用对数线性点过程模型评估多个神经元锋电位序列的同步性
Assessment of synchrony in multiple neural spike trains using loglinear point process models
摘要：
神经尖峰序列是细胞膜上电压的一系列非常短暂的跳跃，是点过程方法学发展的激励应用之一。早期的工作需要假设平稳性，但当代的实验经常使用时变刺激并产生时变神经反应。最近，许多统计方法已经开发了非平稳神经点过程数据。人们对识别同步性也很感兴趣，这意味着在记录的时间尺度上，两个或多个神经元之间的事件几乎是同时发生的。一个自然的统计方法是离散时间，使用短的时间箱，并引入对数线性模型的依赖神经元之间，但以前使用的对数线性建模技术已假定平稳。我们引入了一类简洁而强大的时变对数线性模型：（a）允许个体神经元效应（主效应）涉及时变强度;（b）还允许个体神经元效应涉及由于过去尖峰的自协变效应（历史效应）;（c）假设过度同步效应（交互效应）不依赖于历史;（d）假设所有效应随时间平滑变化。
英文摘要：
Neural spike trains, which are sequences of very brief jumps in voltage across the cell membrane, were one of the motivating applications for the development of point process methodology. Early work required the assumption of stationarity, but contemporary experiments often use time-varying stimuli and produce time-varying neural responses. More recently, many statistical methods have been developed for nonstationary neural point process data. There has also been much interest in identifying synchrony, meaning events across two or more neurons that are nearly simultaneous at the time scale of the recordings. A natural statistical approach is to discretize time, using short time bins, and to introduce loglinear models for dependency among neurons, but previous use of loglinear modeling technology has assumed stationarity. We introduce a succinct yet powerful class of time-varying loglinear models by (a) allowing individual-neuron effects (main effects) to involve time-varying intensities; (b) also allowing the individual-neuron effects to involve autocovariation effects (history effects) due to past spiking, (c) assuming excess synchrony effects (interaction effects) do not depend on history, and (d) assuming all effects vary smoothly across time.
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