Unitary events in multiple single-neuron spiking activity:: II.: Nonstationary data
Unitary events in multiple single-neuron spiking activity:: II.: Nonstationary data
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DOI：
10.1162/089976602753284464
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发表时间：
2002-01-01
影响因子：
2.9
通讯作者：
Aertsen, A
中科院分区：
文献类型：
--
作者：
Grün, S;Diesmann, M;Aertsen, A
为了检测同时记录的神经元尖峰活动中的功能组（细胞组装）的成员，我们采用了广泛使用的操作定义，即在一个共同的组装中的成员资格表示在几乎同时的尖峰活动。酉事件分析是最近开发的一种统计方法，用于检测平稳数据中同步尖峰活动的显著发生（参见本期的配套文章）。检测单一事件的技术是基于基本过程在时间上是平稳的假设。然而，这一要求在神经元数据中通常得不到满足。在这里，我们描述了一种方法，适当的标准化率的变化：单一的事件移动窗口分析（UEMWA）。通过沿数据沿着滑动恒定宽度的窗口，在重叠的时间段中单独执行对单一事件的分析。在每个窗口中，假设平稳性。性能和灵敏度证明通过使用模拟的尖峰列车的独立放电的神经元，重合事件插入。如果皮层神经元动态地组织成功能群，那么几乎同时发生的锋电位活动应该是随时间变化的，并且与行为和刺激有关。UEMWA还考虑了这些潜在的有趣的非平稳性，并允许及时定位它们。新方法的潜力说明了从清醒的，行为猴子的额叶和运动皮层区域的多个单单元记录的结果。
In order to detect members of a functional group (cell assembly) in simultaneously recorded neuronal spiking activity, we adopted the widely used operational definition that membership in a common assembly is expressed in near-simultaneous spike activity. Unitary event analysis, a statistical method to detect the significant occurrence of coincident spiking activity in stationary data, was recently developed (see the companion article in this issue). The technique for the detection of unitary events is based on the assumption that the underlying processes are stationary in time. This requirement, however, is usually not fulfilled in neuronal data. Here we describe a method that properly normalizes for changes of rate: the unitary events by moving window analysis (UEMWA). Analysis for unitary events is performed separately in overlapping time segments by sliding a window of constant width along the data. In each window, stationarity is assumed. Performance and sensitivity are demonstrated by use of simulated spike trains of independently firing neurons, into which coincident events are inserted. If cortical neurons organize dynamically into functional groups, the occurrence of near-simultaneous spike activity should be time varying and related to behavior and stimuli. UEMWA also accounts for these potentially interesting nonstationarities and allows locating them in time. The potential of the new method is illustrated by results from multiple single-unit recordings from frontal and motor cortical areas in awake, behaving monkey.