González Montoro Aldana M, Cao Ricardo, Espinosa Nelson, Cudeiro Javier, Mariño Jorge
Department of Mathematics, Facultad de Informática, Universidade da Coruña, Campus de Elviña s/n, 15071 A Coruña, Spain.
BMC Neurosci. 2014 Aug 12;15:96. doi: 10.1186/1471-2202-15-96.
Pairwise association between neurons is a key feature in understanding neural coding. Statistical neuroscience provides tools to estimate and assess these associations. In the mammalian brain, activating ascending pathways arise from neuronal nuclei located at the brainstem and at the basal forebrain that regulate the transition between sleep and awake neuronal firing modes in extensive regions of the cerebral cortex, including the primary visual cortex, where neurons are known to be selective for the orientation of a given stimulus. In this paper, the estimation of neural synchrony as a function of time is studied in data obtained from anesthetized cats. A functional data analysis of variance model is proposed. Bootstrap statistical tests are introduced in this context; they are useful tools for the study of differences in synchrony strength regarding 1) transition between different states (anesthesia and awake), and 2) affinity given by orientation selectivity.
An analysis of variance model for functional data is proposed for neural synchrony curves, estimated with a cross-correlation based method. Dependence arising from the experimental setting needs to be accounted for. Bootstrap tests allow the identification of differences between experimental conditions (modes of activity) and between pairs of neurons formed by cells with different affinities given by their preferred orientations. In our test case, interactions between experimental conditions and preferred orientations are not statistically significant.
The results reflect the effect of different experimental conditions, as well as the affinity regarding orientation selectivity in neural synchrony and, therefore, in neural coding. A cross-correlation based method is proposed that works well under low firing activity. Functional data statistical tools produce results that are useful in this context. Dependence is shown to be necessary to account for, and bootstrap tests are an appropriate method with which to do so.
神经元之间的成对关联是理解神经编码的关键特征。统计神经科学提供了估计和评估这些关联的工具。在哺乳动物大脑中，激活的上行通路起源于位于脑干和基底前脑的神经核，这些神经核调节大脑皮层广泛区域（包括初级视觉皮层）中睡眠和清醒神经元放电模式之间的转换，在初级视觉皮层中，已知神经元对给定刺激的方向具有选择性。在本文中，研究了从麻醉猫获得的数据中神经同步性随时间的估计。提出了一种功能数据分析方差模型。在此背景下引入了自助统计检验；它们是研究同步强度差异的有用工具，这些差异涉及1）不同状态（麻醉和清醒）之间的转换，以及2）由方向选择性给出的亲和力。
针对神经同步曲线，提出了一种基于功能数据的方差分析模型，该模型通过基于互相关的方法进行估计。需要考虑实验设置产生的依赖性。自助检验允许识别实验条件（活动模式）之间以及由具有不同偏好方向亲和力的细胞形成的神经元对之间的差异。在我们的测试案例中，实验条件和偏好方向之间的相互作用在统计上不显著。
结果反映了不同实验条件的影响，以及神经同步中方向选择性的亲和力，因此也反映了神经编码中的情况。提出了一种基于互相关的方法，该方法在低放电活动下效果良好。功能数据统计工具产生的结果在此背景下很有用。依赖性被证明是需要考虑的，而自助检验是一种合适的方法。
