对数动态大脑：偏态分布如何影响网络运作。
The log-dynamic brain: how skewed distributions affect network operations.
机构信息
1] New York University Neuroscience Institute, New York University Langone Medical Center, New York, New York 10016, USA. [2] Center for Neural Science, New York University, New York, New York 10003, USA.
1] New York University Neuroscience Institute, New York University Langone Medical Center, New York, New York 10016, USA. [2] Allen Institute for Brain Science, 551 North 34th Street, Seattle, Washington 98103, USA.
出版信息
Nat Rev Neurosci. 2014 Apr;15(4):264-78. doi: 10.1038/nrn3687. Epub 2014 Feb 26.
We often assume that the variables of functional and structural brain parameters - such as synaptic weights, the firing rates of individual neurons, the synchronous discharge of neural populations, the number of synaptic contacts between neurons and the size of dendritic boutons - have a bell-shaped distribution. However, at many physiological and anatomical levels in the brain, the distribution of numerous parameters is in fact strongly skewed with a heavy tail, suggesting that skewed (typically lognormal) distributions are fundamental to structural and functional brain organization. This insight not only has implications for how we should collect and analyse data, it may also help us to understand how the different levels of skewed distributions - from synapses to cognition - are related to each other.
我们通常假设功能和结构脑参数的变量 - 例如突触权重、单个神经元的发射率、神经元群体的同步放电、神经元之间的突触接触数量和树突末梢的大小 - 具有钟形分布。然而，在大脑的许多生理和解剖学水平上，许多参数的分布实际上是强烈偏态的，具有重尾，这表明偏态（通常是对数正态）分布是结构和功能脑组织的基础。这一见解不仅对我们应该如何收集和分析数据有影响，而且可能有助于我们了解从突触到认知的不同偏态分布水平是如何相互关联的。
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