Neural representation of mating partners by male C. elegans

项目摘要 要了解神经回路如何创造动物行为，需要了解整个系统的活动模式 将感觉体验与运动活动联系起来，所有这些都在由其驱动的全套反馈环路中 在自然执行和执行过程中，运动决策调节动物对自己和外部世界的感知 不受约束的行为。机械论的理解进一步要求对全系统活动进行解释 在所有相关神经元的连接性、突触和细胞属性方面的模式。建模需要 全面映射感觉输入和运动输出的显著维度，以及 维度神经活动模式被适当地投射，通过决策和 神经系统和运动系统，变成任何动物被观察到的更少的维度 行为。这样的模型是通过使用动物来促进的，在动物中可以映射整个大脑和运动神经回路，并 以全分子和细胞分辨率进行讯问。在这里，我们将利用雄性C. 优雅就是这样的典范。线虫的交配行为是一项极其重要和有目标的任务 这发生在自然环境中，并在实验室中强有力地执行。雄性使用一种特殊的环路 ~100个神经元-感觉神经元、中间神经元、神经调节神经元和运动神经元-所有这些都包含在 雄性尾神经节定位两性人，沿着两性人的身体定位外阴，并启动和 完成人工授精。一组不同的机械感觉、化学感觉和信息素感觉神经元是 用于识别两性人身体的形状、质地和化学特征。有几个神经元 专门用于检测两性人身体上的基准点。雄性隐含地使用内部的 表示两性人的大小、形状和可预测的行为，以肯定地识别 两性人，推断它自己沿着两性人的位置，并执行最优移动策略以 保持与两性人的接触，找到并穿透外阴。现在可以记录下神经细胞 雄性尾部一整套神经元的活动具有高时间分辨率和完整的细胞 决议。我们将结合实验，现代数据分析和建模方法，以及细胞和分子 基因扰动，以阐明组织交配行为的一整套感觉运动事件。我们会 开发一个真实的电路模型，将可观察到的行为算法与连接性相结合 以及雄性尾神经节的活动模式。我们将应用基因工具来识别和阐明每一种感觉 神经元类型，以及它如何影响下游中间神经元和运动神经元做出决策的各个方面。 我们将通过对神经递质和受体类型的分子解剖来表征关键的突触特性。我们 将这些全面的神经生理学和神经遗传学数据存储在在线项目数据库中 我们的计算模型将允许更广泛的社区查看和探索我们的数据驱动建模 伊夫·奥特。

Project Summary Understanding how neural circuits create animal behavior requires knowing the system-wide activity patterns that connect sensory experience to motor activities, all within the full set of feedback loops by which actuated motor decisions modulate the animal's perceptions of itself and the outside world during naturally executed and unrestrained behaviors. Mechanistic understanding further requires interpretation of system-wide activity patterns in terms of the connectivity, synaptic, and cellular properties of all relevant neurons. Modeling requires comprehensive mapping of the salient dimensions of sensory input and motor output, as well as how high- dimensional neural activity patterns are properly projected, by decision-making and the internal constraints of the nervous system and motor system, into the fewer dimensions that characterize any animal's observed behavior. Such models are facilitated by using animals where entire brain and motor circuits can be mapped and interrogated with full molecular and cellular resolution. Here, we will use the mating behavior of the male C. elegans as such a paradigm. The mating behavior of C. elegans is a critically important and goal-directed task that occurs in natural environments and is robustly executed in the laboratory. Males use a specialized circuit of ~100 neurons – sensory neurons, interneurons, neuromodulatory, and motor neurons – all contained within the male tail ganglia to locate hermaphrodites, locate the vulva along the hermaphrodite's body, and initiate and complete insemination. A diverse set of mechanosensory, chemosensory, and pheromone sensing neurons are used to recognize the shape, texture, and chemical signature of the hermaphrodite body. Several neurons are specialized to detect fiducial points along the hermaphrodite body. The male implicitly uses an internal representation of the size, shape, and predictable behaviors of the hermaphrodite to positively recognize the hermaphrodite, infer its own position along the hermaphrodite, and execute an optimal movement strategy to maintain contact with the hermaphrodite and find and penetrate the vulva. It is now possible to record neural activity from the entire set of neurons in the male tail with high temporal resolution and complete cellular resolution. We will couple experiments, modern data analysis and modeling methods, and cellular and molecular genetic perturbations to elucidate the full set of sensorimotor events that organize the mating behavior. We will develop a realistic model of the circuit that integrates the observable behavioral algorithms with the connectivity and activity patterns of the male tail ganglion. We will apply genetic tools to identify and elucidate each sensory neuron type, and how it affects each aspect of decision-making by downstream interneurons and motor neurons. We will characterize key synaptic properties by molecular dissection of neurotransmitter and receptor types. We will store this comprehensive neurophysiological and neurogenetic data in online project databases connected to our computational models that will allow the wider community to view and probe our data-driven modeling eff ort.