• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法，用中文像聊天一样搜索，搜遍4000万医学文献。AI智能推荐，让科研检索更轻松。

立即免费搜索

文件翻译

保留排版，准确专业，支持PDF/Word/PPT等文件格式，支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述，25分钟生成高质量综述，智能提取关键信息，辅助科研写作。

立即免费体验

θ神经元环形网络中的集体状态

Collective states in a ring network of theta neurons.

作者信息

Omel'chenko Oleh, Laing Carlo R

机构信息

University of Potsdam, Institute of Physics and Astronomy, Karl-Liebknecht-Str. 24/25, Potsdam 14476, Germany.

School of Natural and Computational Sciences, Massey University, Private Bag 102-904 NSMC, Auckland, New Zealand.

出版信息

Proc Math Phys Eng Sci. 2022 Mar;478(2259):20210817. doi: 10.1098/rspa.2021.0817. Epub 2022 Mar 9.

DOI:10.1098/rspa.2021.0817
PMID:35280327
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8908473/
Abstract

We consider a ring network of theta neurons with non-local homogeneous coupling. We analyse the corresponding continuum evolution equation, analytically describing all possible steady states and their stability. By considering a number of different parameter sets, we determine the typical bifurcation scenarios of the network, and put on a rigorous footing some previously observed numerical results.

摘要

我们考虑一个具有非局部均匀耦合的θ神经元环形网络。我们分析了相应的连续演化方程，解析地描述了所有可能的稳态及其稳定性。通过考虑一些不同的参数集，我们确定了网络的典型分岔情况，并为一些先前观察到的数值结果奠定了严格的基础。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9421/8908473/9fdbbf7aac9f/rspa20210817f13.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9421/8908473/77959b1615a3/rspa20210817f01.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9421/8908473/36ac4a426eca/rspa20210817f02.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9421/8908473/77f27beea258/rspa20210817f03.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9421/8908473/04a2a723b0d8/rspa20210817f04.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9421/8908473/b5c588e970ea/rspa20210817f05.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9421/8908473/95a93c379eb6/rspa20210817f06.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9421/8908473/66269f9a9867/rspa20210817f07.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9421/8908473/4f9cf7daeedd/rspa20210817f08.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9421/8908473/da76dba11155/rspa20210817f09.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9421/8908473/d77b5f58d467/rspa20210817f10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9421/8908473/c702c0ef1d26/rspa20210817f11.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9421/8908473/dfd6875d9273/rspa20210817f12.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9421/8908473/9fdbbf7aac9f/rspa20210817f13.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9421/8908473/77959b1615a3/rspa20210817f01.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9421/8908473/36ac4a426eca/rspa20210817f02.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9421/8908473/77f27beea258/rspa20210817f03.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9421/8908473/04a2a723b0d8/rspa20210817f04.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9421/8908473/b5c588e970ea/rspa20210817f05.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9421/8908473/95a93c379eb6/rspa20210817f06.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9421/8908473/66269f9a9867/rspa20210817f07.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9421/8908473/4f9cf7daeedd/rspa20210817f08.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9421/8908473/da76dba11155/rspa20210817f09.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9421/8908473/d77b5f58d467/rspa20210817f10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9421/8908473/c702c0ef1d26/rspa20210817f11.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9421/8908473/dfd6875d9273/rspa20210817f12.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9421/8908473/9fdbbf7aac9f/rspa20210817f13.jpg

相似文献

1
Collective states in a ring network of theta neurons.θ神经元环形网络中的集体状态
Proc Math Phys Eng Sci. 2022 Mar;478(2259):20210817. doi: 10.1098/rspa.2021.0817. Epub 2022 Mar 9.
2
Moving bumps in theta neuron networks.theta 神经元网络中的移动凸起。
Chaos. 2020 Apr;30(4):043117. doi: 10.1063/1.5143261.
3
Bumps in Small-World Networks.小世界网络中的节点
Front Comput Neurosci. 2016 Jun 15;10:53. doi: 10.3389/fncom.2016.00053. eCollection 2016.
4
Synaptic Diversity Suppresses Complex Collective Behavior in Networks of Theta Neurons.突触多样性抑制θ神经元网络中的复杂集体行为。
Front Comput Neurosci. 2020 May 26;14:44. doi: 10.3389/fncom.2020.00044. eCollection 2020.
5
Macroscopic complexity from an autonomous network of networks of theta neurons.来自θ神经元网络的自主网络的宏观复杂性。
Front Comput Neurosci. 2014 Nov 18;8:145. doi: 10.3389/fncom.2014.00145. eCollection 2014.
6
Interpolating between bumps and chimeras.在驼峰和嵌合体之间插值。
Chaos. 2021 Nov;31(11):113116. doi: 10.1063/5.0070341.
7
Stability and bifurcation of collective dynamics in phase oscillator populations with general coupling.具有一般耦合的相位振子群体中集体动力学的稳定性与分岔
Phys Rev E. 2021 Mar;103(3-1):032307. doi: 10.1103/PhysRevE.103.032307.
8
Stability switches and double Hopf bifurcation in a two-neural network system with multiple delays.具有多个时滞的两个神经网络系统中的稳定性开关和双 Hopf 分岔。
Cogn Neurodyn. 2013 Dec;7(6):505-21. doi: 10.1007/s11571-013-9254-0. Epub 2013 Apr 16.
9
Population dynamics of the modified theta model: macroscopic phase reduction and bifurcation analysis link microscopic neuronal interactions to macroscopic gamma oscillation.修正theta模型的种群动力学：宏观相位约简与分岔分析将微观神经元相互作用与宏观伽马振荡联系起来。
J R Soc Interface. 2014 Mar 19;11(95):20140058. doi: 10.1098/rsif.2014.0058. Print 2014 Jun 6.
10
Dynamics of unidirectionally-coupled ring neural network with discrete and distributed delays.具有离散和分布式时滞的单向耦合环形神经网络动力学。
J Math Biol. 2020 May;80(6):1617-1653. doi: 10.1007/s00285-020-01475-0. Epub 2020 Jan 30.

引用本文的文献

1
Periodic solutions in next generation neural field models.下一代神经场模型中的周期解。
Biol Cybern. 2023 Oct;117(4-5):259-274. doi: 10.1007/s00422-023-00969-6. Epub 2023 Aug 3.

本文引用的文献

1
Effects of degree distributions in random networks of type-I neurons.I型神经元随机网络中度数分布的影响。
Phys Rev E. 2021 May;103(5-1):052305. doi: 10.1103/PhysRevE.103.052305.
2
Mean-Field Models for EEG/MEG: From Oscillations to Waves.脑电/脑磁图的平均场模型：从震荡到波动。
Brain Topogr. 2022 Jan;35(1):36-53. doi: 10.1007/s10548-021-00842-4. Epub 2021 May 15.
3
Bumps and oscillons in networks of spiking neurons.神经元网络中的峰和孤子。
Chaos. 2020 Mar;30(3):033133. doi: 10.1063/1.5135579.
4
Next-generation neural field model: The evolution of synchrony within patterns and waves.下一代神经场模型：模式和波内同步的演变。
Phys Rev E. 2019 Jan;99(1-1):012313. doi: 10.1103/PhysRevE.99.012313.
5
Synchrony-induced modes of oscillation of a neural field model.同步诱导的神经场模型的振荡模式。
Phys Rev E. 2017 Nov;96(5-1):052407. doi: 10.1103/PhysRevE.96.052407. Epub 2017 Nov 13.
6
Derivation of a neural field model from a network of theta neurons.从θ神经元网络推导神经场模型。
Phys Rev E Stat Nonlin Soft Matter Phys. 2014 Jul;90(1):010901. doi: 10.1103/PhysRevE.90.010901. Epub 2014 Jul 28.
7
Complete classification of the macroscopic behavior of a heterogeneous network of theta neurons.完整分类不均匀网络的θ神经元的宏观行为。
Neural Comput. 2013 Dec;25(12):3207-34. doi: 10.1162/NECO_a_00525. Epub 2013 Sep 18.
8
Synchronization and desynchronization in epilepsy: controversies and hypotheses.癫痫的同步和去同步：争议和假说。
J Physiol. 2013 Feb 15;591(4):787-97. doi: 10.1113/jphysiol.2012.239590. Epub 2012 Nov 26.
9
Long time evolution of phase oscillator systems.相位振荡器系统的长期演化
Chaos. 2009 Jun;19(2):023117. doi: 10.1063/1.3136851.
10
Low dimensional behavior of large systems of globally coupled oscillators.全局耦合振子大系统的低维行为
Chaos. 2008 Sep;18(3):037113. doi: 10.1063/1.2930766.