringing a step closer to mimicking biological neurons.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
求助全文
约1分钟内获得全文 去求助
来源期刊
IEEE Access
IEEE Access COMPUTER SCIENCE, INFORMATION SYSTEMSENGIN-ENGINEERING, ELECTRICAL & ELECTRONIC
CiteScore
9.80
自引率
7.70%
发文量
6673
审稿时长
6 weeks
期刊介绍: IEEE Access® is a multidisciplinary, open access (OA), applications-oriented, all-electronic archival journal that continuously presents the results of original research or development across all of IEEE''s fields of interest. IEEE Access will publish articles that are of high interest to readers, original, technically correct, and clearly presented. Supported by author publication charges (APC), its hallmarks are a rapid peer review and publication process with open access to all readers. Unlike IEEE''s traditional Transactions or Journals, reviews are "binary", in that reviewers will either Accept or Reject an article in the form it is submitted in order to achieve rapid turnaround. Especially encouraged are submissions on: Multidisciplinary topics, or applications-oriented articles and negative results that do not fit within the scope of IEEE''s traditional journals. Practical articles discussing new experiments or measurement techniques, interesting solutions to engineering. Development of new or improved fabrication or manufacturing techniques. Reviews or survey articles of new or evolving fields oriented to assist others in understanding the new area.
期刊最新文献
High-Performance NTRU Accelerator Using a Direct Memory Access Controller Corrections to “A Visual Prompt-Based Mobile Learning System for Improved Algebraic Understanding in Students With Learning Disabilities” Utility Meets Privacy: A Critical Evaluation of Tabular Data Synthesizers Minimizing Delay at Closely Spaced Signalized Intersections Through Green Time Ratio Optimization: A Hybrid Approach With K-Means Clustering and Genetic Algorithms Analysis and Design of mmWave Passive CMOS Circulators With Unequal Port Impedances
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1