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本文引用的文献

1
Encoding innate ability through a genomic bottleneck.通过基因组瓶颈来编码先天能力。
Proc Natl Acad Sci U S A. 2024 Sep 17;121(38):e2409160121. doi: 10.1073/pnas.2409160121. Epub 2024 Sep 12.
2
Functional neuronal circuits emerge in the absence of developmental activity.功能性神经元回路在缺乏发育活动的情况下出现。
Nat Commun. 2024 Jan 8;15(1):364. doi: 10.1038/s41467-023-44681-2.
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Complex computation from developmental priors.基于发育先验的复杂计算。
Nat Commun. 2023 Apr 19;14(1):2226. doi: 10.1038/s41467-023-37980-1.
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Cognition from the Body-Brain Partnership: Exaptation of Memory.从身心共同体角度看认知:记忆的适应。
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The dynamic nature of percolation on networks with triadic interactions.具有三重相互作用的网络上渗流的动态特性。
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The connectome of an insect brain.昆虫大脑的连接组图谱。
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Everything is connected: Graph neural networks.万物皆相连:图神经网络。
Curr Opin Struct Biol. 2023 Apr;79:102538. doi: 10.1016/j.sbi.2023.102538. Epub 2023 Feb 9.
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Preconfigured dynamics in the hippocampus are guided by embryonic birthdate and rate of neurogenesis.海马体中的预配置动力学受胚胎出生日期和神经发生速度的指导。
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What is a cell type and how to define it?什么是细胞类型,如何定义它?
Cell. 2022 Jul 21;185(15):2739-2755. doi: 10.1016/j.cell.2022.06.031.
10
A guided multiverse study of neuroimaging analyses.神经影像学分析的引导式多重宇宙研究。
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神经科学需要网络科学。

Neuroscience Needs Network Science.

机构信息

Biophysics Program, Harvard University, Cambridge, 02138, Massachusetts

Department of Molecular and Cellular Biology and Center for Brain Science, Harvard University, Cambridge, 02138, Massachusetts.

出版信息

J Neurosci. 2023 Aug 23;43(34):5989-5995. doi: 10.1523/JNEUROSCI.1014-23.2023.

DOI:10.1523/JNEUROSCI.1014-23.2023
PMID:37612141
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10451115/
Abstract

The brain is a complex system comprising a myriad of interacting neurons, posing significant challenges in understanding its structure, function, and dynamics. Network science has emerged as a powerful tool for studying such interconnected systems, offering a framework for integrating multiscale data and complexity. To date, network methods have significantly advanced functional imaging studies of the human brain and have facilitated the development of control theory-based applications for directing brain activity. Here, we discuss emerging frontiers for network neuroscience in the brain atlas era, addressing the challenges and opportunities in integrating multiple data streams for understanding the neural transitions from development to healthy function to disease. We underscore the importance of fostering interdisciplinary opportunities through workshops, conferences, and funding initiatives, such as supporting students and postdoctoral fellows with interests in both disciplines. By bringing together the network science and neuroscience communities, we can develop novel network-based methods tailored to neural circuits, paving the way toward a deeper understanding of the brain and its functions, as well as offering new challenges for network science.

摘要

大脑是一个由无数相互作用的神经元组成的复杂系统,理解其结构、功能和动态具有很大的挑战性。网络科学已经成为研究这种相互关联系统的有力工具,为整合多尺度数据和复杂性提供了一个框架。迄今为止,网络方法极大地推动了人类大脑功能成像研究,并促进了基于控制理论的引导大脑活动的应用的发展。在这里,我们讨论了在脑图谱时代网络神经科学的新兴前沿,解决了整合多个数据流以理解从发育到健康功能再到疾病的神经转变的挑战和机遇。我们强调了通过研讨会、会议和资助计划促进跨学科机会的重要性,例如支持对这两个学科都感兴趣的学生和博士后研究员。通过将网络科学和神经科学社区聚集在一起,我们可以开发针对神经回路的新型基于网络的方法,为更深入地理解大脑及其功能铺平道路,并为网络科学提出新的挑战。