Biologically plausible information propagation in a complementary metal-oxide semiconductor integrate-and-fire artificial neuron circuit with memristive synapses

IF 2.5 4区 材料科学 Q3 MATERIALS SCIENCE, MULTIDISCIPLINARY Nano Futures Pub Date : 2023-04-21 DOI:10.1088/2399-1984/accf53
Lorenzo Benatti, T. Zanotti, D. Gandolfi, J. Mapelli, F. Puglisi
{"title":"Biologically plausible information propagation in a complementary metal-oxide semiconductor integrate-and-fire artificial neuron circuit with memristive synapses","authors":"Lorenzo Benatti, T. Zanotti, D. Gandolfi, J. Mapelli, F. Puglisi","doi":"10.1088/2399-1984/accf53","DOIUrl":null,"url":null,"abstract":"Neuromorphic circuits based on spikes are currently envisioned as a viable option to achieve brain-like computation capabilities in specific electronic implementations while limiting power dissipation given their ability to mimic energy-efficient bioinspired mechanisms. While several network architectures have been developed to embed in hardware the bioinspired learning rules found in the biological brain, such as spike timing-dependent plasticity, it is still unclear if hardware spiking neural network architectures can handle and transfer information akin to biological networks. In this work, we investigate the analogies between an artificial neuron combining memristor synapses and rate-based learning rule with biological neuron response in terms of information propagation from a theoretical perspective. Bioinspired experiments have been reproduced by linking the biological probability of release with the artificial synapse conductance. Mutual information and surprise have been chosen as metrics to evidence how, for different values of synaptic weights, an artificial neuron allows to develop a reliable and biological resembling neural network in terms of information propagation and analysis.","PeriodicalId":54222,"journal":{"name":"Nano Futures","volume":null,"pages":null},"PeriodicalIF":2.5000,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nano Futures","FirstCategoryId":"88","ListUrlMain":"https://doi.org/10.1088/2399-1984/accf53","RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0

Abstract

Neuromorphic circuits based on spikes are currently envisioned as a viable option to achieve brain-like computation capabilities in specific electronic implementations while limiting power dissipation given their ability to mimic energy-efficient bioinspired mechanisms. While several network architectures have been developed to embed in hardware the bioinspired learning rules found in the biological brain, such as spike timing-dependent plasticity, it is still unclear if hardware spiking neural network architectures can handle and transfer information akin to biological networks. In this work, we investigate the analogies between an artificial neuron combining memristor synapses and rate-based learning rule with biological neuron response in terms of information propagation from a theoretical perspective. Bioinspired experiments have been reproduced by linking the biological probability of release with the artificial synapse conductance. Mutual information and surprise have been chosen as metrics to evidence how, for different values of synaptic weights, an artificial neuron allows to develop a reliable and biological resembling neural network in terms of information propagation and analysis.
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
生物学上可信的信息传播在互补金属氧化物半导体集成和发射人工神经元电路与忆阻突触
基于尖峰的神经形态电路目前被设想为在特定电子实现中实现类脑计算能力的可行选择,同时由于其模拟节能生物激励机制的能力而限制了功耗。虽然已经开发了几种网络架构来将生物大脑中发现的生物启发学习规则嵌入到硬件中,例如峰值时间依赖的可塑性,但尚不清楚硬件峰值神经网络架构是否可以处理和传输类似于生物网络的信息。在这项工作中,我们从理论的角度研究了结合记忆电阻突触和基于速率的学习规则的人工神经元与生物神经元响应在信息传播方面的相似性。通过将释放的生物概率与人工突触传导联系起来,再现了生物启发实验。互信息和惊讶度被选择作为度量来证明,对于不同的突触权重值,人工神经元如何在信息传播和分析方面发展出可靠的和生物相似的神经网络。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Nano Futures
Nano Futures Chemistry-General Chemistry
CiteScore
4.30
自引率
0.00%
发文量
35
期刊介绍: Nano Futures mission is to reflect the diverse and multidisciplinary field of nanoscience and nanotechnology that now brings together researchers from across physics, chemistry, biomedicine, materials science, engineering and industry.
期刊最新文献
Nanobiohybrids and bacterial carriers: a novel pathway to targeted cancer therapy The use of orthogonal analytical approaches to profile lipid nanoparticle physicochemical attributes Navigating the frontiers of graphene quality control to enable product optimisation and market confidence Overlapping top gate electrodes based on low temperature atomic layer deposition for nanoscale ambipolar lateral junctions Turning CO2 into Sustainable Graphene: A Comprehensive Review of Recent Synthesis Techniques and Developments
×
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