ASSESSMENT OF SYNCHRONY IN MULTIPLE NEURAL SPIKE TRAINS USING LOGLINEAR POINT PROCESS MODELS.
ASSESSMENT OF SYNCHRONY IN MULTIPLE NEURAL SPIKE TRAINS USING LOGLINEAR POINT PROCESS MODELS.
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DOI：
10.1214/10-aoas429
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发表时间：
2011-06-01
期刊：
影响因子：
--
通讯作者：
Loh WL
中科院分区：
文献类型：
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作者：
Kass RE;Kelly RC;Loh WL
神经尖峰序列是细胞膜上电压的非常短暂的跳跃序列，是点过程方法论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。使用麻醉猴的初级视皮层的数据，我们给出了两个例子，其中同步尖峰的速率不能用个体神经元效应中与刺激相关的变化来解释。在一个例子中，当慢波“向上”状态被考虑为历史效应时，过度同步消失，而在第二个例子中，它没有。标准点过程理论明确排除了同步事件。为了证明我们使用的连续时间的方法，我们引入了一个框架，结合同步事件，并提供连续时间对数线性点过程近似离散时间对数线性模型。
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. Using data from primary visual cortex of an anesthetized monkey we give two examples in which the rate of synchronous spiking can not be explained by stimulus-related changes in individual-neuron effects. In one example, the excess synchrony disappears when slow-wave “up” states are taken into account as history effects, while in the second example it does not. Standard point process theory explicitly rules out synchronous events. To justify our use of continuous-time methodology we introduce a framework that incorporates synchronous events and provides continuous-time loglinear point process approximations to discrete-time loglinear models.