Neuronal mechanisms of multiplication and invariance

描述：(申请人提供) 这项研究的长期目标是了解生物物理学 个体整合时变感觉刺激的机制 神经元。目前的目标是提供详细的生物物理解释 单个神经元如何将两个输入相乘，以及它们如何实现不变性 某些刺激属性。乘法被认为与许多神经 计算，如从视觉图像中提取运动信息，在 脊椎动物和无脊椎动物的神经系统。不变性是一种属性 常见于对刺激有选择性反应的高级神经元 独立于其上下文的功能。目前，人们对此知之甚少 这些计算是如何由神经元完成的。这些问题将是 在拥有神经元的蝗虫的视觉系统中进行了研究， 小叶巨型运动探测器(LGMD)，对隐约可见的物体做出反应 冲向这只动物的碰撞路线。这个神经元执行乘法运算 两个不同的输入撞击其树枝晶和展品之间的操作 对隐约可见对象的许多属性不变的响应。许多 LGMD的特性使其成为生物物理研究的有利对象。这个 该项目的具体目标是描述激活的时间过程 影响LGMD的两种输入及其在树突中的整合 树。此外，细胞的解剖，其固有的膜特性 眼睛的几何形状将被特征化并用于建立一个模型 细胞及其对隐约可见的刺激的反应。应用的技术将 包括从突触前神经元到LGMD多电极记录， 细胞内记录、药物操作和隔室 模特儿。模型和实验数据将被用来识别 该神经元增殖和不变性的生物物理机制。 因为在脊椎动物的中枢神经系统中也发现了类似的计算， 该项目预计将促进对如何 实现了神经信息的乘法和不变性 正在处理。

DESCRIPTION:(provided by applicant) The long term objective of this research is to understand the biophysical mechanisms by which time varying sensory stimuli are integrated in individual neurons. The immediate goal is to provide detailed biophysical explanations of how individual neurons multiply two inputs and how they implement invariance to certain stimulus attributes. Multiplication has been implicated in many neural computations, like the extraction of motion information from visual images, in both vertebrate and invertebrate nervous systems. Invariance is an attribute commonly found in higher order neurons that respond selectively to a stimulus feature independently of its context. Currently, there is little understanding of how these computations are accomplished by neurons. These issues will be investigated in the visual system of the locust, which possesses a neuron, the lobula giant movement detector (LGMD), that responds to objects looming on a collision course towards the animal. This neuron implements a multiplication operation between two distinct inputs impinging on its dendrites and exhibits responses that are invariant to many attributes of the looming object. Many features of LGMD make it a favorable subject for biophysical studies. The specific aims of the project are to characterize the time-course of activation of the two inputs impinging on LGMD and their integration within its dendritic tree. Additionally, the anatomy of the cell, its intrinsic membrane properties and the geometry of the eye will be characterized and used to build a model of the cell and its response to looming stimuli. The techniques applied will include multielectrode recordings from presynaptic neurons to LGMD, intracellular recordings, pharmacological manipulations and compartmental modeling. The model and experimental data will be used to identify the biophysical mechanisms underlying multiplication and invariance in this neuron. Because similar computations are found in vertebrate central nervous systems, this project is expected to advance the general understanding of how multiplication and invariance are implemented for neural information processing.