A scale-dependent measure of system dimensionality

A scale-dependent measure of system dimensionality
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系统维数的尺度相关度量

DOI:
10.1016/j.patter.2022.100
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发表时间:
2022
期刊:
影响因子:
6.5
通讯作者:
Shea-Brown, E.
Shea-Brown, E.
中科院分区:
--
文献类型:
--
作者:
Recanatesi, S.;Bradde, S.;Balasubramanian, V.;Steinmetz, N.;Shea-Brown, E.

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科学中的一个基本问题是揭示复杂系统中自由度的有效数量:它的维数。一个系统的维数取决于它的时空尺度。在这里,我们介绍了一个规模相关的推广的一个经典的枚举的潜在变量,参与率。我们演示了如何尺度相关的参与比确定适当的尺寸在本地,中间和全球范围内的几个系统,如洛伦兹吸引子,隐马尔可夫模型,切换线性动力系统。我们分析表明,在不同的限制规模,规模依赖的参与率涉及到完善的措施的维度。这种测量方法应用于多个大脑区域和大脑状态的神经群体记录,显示了神经活动维度的基本趋势,例如,行为参与与自发状态。我们的新方法统一了广泛使用的维度度量,并广泛适用于多个科学领域的多变量数据。
A fundamental problem in science is uncovering the effective number of degrees of freedom in a complex system: its dimensionality. A system's dimensionality depends on its spatiotemporal scale. Here, we introduce a scale-dependent generalization of a classic enumeration of latent variables, the participation ratio. We demonstrate how the scale-dependent participation ratio identifies the appropriate dimension at local, intermediate, and global scales in several systems such as the Lorenz attractor, hidden Markov models, and switching linear dynamical systems. We show analytically how, at different limiting scales, the scale-dependent participation ratio relates to well-established measures of dimensionality. This measure applied in neural population recordings across multiple brain areas and brain states shows fundamental trends in the dimensionality of neural activity—for example, in behaviorally engaged versus spontaneous states. Our novel method unifies widely used measures of dimensionality and applies broadly to multivariate data across several fields of science.
Neuropixels 2.0:用于稳定、长期脑部记录的小型化高密度探针
DOI: 10.1101/2020.10.27.358291
发表时间: 2020
期刊: --
影响因子: --
作者:
Steinmetz N
通讯作者: Steinmetz N
“有效”缔约方数量
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影响因子: --
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M. Laakso;R. Taagepera
通讯作者: R. Taagepera
DOI: 10.1088/0026-1394/37/1/8
发表时间: 2000-01-01
期刊: METROLOGIA
影响因子: 2.4
作者:
Ballico, M
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