IDENTIFICATION OF FUNCTIONALLY RELATED NEURAL ASSEMBLIES
IDENTIFICATION OF FUNCTIONALLY RELATED NEURAL ASSEMBLIES
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DOI：
10.1016/0006-8993(78)90237-8
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发表时间：
1978-01-01
期刊：
影响因子：
2.9
通讯作者：
SUBRAMANIAN, KN
中科院分区：
文献类型：
--
作者：
GERSTEIN, GL;PERKEL, DH;SUBRAMANIAN, KN
当今的多电极记录技术以及计算机辅助分离不同神经元产生的脉冲允许同时记录数量超过 20 个神经元组中的神经脉冲时序。这反过来又使得搜索神经元的功能组成为可能，神经元的功能组被定义为倾向于几乎同时激发的子集，其频率显着高于以相应平均速率的独立神经元。描述了一种统计技术，可以检测和识别此类功能组。该方法是累加性的，基于通过对观察窗口内神经元放电的多个巧合进行显着性测试的迭代应用来识别相关神经元。通过神经网络的计算机模拟提供了该方法的操作示例以及关于其灵敏度的指示。整个算法可以用作筛选技术来选择较小的神经元组进行互相关和相关的更细粒度的时间分析，或者它本身可以用于检测和表征其他统计程序无法区分的功能组。
Present-day techniques of multiple-electrode recording together with computer-aided separation of impulses arising from different neurons permit the simultaneous recording of nerve-impulse timings in sets of neurons exceeding 20 in number. This in turn makes it feasible to search for functional groups of neurons, defined as subsets that tend to fire in near simultaneity significantly more often than would independent neurons at corresponding mean rates. A statistical technique was described that permitted the detection and identification of such functional groups. The method was accretional, based on identification of associated neurons through iterative application of a significance test on multiple coincidences of neuronal firings within an observational window. Examples of the operation of the method and indications as to its sensitivity were furnished through computer simulations of neural networks. The entire algorithm may be used as a screening technique to select smaller groups of neurons for cross-correlational and related finer-grained temporal analyses, or it may be used in its own right to detect and characterize functional groups that are not distinguishable by other statistical procedures.