A scale-dependent measure of system dimensionality
A scale-dependent measure of system dimensionality
复制标题
系统维数的尺度相关度量
DOI:
10.1016/j.patter.2022.100
复制
发表时间:
2022
期刊:
影响因子:
6.5
通讯作者:
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.
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.
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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
DOI:
--
发表时间:
1979
期刊:
影响因子:
--
作者:
M. Laakso;R. Taagepera
通讯作者:
R. Taagepera
影响因子:
2.4
作者:
Ballico, M
通讯作者:
Ballico, M
影响因子:
5.6
作者:
Kuznetsov, N., V;Mokaev, T. N.;Kudryashova, E., V
通讯作者:
Kudryashova, E., V
影响因子:
4.6
作者:
Facco E;d'Errico M;Rodriguez A;Laio A
通讯作者:
Laio A