Analysis of between-trial and within-trial neural spiking dynamics
Analysis of between-trial and within-trial neural spiking dynamics
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DOI：
10.1152/jn.00343.2007
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发表时间：
2008-05-01
影响因子：
2.5
通讯作者：
Brown, Emery N.
中科院分区：
文献类型：
--
作者：
Czanner, Gabriela;Eden, Uri T.;Brown, Emery N.
记录多个试验中特定大脑区域的单个神经元活动，以响应相同的刺激或执行相同的行为任务，是一种常见的神经生理学方案。棘波序列的栅格图通常显示出很强的试验间和试验内动态，但是使用刺激前时间直方图(PSTH)和方差分析对这些数据进行的标准分析没有考虑试验间的动态。PSTH本身并不提供统计推断的框架。我们提出了一个状态空间广义线性模型(SS-GLM)来描述试验间和试验内神经放电动力学的点过程表示。我们的模型将PSTH作为特例。我们为模型估计、模型选择、拟合度分析和推断提供了一个框架。在对执行位置-场景关联任务的猴子记录的海马神经活动的分析中，我们演示了如何使用SS-GLM来回答常见的神经生理学问题，包括，试验之间和试验内任务特定的调节神经放电活动的性质？我们如何描述与学习相关的神经动力学？神经元的生物物理特性的时间尺度和特征是什么？我们的结果表明，SS-GLM是一种比PSTH和ANOVA更丰富的分析多个试验神经反应的工具，它提供了在栅格图中容易看到的试验间和试验内神经动力学的定量表征，以及神经元生物物理特性的不太明显的快速(1-10ms)、中间(11-20ms)和更长(>；20ms)的时间尺度特征。
Recording single-neuron activity from a specific brain region across multiple trials in response to the same stimulus or execution of the same behavioral task is a common neurophysiology protocol. The raster plots of the spike trains often show strong between-trial and within-trial dynamics, yet the standard analysis of these data with the peristimulus time histogram (PSTH) and ANOVA do not consider between-trial dynamics. By itself, the PSTH does not provide a framework for statistical inference. We present a state-space generalized linear model (SS-GLM) to formulate a point process representation of between-trial and within-trial neural spiking dynamics. Our model has the PSTH as a special case. We provide a framework for model estimation, model selection, goodness-of-fit analysis, and inference. In an analysis of hippocampal neural activity recorded from a monkey performing a location-scene association task, we demonstrate how the SS-GLM may be used to answer frequently posed neurophysiological questions including, What is the nature of the between-trial and within-trial task-specific modulation of the neural spiking activity? How can we characterize learning-related neural dynamics? What are the timescales and characteristics of the neuron's biophysical properties? Our results demonstrate that the SS-GLM is a more informative tool than the PSTH and ANOVA for analysis of multiple trial neural responses and that it provides a quantitative characterization of the between-trial and within-trial neural dynamics readily visible in raster plots, as well as the less apparent fast (1-10 ms), intermediate (11-20 ms), and longer (>20 ms) timescale features of the neuron's biophysical properties.