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神经科学中跨尺度的熵与复杂性工具:综述

Entropy and Complexity Tools Across Scales in Neuroscience: A Review.

作者信息

Cofré Rodrigo, Destexhe Alain

机构信息

Centre National de la Recherche Scientifique (CNRS), Institute of Neuroscience (NeuroPSI), Paris-Saclay University, 91400 Saclay, France.

出版信息

Entropy (Basel). 2025 Jan 24;27(2):115. doi: 10.3390/e27020115.


DOI:10.3390/e27020115
PMID:40003111
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11854896/
Abstract

Understanding the brain's intricate dynamics across multiple scales-from cellular interactions to large-scale brain behavior-remains one of the most significant challenges in modern neuroscience. Two key concepts, entropy and complexity, have been increasingly employed by neuroscientists as powerful tools for characterizing the interplay between structure and function in the brain across scales. The flexibility of these two concepts enables researchers to explore quantitatively how the brain processes information, adapts to changing environments, and maintains a delicate balance between order and disorder. This review illustrates the main tools and ideas to study neural phenomena using these concepts. This review does not delve into the specific methods or analyses of each study. Instead, it aims to offer a broad overview of how these tools are applied within the neuroscientific community and how they are transforming our understanding of the brain. We focus on their applications across scales, discuss the strengths and limitations of different metrics, and examine their practical applications and theoretical significance.

摘要

理解大脑在多个尺度上的复杂动态——从细胞间相互作用到大规模大脑行为——仍然是现代神经科学中最重大的挑战之一。熵和复杂性这两个关键概念越来越多地被神经科学家用作强大工具,以表征大脑跨尺度的结构与功能之间的相互作用。这两个概念的灵活性使研究人员能够定量探索大脑如何处理信息、适应不断变化的环境以及在有序和无序之间维持微妙平衡。本综述阐述了使用这些概念研究神经现象的主要工具和理念。本综述不深入探讨每项研究的具体方法或分析。相反,其目的是广泛概述这些工具在神经科学界的应用方式以及它们如何改变我们对大脑的理解。我们关注它们在不同尺度上的应用,讨论不同指标的优缺点,并审视它们的实际应用和理论意义。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d5d8/11854896/7b68a103c140/entropy-27-00115-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d5d8/11854896/aa25066ddf4d/entropy-27-00115-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d5d8/11854896/cb4f23514b18/entropy-27-00115-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d5d8/11854896/7b68a103c140/entropy-27-00115-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d5d8/11854896/aa25066ddf4d/entropy-27-00115-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d5d8/11854896/cb4f23514b18/entropy-27-00115-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d5d8/11854896/7b68a103c140/entropy-27-00115-g003.jpg

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[1]
Entropy and Complexity Tools Across Scales in Neuroscience: A Review.

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[5]
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[6]
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[7]
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本文引用的文献

[1]
Neural mass modeling for the masses: Democratizing access to whole-brain biophysical modeling with FastDMF.

Netw Neurosci. 2024-12-10

[2]
Hopfield and Hinton's neural network revolution and the future of AI.

Patterns (N Y). 2024-11-8

[3]
Dynamical structure-function correlations provide robust and generalizable signatures of consciousness in humans.

Commun Biol. 2024-9-30

[4]
A synergistic workspace for human consciousness revealed by Integrated Information Decomposition.

Elife. 2024-7-18

[5]
Spectral Slope and Lempel-Ziv Complexity as Robust Markers of Brain States during Sleep and Wakefulness.

eNeuro. 2024-3

[6]
Task-induced changes in brain entropy.

J Neurosci Res. 2024-2

[7]
Functional connectivity and complexity analyses of resting-state fMRI in pre-adolescents demonstrating the behavioral symptoms of ADHD.

Psychiatry Res. 2024-4

[8]
Effects of External Stimulation on Psychedelic State Neurodynamics.

ACS Chem Neurosci. 2024-2-7

[9]
A whole-brain model of the neural entropy increase elicited by psychedelic drugs.

Sci Rep. 2023-4-17

[10]
Higher-order interactions shape collective dynamics differently in hypergraphs and simplicial complexes.

Nat Commun. 2023-3-23

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