Suppr超能文献

神经元的命名:分类学理论在细胞群体研究中的应用。

The naming of neurons: applications of taxonomic theory to the study of cellular populations.

作者信息

Tyner C F

出版信息

Brain Behav Evol. 1975;12(1-2):75-96. doi: 10.1159/000124141.

Abstract

For many purposes, biologists must study large brains through groups of similar neurons, since these populations - not individual cells - are the smallest units for which exact counterparts can be recognized unequivocally across a series of brains. One who surveys singel neurons, by whatever techniques, may discern major aspects of a tissue's organization by classifying the elements studied, thereby performing an exercise in taxonomy at the cellular level. The discovery of neuronal types is best achieved by imitating the naturalist who seeks new biological species: a large sample of cells is gathered by a regular, widely effective method, and an effort is made to understand the biases in the sampling procedure; a numerous and diverse set of features is observed for each neuron encountered; and the cell sets recognized are described in agreement with the polythetic concept of natural groups. The resulting multidimensional population descriptions, the most useful of which include the temporal information available through electrophysiologic recording, may be quite powerful for testing circuit hypotheses about the large nervous system.

摘要

在许多情况下,生物学家必须通过相似神经元群体来研究大型大脑,因为这些群体——而非单个细胞——是在一系列大脑中能够明确识别出确切对应物的最小单位。无论采用何种技术来观察单个神经元的人,都可以通过对所研究的元素进行分类,从而辨别组织的主要组织结构,进而在细胞水平上进行分类学研究。发现神经元类型的最佳方法是模仿寻找新生物物种的博物学家:通过一种常规且广泛有效的方法收集大量细胞样本,并努力了解采样过程中的偏差;对遇到的每个神经元观察大量多样的特征;并且根据自然群体的多性状概念来描述所识别的细胞集。由此得到的多维群体描述,其中最有用的包括通过电生理记录获得的时间信息,对于检验关于大型神经系统的回路假说可能非常有力。

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验