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具有时滞耦合的兴奋单元网络中的随机爆发。

Stochastic bursting in networks of excitable units with delayed coupling.

机构信息

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

Department of Control Theory, Nizhny Novgorod State University, Gagarin Avenue 23, Nizhny Novgorod, Russia, 606950.

出版信息

Biol Cybern. 2022 Apr;116(2):121-128. doi: 10.1007/s00422-021-00883-9. Epub 2021 Jun 28.

DOI:10.1007/s00422-021-00883-9
PMID:34181074
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9068677/
Abstract

We investigate the phenomenon of stochastic bursting in a noisy excitable unit with multiple weak delay feedbacks, by virtue of a directed tree lattice model. We find statistical properties of the appearing sequence of spikes and expressions for the power spectral density. This simple model is extended to a network of three units with delayed coupling of a star type. We find the power spectral density of each unit and the cross-spectral density between any two units. The basic assumptions behind the analytical approach are the separation of timescales, allowing for a description of the spike train as a point process, and weakness of coupling, allowing for a representation of the action of overlapped spikes via the sum of the one-spike excitation probabilities.

摘要

我们借助有向树格模型研究了在具有多个弱延迟反馈的噪声兴奋性单元中随机突发的现象。我们发现了尖峰出现序列的统计特性和功率谱密度的表达式。此简单模型扩展到了具有星型延迟耦合的三个单元网络。我们找到了每个单元的功率谱密度和任意两个单元之间的互功率谱密度。分析方法背后的基本假设是时间尺度的分离,这允许将尖峰序列描述为一个点过程,以及耦合的弱度,这允许通过单尖峰激发概率的和来表示重叠尖峰的作用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/adb8/9068677/1cdad3eff387/422_2021_883_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/adb8/9068677/89c8cfe36dd5/422_2021_883_Fig1_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/adb8/9068677/a9efe3a51724/422_2021_883_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/adb8/9068677/09f8ea61c0a9/422_2021_883_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/adb8/9068677/154f512f314d/422_2021_883_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/adb8/9068677/1cdad3eff387/422_2021_883_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/adb8/9068677/89c8cfe36dd5/422_2021_883_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/adb8/9068677/6d6aac805485/422_2021_883_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/adb8/9068677/492c54956480/422_2021_883_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/adb8/9068677/64d25cf5abff/422_2021_883_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/adb8/9068677/a9efe3a51724/422_2021_883_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/adb8/9068677/09f8ea61c0a9/422_2021_883_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/adb8/9068677/154f512f314d/422_2021_883_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/adb8/9068677/1cdad3eff387/422_2021_883_Fig8_HTML.jpg

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本文引用的文献

1
Stochastic bursting in unidirectionally delay-coupled noisy excitable systems.单向延迟耦合噪声可激发系统中的随机爆发
Chaos. 2019 Apr;29(4):041103. doi: 10.1063/1.5093180.
2
Linking structure and activity in nonlinear spiking networks.非线性脉冲发放网络中结构与活动的关联
PLoS Comput Biol. 2017 Jun 23;13(6):e1005583. doi: 10.1371/journal.pcbi.1005583. eCollection 2017 Jun.
3
Interplay between Graph Topology and Correlations of Third Order in Spiking Neuronal Networks.脉冲神经元网络中图形拓扑与三阶相关性之间的相互作用
PLoS Comput Biol. 2016 Jun 6;12(6):e1004963. doi: 10.1371/journal.pcbi.1004963. eCollection 2016 Jun.
4
The mechanics of state-dependent neural correlations.状态依赖神经相关性的机制。
Nat Neurosci. 2016 Mar;19(3):383-93. doi: 10.1038/nn.4242.
5
Impact of network structure and cellular response on spike time correlations.网络结构和细胞反应对尖峰时间相关性的影响。
PLoS Comput Biol. 2012;8(3):e1002408. doi: 10.1371/journal.pcbi.1002408. Epub 2012 Mar 22.
6
How structure determines correlations in neuronal networks.结构如何决定神经元网络中的相关性。
PLoS Comput Biol. 2011 May;7(5):e1002059. doi: 10.1371/journal.pcbi.1002059. Epub 2011 May 19.
7
Correlation between neural spike trains increases with firing rate.神经脉冲序列之间的相关性随放电率增加。
Nature. 2007 Aug 16;448(7155):802-6. doi: 10.1038/nature06028.
8
Origin of bursting through homoclinic spike adding in a neuron model.神经元模型中同宿尖峰添加引发的爆发起源
Phys Rev Lett. 2007 Mar 30;98(13):134101. doi: 10.1103/PhysRevLett.98.134101.
9
Polychronization: computation with spikes.多步同步:基于脉冲的计算。
Neural Comput. 2006 Feb;18(2):245-82. doi: 10.1162/089976606775093882.
10
Integrate-and-fire neurons with threshold noise: a tractable model of how interspike interval correlations affect neuronal signal transmission.具有阈值噪声的积分发放神经元:一种关于峰间期相关性如何影响神经元信号传递的易处理模型。
Phys Rev E Stat Nonlin Soft Matter Phys. 2005 Aug;72(2 Pt 1):021911. doi: 10.1103/PhysRevE.72.021911. Epub 2005 Aug 26.