Network structure influences self-organized criticality in neural networks with dynamical synapses.

IF 3.1 4区 医学 Q2 NEUROSCIENCES
Frontiers in Systems Neuroscience Pub Date : 2025-06-18 eCollection Date: 2025-01-01 DOI:10.3389/fnsys.2025.1590743
Yoshiki A Sugimoto, Hiroshi Yadohisa, Masato S Abe
{"title":"Network structure influences self-organized criticality in neural networks with dynamical synapses.","authors":"Yoshiki A Sugimoto, Hiroshi Yadohisa, Masato S Abe","doi":"10.3389/fnsys.2025.1590743","DOIUrl":null,"url":null,"abstract":"<p><p>The brain criticality hypothesis has been a central research topic in theoretical neuroscience for two decades. This hypothesis suggests that the brain operates near the critical point at the boundary between order and disorder, where it acquires its information-processing capabilities. The mechanism that maintains this critical state has been proposed as a feedback system known as self-organized criticality (SOC); brain parameters, such as synaptic plasticity, are regulated internally without external adjustment. Therefore, clarifying how SOC occurs may provide insights into the mechanisms that maintain brain function and cause brain disorders. From the standpoint of neural network structures, the topology of neural circuits also plays a crucial role in information processing, with healthy neural networks exhibiting small world, scale-free, and modular characteristics. However, how these network structures affect SOC remains poorly understood. In this study, we conducted numerical simulations using a simplified neural network model to investigate how network structure may influence SOC. Our results reveal that the time scales at which synaptic plasticity operates to achieve a critical state differ depending on the network structure. Additionally, we observed Dragon king phenomena associated with abnormal neural activity, depending on the network structure and synaptic plasticity time scales. Notably, Dragon king was observed over a wide range of synaptic plasticity time scales in scale-free networks with high-degree hub nodes. These findings highlight the potential importance of neural network topology in shaping SOC dynamics in simplified models of neural systems.</p>","PeriodicalId":12649,"journal":{"name":"Frontiers in Systems Neuroscience","volume":"19 ","pages":"1590743"},"PeriodicalIF":3.1000,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12213684/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in Systems Neuroscience","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.3389/fnsys.2025.1590743","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"NEUROSCIENCES","Score":null,"Total":0}
引用次数: 0

Abstract

The brain criticality hypothesis has been a central research topic in theoretical neuroscience for two decades. This hypothesis suggests that the brain operates near the critical point at the boundary between order and disorder, where it acquires its information-processing capabilities. The mechanism that maintains this critical state has been proposed as a feedback system known as self-organized criticality (SOC); brain parameters, such as synaptic plasticity, are regulated internally without external adjustment. Therefore, clarifying how SOC occurs may provide insights into the mechanisms that maintain brain function and cause brain disorders. From the standpoint of neural network structures, the topology of neural circuits also plays a crucial role in information processing, with healthy neural networks exhibiting small world, scale-free, and modular characteristics. However, how these network structures affect SOC remains poorly understood. In this study, we conducted numerical simulations using a simplified neural network model to investigate how network structure may influence SOC. Our results reveal that the time scales at which synaptic plasticity operates to achieve a critical state differ depending on the network structure. Additionally, we observed Dragon king phenomena associated with abnormal neural activity, depending on the network structure and synaptic plasticity time scales. Notably, Dragon king was observed over a wide range of synaptic plasticity time scales in scale-free networks with high-degree hub nodes. These findings highlight the potential importance of neural network topology in shaping SOC dynamics in simplified models of neural systems.

网络结构影响动态突触神经网络的自组织临界性。
二十年来,大脑临界假说一直是理论神经科学的核心研究课题。这一假设表明,大脑在有序和无序边界的临界点附近运作,在那里它获得了信息处理能力。维持这种临界状态的机制被认为是一种被称为自组织临界(SOC)的反馈系统;大脑参数,如突触可塑性,是内部调节的,不需要外部调节。因此,阐明SOC如何发生可能为维持脑功能和引起脑疾病的机制提供见解。从神经网络结构的角度来看,神经电路的拓扑结构在信息处理中也起着至关重要的作用,健康的神经网络具有小世界、无标度和模块化的特征。然而,这些网络结构如何影响SOC仍然知之甚少。在本研究中,我们使用简化的神经网络模型进行了数值模拟,以研究网络结构如何影响SOC。我们的研究结果表明,突触可塑性达到临界状态的时间尺度因网络结构而异。此外,我们还观察到与异常神经活动相关的龙王现象,这取决于网络结构和突触可塑性的时间尺度。值得注意的是,在具有高度枢纽节点的无标度网络中,龙王在大范围的突触可塑性时间尺度上被观察到。这些发现强调了神经网络拓扑在神经系统简化模型中形成SOC动力学的潜在重要性。
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 求助全文
来源期刊
Frontiers in Systems Neuroscience
Frontiers in Systems Neuroscience Neuroscience-Developmental Neuroscience
CiteScore
6.00
自引率
3.30%
发文量
144
审稿时长
14 weeks
期刊介绍: Frontiers in Systems Neuroscience publishes rigorously peer-reviewed research that advances our understanding of whole systems of the brain, including those involved in sensation, movement, learning and memory, attention, reward, decision-making, reasoning, executive functions, and emotions.
×
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:604180095
Book学术官方微信