Using Drosophila Olfactory Navigation to Study Principles of Motor Encoding

项目摘要/摘要 目标导向的动作通常由较短的随机运动元素组成。马达电路是如何 将感官决定的目标转化为一系列随机的运动动作的组织尚不清楚。在这里，我 建议在遗传模式生物黑腹果蝇中利用嗅觉导航行为 在一个复杂的、以目标为导向的任务中确定电机控制的电路和计算基础。 苍蝇嗅觉导航是一种由较短的随机图案组成的高度健壮的行为。嗅觉导航 包括三个阶段。在基线阶段，苍蝇以随机的方式探索它们的环境。当呈现时 带着一种令人垂涎的气味，苍蝇会在逆风方向飞行。在气味抵消时，苍蝇会完成类似搜索的行为， 由高速的角速度运动组成。每个阶段都具有两个可靠的分量(迎风方向， 增加角速度)和随机分量(转弯和奔跑的精确计时)。我们的实验室有 开发了一个高吞吐量的范例，其中这三个阶段可以重复进行 呈现诱人的气味，或通过呈现虚构的光遗传气味。大型数据集包括 用这一范式可以得到的是既适用于计算分析又适用于遗传分析的。 在我的第一个目标中，我将执行嗅觉导航行为的计算分析，确定 气味呈现或戒断后行为调节的时间尺度，以及分解苍蝇 轨迹变成了一系列的行为主题。基于我的主题分析，我将构建一个马尔科夫模型 它试图复制导航行为的复杂统计数据，并理解随机的 导航元素被串联在一起，以产生可靠的目标查找。在第二个目标中，我将使用基因 沉默和激活以识别参与行为主题和 第一个目标中确定的时间结构。神经递质将运动信息从大脑传递到腹神经 脊髓，类似于脊椎动物中的神经元，将信息从大脑传递到脊髓。这一分析 将使我能够获得一个相当完整的马达电路的电路图，这有助于嗅觉导航。 最后，在我的第三个目标中，我将确定感觉和运动信息的哪些特征编码在 特定域名系统的活动。目前，在果蝇中存在两种运动编码的观点。一些研究支持 根据行为上下文传递运动信息的概念，而其他人则建议它们编码为 特定的动作，与感官驱动无关。嗅觉导航，由变化的时代组成 刺激和行为目标，准备决定不同感官起源或行为的运动 上下文被编码在马达电路中。使用闭合环路行为设备，我将从SELECT DNS成像 在嗅觉导航过程中，并将活动与行为主题和导航阶段相关联。一起， 这些实验将有助于揭示电机编码的原理，这可能有助于理解 大脑在受伤后能够恢复对运动的控制。

Project Summary/Abstract Goal directed actions are often composed of shorter stochastic motor elements. How motor circuits are organized to translate a sensory-determined goal into a set of stochastic motor actions is unclear. Here I propose to use olfactory navigation behavior in the genetic model organism Drosophila melanogaster to identify the circuitry and computational basis of motor control in a complex, goal-oriented task. Fly olfactory navigation is a highly robust behavior composed of shorter, stochastic motifs. Olfactory navigation involves three stages. At baseline, flies explore their environment in a stochastic fashion. When presented with an appetitive odor, flies orient and run upwind. At odor offset, flies complete a search-like behavior, consisting of high angular velocity movements. Each phase has both reliable components (upwind orientation, increased angular velocity) and stochastic components (the precise timing of turns and runs). Our lab has developed a high-throughput paradigm in which these three phases can be elicited repeatedly either though presentation of an attractive odor, or through presentation of a fictive optogenetic odor. The large datasets I can obtain with this paradigm are amenable to both computational and genetic analysis. In my first Aim, I will perform a computational analysis of olfactory navigation behavior, identifying the timescales at which behavior is modulated following odor presentation or withdrawal, and decomposing fly trajectories into a series of behavioral motifs. Based on my motif analysis I will construct a Markovian model that seeks to reproduce the complex statistics of navigation behavior, and to understand how the stochastic elements of navigation are concatenated to produce reliable goal-finding. In the second Aim, I will use genetic silencing and activation to identify descending neurons (DNs) that contribute to the behavior motifs and temporal structure identified in the first Aim. DNs carry motor information from the brain to the ventral nerve cord, similar to neurons in the vertebrate that carry information from the brain to the spinal cord. This analysis will allow me to obtain a fairly complete circuit map of the motor circuitry the contributes to olfactory navigation. Finally, in my third Aim, I will determine what features of sensory and motor information are encoded in the activity of particular DNs. Currently, two views of motor encoding exist in the fruit fly. Some studies support the notion that DNs relay motor information depending on behavioral context, while others suggest they encode for specific movements, regardless of sensory driver. Olfactory navigation, composed of epochs of varying stimulus and behavioral goal, is poised to determine how movements of different sensory origin or behavioral context are encoded in motor circuitry. Using a closed loop behavioral apparatus, I will image from select DNs during olfactory navigation and correlate activity with both behavioral motifs and navigational phase. Together, these experiments will help to uncover principles of motor encoding, which could aid in understanding how the brain is able to regain motor control after injury.