Neural representation of mating partners by male C. elegans

项目摘要 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 连接感官体验和运动活动，所有这些都在一整套反馈回路中， 运动决策调节动物在自然执行过程中对自身和外部世界的感知， 不受约束的行为要理解机制，还需要解释全系统的活动 所有相关神经元的连接性、突触和细胞特性方面的模式。建模需要 全面绘制感觉输入和运动输出的突出维度，以及 三维神经活动模式被适当地投射，通过决策和内部约束， 神经系统和运动系统，到较少的维度，表征任何动物的观察 行为这种模型是通过使用动物来促进的，在动物中，整个大脑和运动回路可以被映射， 用完整的分子和细胞分辨率进行询问。在这里，我们将使用雄性C的交配行为。 elegans作为这样一个范例。对C. elegans是一项非常重要的目标导向任务 在自然环境中发生，并在实验室中稳健地执行。雄性使用一种特殊的 ~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-全部包含在 雄性尾神经节定位雌雄同体，沿着雌雄同体的身体沿着定位外阴，并启动和 完全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 用来识别雌雄同体身体的形状、质地和化学特征。一些神经元 专门用来检测沿着雌雄同体身体的基准点。男性含蓄地使用内部 代表的大小，形状，和可预测的行为的雌雄同体积极认识到， 它可以确定自己的位置，沿着雌雄同体，并执行最佳运动策略， 保持与雌雄同体的接触，找到并插入外阴。现在可以记录神经 来自雄性尾部整组神经元的活动具有高时间分辨率和完整的细胞 分辨率我们将结合实验，现代数据分析和建模方法，以及细胞和分子 遗传扰动，以阐明组织交配行为的全套感觉运动事件。我们将 开发一个现实的电路模型，将可观察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遗传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决策的各个方面。 我们将通过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 将把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，这将允许更广泛的社区查看和探索我们的数据驱动的建模 结果。

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.