Coding properties of Vibrissal-Responsive Trigeminal Ganglion Neurons

 描述（由申请人提供）：我们看到，因为视网膜神经节细胞对光有反应。我们听到声音是因为螺旋神经节细胞对声音有反应。我们有感觉是因为初级躯体感觉神经元对“触摸”有反应。什么是“触摸“？“虽然光和声音可以通过物理参数（振幅，频率，相位和极化）来表征，但触摸的机制以及初级感觉神经元编码触摸参数的方式在很大程度上是不可量化的。这是整个躯体感觉领域中的一个明显差距，它的出现是因为力学很难量化。为了缩小这一差距，我们将使用大鼠触须（胡须）系统作为模型，直接将初级感觉神经元的反应与量化的触觉力学联系起来。随着啮齿类动物在遗传学和光遗传学研究中的使用越来越多，啮齿类动物触须阵列已经成为研究触觉和感觉运动整合的越来越重要的模型。在过去的几年中，我们的实验室在表征触须力学方面取得了快速进展，我们现在处于独特的位置，以确定三维触须偏转和振动是如何在三叉神经节（Vg）的初级感觉神经元的放电模式中表现出来的。我们调查的中心目标是预测Vg神经元在接触和非接触搅拌的反应，通过适当地结合3D动态和准静态模型的机械信号。我们的三个目标从大鼠的外部向内移动，从胡须到卵泡，再到Vg神经元。在目标1中，我们将开发晶须的机械编码模型，量化接触和非接触搅拌过程中触须基部的3D机械信号。在目标2中，这些模型将用于预测毛囊内机械感受器的反应，从而根据它们执行的机械转换来识别Vg神经元的类别。最后，在目标3中，我们将量化清醒动物自然搅拌行为期间Vg神经元的反应。利用目标2中确定的细胞类别，并与目标1的建模一致，我们将测试以下假设：在搅拌过程中，Vg响应与机械信号的线性相关性比与搅拌行为的几何形状和运动学的线性相关性更高。拟议的工作将是第一个记录清醒行为动物的Vg神经元，同时充分表征接触和非接触搅拌期间的机械输入。我们的目标是解决大部分的“编码问题”的触须三叉神经系统。解决这个问题将提供一个更好的理解什么是Vg尖峰“意味着”三叉神经系统的更中央的阶段，包括感觉丘脑和桶皮质。

 DESCRIPTION (provided by applicant): We see because retinal ganglion cells respond to light. We hear because spiral ganglion cells respond to sound. We feel because primary somatosensory neurons respond to "touch." But what is "touch?" Whereas light and sound can be characterized by physical parameters (amplitude, frequency, phase, and polarization), the mechanics of touch, and the manner in which primary sensory neurons encode the parameters of touch, are largely unquantified. This is a glaring gap within the entire field of somatosensation, and it occurs because mechanics are difficult to quantify. To close this gap we will use the rat vibrissal (whisker) system as a model to directly relate the responses of primary sensory neurons to the quantified mechanics of touch. Paralleling the increased use of rodents in genetic and optogenetic research, the rodent vibrissal array has become an increasingly important model for the study of touch and sensorimotor integration. In the past few years, our laboratory has made rapid progress in characterizing vibrissal mechanics, and we are now uniquely positioned to determine how 3D whisker deflections and vibrations are represented in the firing patterns of primary sensory neurons of the trigeminal ganglion (Vg) during natural whisking behavior. The central goal of our investigation is to predict the responses of Vg neurons during both contact and non-contact whisking by appropriately combining 3D dynamic and quasistatic models of mechanical signals. Our three aims move from the outside of the rat inwards, from whisker, to follicle, to Vg neurons. In Aim 1, we will develop models of mechanical coding by the whisker, quantifying the 3D mechanical signals at the vibrissal base during both contact and non-contact whisking. In Aim 2, these models will be used to predict responses of mechanoreceptors within the follicle and thus to identify classes of Vg neurons based on the mechanical transformation they perform. Finally, in Aim 3 we will quantify the responses of Vg neurons during natural whisking behavior in awake animals. Exploiting the cell classes identified in Aim 2, and consistent with the modeling of Aim1, we will test the hypothesis that Vg responses are more linearly correlated with mechanical signals during whisking than they are with the geometry and kinematics of whisking behavior. The proposed work will be the first to record from Vg neurons in awake behaving animals while fully characterizing the mechanical input during both contact and non-contact whisking. We aim to solve a large portion of the "coding problem" for the vibrissal-trigeminal system. Solving this problem will provide a better understanding of what a Vg spike "means" for more central stages of the trigeminal system, including sensory thalamus and barrel cortex.