Gerstein G L, Perkel D H, Subramanian K N
Brain Res. 1978 Jan 20;140(1):43-62. doi: 10.1016/0006-8993(78)90237-8.
Present-day techniques of multiple-electrode 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 is described that permits the detection and identification of such functional groups. The method is accretional, based on identification of associated neurons through interative 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 are 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.
当今的多电极技术，再加上计算机辅助分离不同神经元产生的冲动，使得能够同时记录数量超过20个的神经元组中的神经冲动时间。这反过来又使得寻找神经元功能组成为可能，这些功能组被定义为与独立神经元以相应平均速率放电相比，更倾向于几乎同时放电的子集。本文描述了一种统计技术，该技术能够检测和识别此类功能组。该方法是累积性的，基于通过对观察窗口内神经元放电的多次重合进行显著性检验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及其灵敏度指标。整个算法可用作一种筛选技术，以选择较小的神经元组进行互相关和相关的更精细时间分析，或者它本身可用于检测和表征其他统计程序无法区分的功能组。
