Neurons, behavior, data analysis, and theory最新文献

英文中文

A study of animal action segmentation algorithms across supervised, unsupervised, and semi-supervised learning paradigms. 跨监督、无监督和半监督学习范式的动物动作分割算法研究。

Neurons, behavior, data analysis, and theory

Pub Date : 2024-01-01 Epub Date: 2024-12-20 DOI: 10.51628/001c.127770

Ari Blau, Evan S Schaffer, Neeli Mishra, Nathaniel J Miska, Liam Paninski, Matthew R Whiteway

Action segmentation of behavioral videos is the process of labeling each frame as belonging to one or more discrete classes, and is a crucial component of many studies that investigate animal behavior. A wide range of algorithms exist to automatically parse discrete animal behavior, encompassing supervised, unsupervised, and semi-supervised learning paradigms. These algorithms - which include tree-based models, deep neural networks, and graphical models - differ widely in their structure and assumptions on the data. Using four datasets spanning multiple species - fly, mouse, and human - we systematically study how the outputs of these various algorithms align with manually annotated behaviors of interest. Along the way, we introduce a semi-supervised action segmentation model that bridges the gap between supervised deep neural networks and unsupervised graphical models. We find that fully supervised temporal convolutional networks with the addition of temporal information in the observations perform the best on our supervised metrics across all datasets.

行为视频的动作分割是将每一帧标记为属于一个或多个离散类的过程，是许多研究动物行为的重要组成部分。有很多算法可以自动解析离散的动物行为，包括监督式、无监督式和半监督式学习范式。这些算法——包括基于树的模型、深度神经网络和图形模型——在结构和对数据的假设上有很大的不同。使用跨越多个物种（苍蝇、老鼠和人类）的四个数据集，我们系统地研究了这些不同算法的输出如何与人工注释感兴趣的行为保持一致。在此过程中，我们引入了一种半监督动作分割模型，它弥合了监督深度神经网络和无监督图形模型之间的差距。我们发现，在观察中添加时间信息的完全监督时间卷积网络在所有数据集的监督指标上表现最好。

{"title":"A study of animal action segmentation algorithms across supervised, unsupervised, and semi-supervised learning paradigms.","authors":"Ari Blau, Evan S Schaffer, Neeli Mishra, Nathaniel J Miska, Liam Paninski, Matthew R Whiteway","doi":"10.51628/001c.127770","DOIUrl":"https://doi.org/10.51628/001c.127770","url":null,"abstract":"Action segmentation of behavioral videos is the process of labeling each frame as belonging to one or more discrete classes, and is a crucial component of many studies that investigate animal behavior. A wide range of algorithms exist to automatically parse discrete animal behavior, encompassing supervised, unsupervised, and semi-supervised learning paradigms. These algorithms - which include tree-based models, deep neural networks, and graphical models - differ widely in their structure and assumptions on the data. Using four datasets spanning multiple species - fly, mouse, and human - we systematically study how the outputs of these various algorithms align with manually annotated behaviors of interest. Along the way, we introduce a semi-supervised action segmentation model that bridges the gap between supervised deep neural networks and unsupervised graphical models. We find that fully supervised temporal convolutional networks with the addition of temporal information in the observations perform the best on our supervised metrics across all datasets.","PeriodicalId":519987,"journal":{"name":"Neurons, behavior, data analysis, and theory","volume":"2024 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12365852/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144985248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Characterizing the nonlinear structure of shared variability in cortical neuron populations using latent variable models. 利用潜在变量模型描述大脑皮层神经元群共享变异性的非线性结构。

Neurons, behavior, data analysis, and theory

Pub Date : 2019-01-01 Epub Date: 2019-04-27

Matthew R Whiteway, Karolina Socha, Vincent Bonin, Daniel A Butts

Sensory neurons often have variable responses to repeated presentations of the same stimulus, which can significantly degrade the stimulus information contained in those responses. This information can in principle be preserved if variability is shared across many neurons, but depends on the structure of the shared variability and its relationship to sensory encoding at the population level. The structure of this shared variability in neural activity can be characterized by latent variable models, although they have thus far typically been used under restrictive mathematical assumptions, such as assuming linear transformations between the latent variables and neural activity. Here we introduce two nonlinear latent variable models for analyzing large-scale neural recordings. We first present a general nonlinear latent variable model that is agnostic to the stimulus tuning properties of the individual neurons, and is hence well suited for exploring neural populations whose tuning properties are not well characterized. This motivates a second class of model, the Generalized Affine Model, which simultaneously determines each neuron's stimulus selectivity and a set of latent variables that modulate these stimulus-driven responses both additively and multiplicatively. While these approaches can detect very general nonlinear relationships in shared neural variability, we find that neural activity recorded in anesthetized primary visual cortex (V1) is best described by a single additive and single multiplicative latent variable, i.e., an "affine model". In contrast, application of the same models to recordings in awake macaque prefrontal cortex discover more general nonlinearities to compactly describe the population response variability. These results thus demonstrate how nonlinear latent variable models can be used to describe population variability, and suggest that a range of methods is necessary to study different brain regions under different experimental conditions.

感觉神经元对重复出现的相同刺激往往会产生不同的反应，这会大大降低这些反应所包含的刺激信息。如果许多神经元之间共享变异性，原则上这种信息可以得到保留，但这取决于共享变异性的结构及其与群体水平的感觉编码之间的关系。神经活动中这种共享变异性的结构可以用潜在变量模型来描述，不过迄今为止，这些模型通常是在限制性数学假设条件下使用的，例如假设潜在变量和神经活动之间存在线性变换。在此，我们介绍两种用于分析大规模神经记录的非线性潜变量模型。首先，我们提出了一种通用的非线性潜变量模型，该模型与单个神经元的刺激调谐特性无关，因此非常适合探索调谐特性不明确的神经群。这就激发了第二类模型--广义仿射模型--的出现，它能同时确定每个神经元的刺激选择性和一组潜在变量，这些变量能以加法和乘法的方式调节这些刺激驱动的反应。虽然这些方法可以检测出共享神经变异性中非常普遍的非线性关系，但我们发现，在麻醉的初级视觉皮层（V1）中记录的神经活动用单一的加法和单一的乘法潜变量（即 "仿射模型"）来描述最为合适。与此相反，将相同的模型应用于清醒猕猴前额叶皮层的记录时，却发现了更普遍的非线性因素，从而紧凑地描述了群体反应的变异性。这些结果证明了非线性潜变量模型如何用于描述群体变异性，并表明在不同实验条件下研究不同脑区需要一系列方法。

{"title":"Characterizing the nonlinear structure of shared variability in cortical neuron populations using latent variable models.","authors":"Matthew R Whiteway, Karolina Socha, Vincent Bonin, Daniel A Butts","doi":"","DOIUrl":"","url":null,"abstract":"Sensory neurons often have variable responses to repeated presentations of the same stimulus, which can significantly degrade the stimulus information contained in those responses. This information can in principle be preserved if variability is shared across many neurons, but depends on the structure of the shared variability and its relationship to sensory encoding at the population level. The structure of this shared variability in neural activity can be characterized by latent variable models, although they have thus far typically been used under restrictive mathematical assumptions, such as assuming linear transformations between the latent variables and neural activity. Here we introduce two nonlinear latent variable models for analyzing large-scale neural recordings. We first present a general nonlinear latent variable model that is agnostic to the stimulus tuning properties of the individual neurons, and is hence well suited for exploring neural populations whose tuning properties are not well characterized. This motivates a second class of model, the Generalized Affine Model, which simultaneously determines each neuron's stimulus selectivity and a set of latent variables that modulate these stimulus-driven responses both additively and multiplicatively. While these approaches can detect very general nonlinear relationships in shared neural variability, we find that neural activity recorded in anesthetized primary visual cortex (V1) is best described by a single additive and single multiplicative latent variable, i.e., an \"affine model\". In contrast, application of the same models to recordings in awake macaque prefrontal cortex discover more general nonlinearities to compactly describe the population response variability. These results thus demonstrate how nonlinear latent variable models can be used to describe population variability, and suggest that a range of methods is necessary to study different brain regions under different experimental conditions.","PeriodicalId":519987,"journal":{"name":"Neurons, behavior, data analysis, and theory","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6779168/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141728515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

类型

全部化学•材料生命科学医学物理工程技术环境•农林材料科学地球科学法学管理学化学环境科学与生态学计算机科学教育学经济学农林科学人文科学生物学数学物理与天体物理心理学综合性期刊其他工业工程理学历史学农学文学信息工程

数据库

全部 ACS Publications Elsevier ieeexplore Springer The Royal Society of Chemistry Wiley

期刊

Neurons, behavior, data analysis, and theory

全部 Acc. Chem. Res. ACS Applied Bio Materials ACS Appl. Electron. Mater. ACS Appl. Energy Mater. ACS Appl. Mater. Interfaces ACS Appl. Nano Mater. ACS Appl. Polym. Mater. ACS BIOMATER-SCI ENG ACS Catal. ACS Cent. Sci. ACS Chem. Biol. ACS Chemical Health & Safety ACS Chem. Neurosci. ACS Comb. Sci. ACS Earth Space Chem. ACS Energy Lett. ACS Infect. Dis. ACS Macro Lett. ACS Mater. Lett. ACS Med. Chem. Lett. ACS Nano ACS Omega ACS Photonics ACS Sens. ACS Sustainable Chem. Eng. ACS Synth. Biol. Anal. Chem. BIOCHEMISTRY-US Bioconjugate Chem. BIOMACROMOLECULES Chem. Res. Toxicol. Chem. Rev. Chem. Mater. CRYST GROWTH DES ENERG FUEL Environ. Sci. Technol. Environ. Sci. Technol. Lett. Eur. J. Inorg. Chem. IND ENG CHEM RES Inorg. Chem. J. Agric. Food. Chem. J. Chem. Eng. Data J. Chem. Educ. J. Chem. Inf. Model. J. Chem. Theory Comput. J. Med. Chem. J. Nat. Prod. J PROTEOME RES J. Am. Chem. Soc. LANGMUIR MACROMOLECULES Mol. Pharmaceutics Nano Lett. Org. Lett. ORG PROCESS RES DEV ORGANOMETALLICS J. Org. Chem. J. Phys. Chem. J. Phys. Chem. A J. Phys. Chem. B J. Phys. Chem. C J. Phys. Chem. Lett. Analyst Anal. Methods Biomater. Sci. Catal. Sci. Technol. Chem. Commun. Chem. Soc. Rev. CHEM EDUC RES PRACT CRYSTENGCOMM Dalton Trans. Energy Environ. Sci. ENVIRON SCI-NANO ENVIRON SCI-PROC IMP ENVIRON SCI-WAT RES Faraday Discuss. Food Funct. Green Chem. Inorg. Chem. Front. Integr. Biol. J. Anal. At. Spectrom. J. Mater. Chem. A J. Mater. Chem. B J. Mater. Chem. C Lab Chip Mater. Chem. Front. Mater. Horiz. MEDCHEMCOMM Metallomics Mol. Biosyst. Mol. Syst. Des. Eng. Nanoscale Nanoscale Horiz. Nat. Prod. Rep. New J. Chem. Org. Biomol. Chem. Org. Chem. Front. PHOTOCH PHOTOBIO SCI PCCP Polym. Chem.

﹀