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检测动物神经网络中的方格结构。

Detecting Square Grid Structure in an Animal Neuronal Network.

作者信息

Friedman Robert

机构信息

Retired from Department of Biological Sciences, University of South Carolina, Columbia, SC 29208, USA.

出版信息

NeuroSci. 2022 Feb 3;3(1):91-103. doi: 10.3390/neurosci3010007. eCollection 2022 Mar.

Abstract

An animal neural system ranges from a cluster of a few neurons to a brain of billions. At the lower range, it is possible to test each neuron for its role across a set of environmental conditions. However, the higher range requires another approach. One method is to disentangle the organization of the neuronal network. In the case of the entorhinal cortex in a rodent, a set of neuronal cells involved in spatial location activate in a regular grid-like arrangement. Therefore, it is of interest to develop methods to find these kinds of patterns in a neural network. For this study, a square grid arrangement of neurons is quantified by network metrics and then applied for identification of square grid structure in areas of the fruit fly brain. The results show several regions with contiguous clusters of square grid arrangements in the neural network, supportive of specialization in the information processing of the system.

摘要

动物神经系统的范围从由少数神经元组成的神经簇到拥有数十亿个神经元的大脑。在较低层次上,可以在一组环境条件下测试每个神经元的作用。然而,在较高层次上则需要另一种方法。一种方法是解开神经网络的组织结构。就啮齿动物的内嗅皮层而言,一组参与空间定位的神经元细胞以规则的网格状排列激活。因此,开发在神经网络中发现这类模式的方法是很有意义的。在本研究中,通过网络指标对神经元的方形网格排列进行量化,然后将其应用于果蝇大脑区域方形网格结构的识别。结果显示,神经网络中有几个区域存在连续的方形网格排列簇,这支持了该系统在信息处理方面的专业化。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f7f3/11523746/14e8ea73c0c4/neurosci-03-00007-g001.jpg

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