Integrating visual counterevidence to detect self-motion in a small visual circuit

整合视觉反证以检测小型视觉回路中的自我运动

• 批准号: 10388229
• 负责人: Damon Alistair Clark
• 金额: $ 39万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-04-01 至 2026-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10388229
• 关键词: Algorithms; Anatomy; Animals; Behavior; Behavioral; Biological Models; Brain; Brain Diseases; Calcium; Cues; Custom; Detection; Diagnosis; Drosophila genus; Eye; Eye Movements; Frequencies; Genetic; Geometry; Goals; Head; Human; Image; Impairment; Individual; Lead; Left; Mammals; Measurement; Measures; Modeling; Motion; Movement; Mus; Neurons; Neurotransmitters; Pathologic Nystagmus; Pathway interactions; Pattern; Primates; Process; Property; Psychophysics; Research; Role; Rotation; Sensory; Signal Transduction; Stimulus; Testing; Visual; Visual Fields; Visual Motion; Visual system structure; Walking; Weight; Work; behavior measurement; detection platform; driving behavior; experimental study; feature detection; fly; gaze; nervous system disorder; neural circuit; neuromechanism; novel; novel strategies; optic flow; relating to nervous system; response; scaffold; spatiotemporal; tool; two-photon; visual motor; visual processing; visual stimulus; voltage

Animals perceive optic flow and use it to guide a range of navigational behaviors, such as stabilizing body trajectories and eye movements. In this project, we propose to dissect how different visual cues are combined to guide stabilizing behaviors, and how these cues serve as evidence for and against an animal’s self-motion. We will investigate these computations in the fruit fly Drosophila, which allows us to use powerful genetic tools to define the roles of individual neurons in neural computations. With these tools, our research will (a) characterize the algorithms that combine types of visual evidence, (b) identify the circuits that encode and combine these cues, and (c) determine how neurons perform these computations. This work is significant for two reasons. First, our studies will examine how the fly integrates visual evidence both for and against its own self-motion. This form of counterevidence integration cannot be accounted for by current models of optic flow detection. Thus, this project will establish new approaches to studying how animals estimate their own self-motion. Second, the types of visual evidence for and against self-motion are constrained by the geometry of the visual world. Because of this common geometry and parallels in visual processing between flies and mammals, it is likely that mammalian visual systems make use of algorithms similar to those we will find in the fly. Thus, analysis in the compact fly brain will uncover principles for understanding these computations in the brains of larger animals. In our research, we will combine genetic tools and behavioral measurements to investigate three questions: How do different visual cues interact in stabilizing the fly’s heading? What circuitry is required for the different types of cues? And how do the circuits process and integrate these cues to generate behavior? These questions lead to the three aims of our research. Aim 1 characterizes the algorithm that integrates evidence for and against the fly’s self-motion and guides its turning behavior. Behavioral measurements with targeted visual stimuli will constrain or rule out potential models. Aim 2 identifies neurons required to integrate visual counterevidence into turning behavior. We will use genetic tools to silence specific neurons in the visual system in order to identify neurons required for this computation. Aim 3 measures functional response properties of visual neurons in the circuits that encode and integrate these visual cues. We will use measurements of neuron responses to stimuli to test hypotheses for how these neurons combine signals to generate the observed motion signals and behaviors. On completion, these studies will result in a detailed understanding of the algorithms and neural mechanisms that integrate different visual cues to stabilize heading in flies. This will provide a template for understanding how visual evidence is combined to estimate self-motion in other animals as well.

动物感知光流并利用它来指导一系列导航行为，例如稳定身体 轨迹和眼球运动。在这个项目中，我们建议剖析不同的视觉线索是如何结合在一起的 来指导稳定行为，以及这些线索如何作为支持和反对动物自我运动的证据。 我们将在果蝇中研究这些计算，这使我们能够使用强大的遗传工具 来定义单个神经元在神经计算中的作用。有了这些工具，我们的研究将（a） 表征组合联合收割机类型的视觉证据的算法，（B）识别编码的电路， 联合收割机，以及（c）确定神经元如何执行这些计算。这项工作对于 两个原因第首先，我们的研究将考察苍蝇如何整合支持和反对自己的视觉证据。 自我运动这种形式的反证积分不能用目前的光流模型来解释 侦测因此，该项目将建立新的方法来研究动物如何估计自己的自我运动。第二，支持和反对自我运动的视觉证据的类型受到 视觉世界。由于这种共同的几何形状和苍蝇和苍蝇之间视觉处理的相似之处， 哺乳动物，很可能哺乳动物的视觉系统使用的算法类似于那些我们将发现在 飞翔紧紧因此，在紧凑的苍蝇大脑中的分析将揭示理解这些计算的原理。 大型动物的大脑在我们的研究中，我们将联合收割机结合遗传工具和行为测量， 研究三个问题：不同的视觉线索如何相互作用，以稳定苍蝇的航向？什么电路 是不同类型的线索所必需的吗神经回路如何处理和整合这些线索， 产生行为？这些问题引出了我们研究的三个目的。目标1描述了算法的特点 它整合了支持和反对苍蝇自我运动的证据，并指导其转向行为。行为 具有目标视觉刺激的测量将限制或排除潜在的模型。Aim 2识别神经元 需要将视觉反证整合到转向行为中。我们会用遗传学的方法来抑制 视觉系统中的神经元，以识别该计算所需的神经元。目标3措施 编码和整合这些视觉线索的电路中的视觉神经元的功能反应特性。我们 将使用神经元对刺激的反应的测量来测试这些神经元如何联合收割机的假设 信号以生成观察到的运动信号和行为。这些研究完成后， 详细了解算法和神经机制，整合不同的视觉线索，以稳定 飞向苍蝇。这将为理解视觉证据如何结合起来进行估计提供一个模板 其他动物的自我运动也是如此。