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

动物能感知光流，并用它来引导一系列导航行为，如稳定身体 轨迹和眼球运动。在这个项目中，我们打算剖析不同的视觉线索是如何组合在一起的 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。 我们将在果蝇身上研究这些计算，这使我们能够使用强大的遗传工具 以确定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) 描述组合视觉证据类型的算法的特征，(B)识别编码和 将这些线索结合起来，(C)确定神经元如何执行这些计算。这项工作具有重要意义 有两个原因。首先，我们的研究将检查苍蝇如何整合支持和反对自己的视觉证据 自动运动。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，这个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。其次，支持和反对自主运动的视觉证据的类型受到几何形状的限制 视觉世界。由于苍蝇和苍蝇在视觉处理上的这种共同的几何和相似之处 哺乳动物的视觉系统使用的算法很可能与我们在 飞。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 较大动物的大脑。在我们的研究中，我们将结合遗传工具和行为测量来 调查三个问题：不同的视觉线索如何在稳定苍蝇的头上相互作用？什么电路 是不同类型的线索所必需的吗？这些电路是如何处理和整合这些线索的 产生行为？这些问题引出了我们研究的三个目标。目标1描述了该算法 它整合了支持和反对苍蝇自我运动的证据，并指导它的转向行为。行为 有针对性的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 需要将视觉反证与转向行为相结合。我们将使用基因工具让特定的人沉默 视觉系统中的神经元，以便识别这一计算所需的神经元。目标3措施 视觉神经元在编码和整合这些视觉线索的电路中的功能反应特性。我们 将使用对神经元对刺激的反应的测量来测试这些神经元如何组合的假设 产生观察到的运动信号和行为的信号。完成后，这些研究将产生一个 详细了解整合不同视觉线索以稳定状态的算法和神经机制 飞向苍蝇。这将为理解视觉证据是如何组合以进行估计提供模板 其他动物的自我运动也是如此。

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.