随机尖峰神经元网络跃迁的拓扑数据分析

IF 3.4 3区医学 Q2 NEUROSCIENCES Frontiers in Neural Circuits Pub Date : 2023-12-06 DOI:10.3389/fncir.2023.1308629

Xiaotian Bai, Chaojun Yu, Jian Zhai

{"title":"随机尖峰神经元网络跃迁的拓扑数据分析","authors":"Xiaotian Bai, Chaojun Yu, Jian Zhai","doi":"10.3389/fncir.2023.1308629","DOIUrl":null,"url":null,"abstract":"<p>Topological data analysis is becoming more and more popular in recent years. It has found various applications in many different fields, for its convenience in analyzing and understanding the structure and dynamic of complex systems. We used topological data analysis to analyze the firings of a network of stochastic spiking neurons, which can be in a sub-critical, critical, or super-critical state depending on the value of the control parameter. We calculated several topological features regarding Betti curves and then analyzed the behaviors of these features, using them as inputs for machine learning to discriminate the three states of the network.</p>","PeriodicalId":12498,"journal":{"name":"Frontiers in Neural Circuits","volume":"28 1","pages":""},"PeriodicalIF":3.4000,"publicationDate":"2023-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Topological data analysis of the firings of a network of stochastic spiking neurons\",\"authors\":\"Xiaotian Bai, Chaojun Yu, Jian Zhai\",\"doi\":\"10.3389/fncir.2023.1308629\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Topological data analysis is becoming more and more popular in recent years. It has found various applications in many different fields, for its convenience in analyzing and understanding the structure and dynamic of complex systems. We used topological data analysis to analyze the firings of a network of stochastic spiking neurons, which can be in a sub-critical, critical, or super-critical state depending on the value of the control parameter. We calculated several topological features regarding Betti curves and then analyzed the behaviors of these features, using them as inputs for machine learning to discriminate the three states of the network.</p>\",\"PeriodicalId\":12498,\"journal\":{\"name\":\"Frontiers in Neural Circuits\",\"volume\":\"28 1\",\"pages\":\"\"},\"PeriodicalIF\":3.4000,\"publicationDate\":\"2023-12-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Frontiers in Neural Circuits\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.3389/fncir.2023.1308629\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"NEUROSCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in Neural Circuits","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.3389/fncir.2023.1308629","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"NEUROSCIENCES","Score":null,"Total":0}

引用次数: 0

摘要

近年来，拓扑数据分析越来越受欢迎。由于拓扑数据分析便于分析和理解复杂系统的结构和动态，它已在许多不同领域得到广泛应用。我们使用拓扑数据分析来分析随机尖峰神经元网络的跃迁，根据控制参数值的不同，该网络可能处于亚临界、临界或超临界状态。我们计算了有关贝蒂曲线的几个拓扑特征，然后分析了这些特征的行为，并将其作为机器学习的输入来区分网络的三种状态。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

Topological data analysis of the firings of a network of stochastic spiking neurons

Topological data analysis is becoming more and more popular in recent years. It has found various applications in many different fields, for its convenience in analyzing and understanding the structure and dynamic of complex systems. We used topological data analysis to analyze the firings of a network of stochastic spiking neurons, which can be in a sub-critical, critical, or super-critical state depending on the value of the control parameter. We calculated several topological features regarding Betti curves and then analyzed the behaviors of these features, using them as inputs for machine learning to discriminate the three states of the network.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Frontiers in Neural Circuits NEUROSCIENCES-

CiteScore

6.00

自引率

5.70%

发文量

135

审稿时长

4-8 weeks

期刊介绍： Frontiers in Neural Circuits publishes rigorously peer-reviewed research on the emergent properties of neural circuits - the elementary modules of the brain. Specialty Chief Editors Takao K. Hensch and Edward Ruthazer at Harvard University and McGill University respectively, are supported by an outstanding Editorial Board of international experts. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics and the public worldwide. Frontiers in Neural Circuits launched in 2011 with great success and remains a "central watering hole" for research in neural circuits, serving the community worldwide to share data, ideas and inspiration. Articles revealing the anatomy, physiology, development or function of any neural circuitry in any species (from sponges to humans) are welcome. Our common thread seeks the computational strategies used by different circuits to link their structure with function (perceptual, motor, or internal), the general rules by which they operate, and how their particular designs lead to the emergence of complex properties and behaviors. Submissions focused on synaptic, cellular and connectivity principles in neural microcircuits using multidisciplinary approaches, especially newer molecular, developmental and genetic tools, are encouraged. Studies with an evolutionary perspective to better understand how circuit design and capabilities evolved to produce progressively more complex properties and behaviors are especially welcome. The journal is further interested in research revealing how plasticity shapes the structural and functional architecture of neural circuits.