A framework for analyzing C. elegans neural activity using multi-dimensional hyperbolic embedding.

Iulia Rusu, Zachary T Cecere, Javier J How, Kathleen T Quach, Eviatar Yemini, Tatyana O Sharpee, Sreekanth H Chalasani
{"title":"A framework for analyzing <i>C. elegans</i> neural activity using multi-dimensional hyperbolic embedding.","authors":"Iulia Rusu, Zachary T Cecere, Javier J How, Kathleen T Quach, Eviatar Yemini, Tatyana O Sharpee, Sreekanth H Chalasani","doi":"10.1101/2021.04.09.439242","DOIUrl":null,"url":null,"abstract":"<p><p>Neurons represent changes in external and internal environments by altering their activity patterns. While coherent brain-wide patterns of neural activity have been observed in neuronal populations, very little is known about their dimensionality, geometry, and how they are correlated with sensory inputs. Here, we recorded the activity of most head neurons in <i>Caenorhabditis elegans</i> experiencing changes in bacterial or control buffer stimuli around their nose. We first classified active neurons into six functional clusters: two sensory neuron clusters (ON and OFF responding to addition and removal of stimuli, respectively) and four motor/command neuron clusters (AVA, RME, SMDD and SMDV). Next, we estimated stimulus selectivity for each cluster and found that while sensory neurons exhibit their maximal responses within 15 seconds, changes in bacterial stimuli drive maximal responses in command and motor neuron clusters after tens of seconds. Furthermore, we show that bacterial stimuli induce neural dynamics that are best described by a hyperbolic, not Euclidean, space, of dimensionality eight. The hyperbolic space provided a better description of neural activity than the standard Euclidean space. This space can be separated into three components - one sensory, and two motor directions (forward-backward and dorsal-ventral). Collectively, we show that <i>C. elegans</i> neural activity can be effectively represented in low-dimensional hyperbolic space to describe a sensorimotor transformation.</p>","PeriodicalId":72407,"journal":{"name":"bioRxiv : the preprint server for biology","volume":"3 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12132397/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"bioRxiv : the preprint server for biology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1101/2021.04.09.439242","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Neurons represent changes in external and internal environments by altering their activity patterns. While coherent brain-wide patterns of neural activity have been observed in neuronal populations, very little is known about their dimensionality, geometry, and how they are correlated with sensory inputs. Here, we recorded the activity of most head neurons in Caenorhabditis elegans experiencing changes in bacterial or control buffer stimuli around their nose. We first classified active neurons into six functional clusters: two sensory neuron clusters (ON and OFF responding to addition and removal of stimuli, respectively) and four motor/command neuron clusters (AVA, RME, SMDD and SMDV). Next, we estimated stimulus selectivity for each cluster and found that while sensory neurons exhibit their maximal responses within 15 seconds, changes in bacterial stimuli drive maximal responses in command and motor neuron clusters after tens of seconds. Furthermore, we show that bacterial stimuli induce neural dynamics that are best described by a hyperbolic, not Euclidean, space, of dimensionality eight. The hyperbolic space provided a better description of neural activity than the standard Euclidean space. This space can be separated into three components - one sensory, and two motor directions (forward-backward and dorsal-ventral). Collectively, we show that C. elegans neural activity can be effectively represented in low-dimensional hyperbolic space to describe a sensorimotor transformation.

利用多维双曲嵌入分析秀丽隐杆线虫神经活动的框架。
神经元通过改变其活动模式来表现外部和内部环境的变化。虽然在神经元群中已经观察到连贯的全脑神经活动模式,但对它们的维度、几何形状以及它们如何与感觉输入相关联知之甚少。在这里,我们记录了秀丽隐杆线虫在鼻子周围的细菌或控制缓冲刺激发生变化时大多数头部神经元的活动。我们首先将活跃的神经元分为六个功能簇:两个感觉神经元簇(分别响应增加和消除刺激的ON和OFF)和四个运动/命令神经元簇(AVA, RME, SMDD和SMDV)。接下来,我们估计了每个神经元簇的刺激选择性,发现当感觉神经元在15秒内表现出最大反应时,细菌刺激的变化在几十秒后驱动命令和运动神经元簇的最大反应。此外,我们表明,细菌刺激诱导的神经动力学是最好的描述双曲,而不是欧几里得空间,维度八。双曲空间比标准欧几里得空间更能描述神经活动。这个空间可以分为三个部分——一个是感觉,两个是运动方向(前后和背腹)。总的来说,我们表明秀丽隐杆线虫的神经活动可以在低维双曲空间中有效地表示,以描述感觉运动转换。意义说明:神经系统的一个主要功能是将感觉信息转化为行为输出。作为感觉输入的第一接收器,感觉神经元的活动通常与刺激特征最相关。然而,这种感觉活动在传播到其他神经元时被修改,在改变运动神经元并驱动相应行为之前,它与网络活动结合在一起。非感觉神经元的活动是由持续的网络活动和感觉输入驱动的,但区分它们的相对贡献通常是困难的。在这里,我们鉴定了线虫神经网络中响应细菌刺激的两个感觉神经元簇和四个命令/运动神经元簇,并定义了它们的接受野。然后,我们使用双曲嵌入来确定这些集群如何相互作用,并确定可能改变行为的相关维度。我们的方法完全可扩展到其他系统,包括那些没有神经元身份的系统,并允许我们将神经活动归因于网络状态和行为输出。
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
自引率
0.00%
发文量
0
×
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