Standard analysis of neuronal functions assesses the temporal correlation between animal behaviors and neuronal activity by aligning spike trains with the timing of a specific behavioral event, e.g. visual cue. However, spike activity is often involved in information processing dependent on a relative phase between two consecutive events rather than a single event. Nevertheless, less attention has so far been paid to such temporal features of spike activity in relation to two behavioral events. Here, we propose 'Phase-Scaling analysis' to simultaneously evaluate the phase-locking and scaling to the interval between two events in task-related spike activity of individual neurons. This analysis method can discriminate conceptual 'scaled'-type neurons from 'non-scaled'-type neurons using an activity variation map that combines phase-locking with scaling to the interval. Its robustness was validated by spike simulation using different spike properties. Furthermore, we applied it to analyzing actual spike data from task-related neurons in the primary visual cortex (V1), posterior parietal cortex (PPC), primary motor cortex (M1), and secondary motor cortex (M2) of behaving rats. After hierarchical clustering of all neurons using their activity variation maps, we divided them objectively into four clusters corresponding to non-scaled-type sensory and motor neurons, scaled-type neurons including sustained and ramping activities, etc. Cluster/subcluster compositions for V1 differed from those of PPC, M1, and M2. The V1 neurons showed the fastest functional activities among those areas. Our method was also applicable to determine temporal 'forms' and the latency of spike activity changes. These findings demonstrate its utility for characterizing neurons.

中文翻译：

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基于锁相和缩放到两个行为事件之间的间隔来表征神经元的尖峰分析方法

神经元功能的标准分析通过将尖峰序列与特定行为事件（例如视觉提示）的时间对齐来评估动物行为和神经元活动之间的时间相关性。然而，尖峰活动通常涉及依赖于两个连续事件之间的相对相位而不是单个事件的信息处理。然而，到目前为止，很少有人关注与两种行为事件相关的尖峰活动的这种时间特征。在这里，我们提出了“相位缩放分析”，以同时评估锁相和缩放到单个神经元的任务相关尖峰活动中两个事件之间的间隔。这种分析方法可以区分概念“缩放”类型的神经元和“非缩放” 型神经元使用活动变化图，将锁相与缩放到间隔相结合。通过使用不同尖峰特性的尖峰模拟验证了其稳健性。此外，我们将其应用于分析来自行为大鼠的初级视觉皮层 (V1)、后顶叶皮层 (PPC)、初级运动皮层 (M1) 和次级运动皮层 (M2) 中与任务相关的神经元的实际尖峰数据。在使用它们的活动变化图对所有神经元进行层次聚类后，我们客观地将它们分为四个簇，分别对应于非标度型感觉和运动神经元、标度型神经元包括持续和斜坡活动等。 V1 的簇/子簇组成不同来自 PPC、M1 和 M2。V1 神经元在这些区域中显示出最快的功能活动。我们的方法也适用于确定时间“形式”和尖峰活动变化的延迟。这些发现证明了它在表征神经元方面的效用。