What is going on in the fish's brain? Characterization and Modeling of Neural Dynamics (CNS and ANS and ICNS)

鱼的大脑里发生了什么？

• 批准号: 10525434
• 负责人: Florian Engert
• 金额: $ 81.62万
• 依托单位: HARVARD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-25 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10525434
• 关键词: Affect; Algorithms; Anatomy; Animals; Area; Autonomic ganglion; Autonomic nervous system; Behavior; Behavioral; Behavioral Assay; Behavioral Model; Biological Assay; Brain; Calcium; Cardiac; Cardiovascular system; Competence; Computational algorithm; Data; Data Set; Electrophysiology (science); Environment; Family; Fishes; Generations; Gills; Goals; Grant; Heart; Hypothalamic structure; Image; Imaging technology; Individual; Knowledge; Laboratories; Maps; Measurement; Measures; Membrane; Mind; Modeling; Motor; Motor output; Nature; Nervous system structure; Neurons; Output; Oxygen; Pathway interactions; Pattern; Performance; Physiological; Play; Population; Preparation; Process; Quality Control; Regulation; Research; Role; Sensorimotor functions; Synapses; System; Technology; Testing; Transgenic Organisms; Validation; Visualization; Weight; Zebrafish; base; connectome; data modeling; design; experimental study; in vivo; member; mind body interaction; neural circuit; neural model; neuroregulation; novel; optogenetics; patch clamp; reconstruction; relating to nervous system; respiratory; response; sensory input; sensory stimulus; simulation environment; statistics; virtual

Project 2 It is the behavioral algorithms that dictate the questions and framework for any model of neural implementation. Thus, to discover how behavioral algorithms are implemented and to generate a realistic brain-body circuit model for larval zebrafish, we need, in addition to knowledge about the anatomical structure of the circuits, detailed information about neural activity patterns during behavior. Further, behavioral algorithms must be implemented in the context of the embodied animal, which is subject to physiological demands, fluctuations and constraints. In order to accommodate both of these requirements, we will estimate and incorporate these physiological state variables into our modeling framework. In order to obtain the necessary datasets, we propose to add detailed measurements of heart and gills, as well as body wide recordings from the autonomic nervous system (ANS) and the intrinsic cardiac nervous system (ICNS) to our experimental repertoire. Such recordings will be combined with established brain wide imaging technologies, where we will focus on critical regions, such as the hypothalamus and various other areas that were already identified to play an important role in the modulation of behaviors and internal states. To facilitate the quantification and control of state-dependent variability, we have also designed a set of behavioral assays where environmental context is modulated to induce specific changes in the animal's internal and autonomic state. The comprehensive data sets collected in these experiments then allow for the generation of a family of realistic circuit models that could, in principle, implement the behavioral algorithms and that can reproduce and emulate the recorded neural and cardiac activity patterns. These realistic models allow for the generation of specific predictions and hypotheses about many of the unconstrained parameters in the underlying circuits. Such parameters include specific connectivity patterns, the synaptic weights, the excitability of membranes and many more. In order to constrain this large variety of variables one needs to apply a variety of independent approaches. To that end we will take advantage of the extensive experimental toolset already developed in the context of the current U19 grant, which includes the use of optogenetics based circuit interrogation, targeted electrophysiology and sparse connectomics tracing in overlaid EM volumes. Many of these experimental approaches require tethered behavioral preparations which are already established in our laboratories. Such preparations provide the ideal setting for brain wide imaging and targeted perturbation, and they will therefore facilitate the generation of a further refined set of validated circuit models for our various behavioral assays. To summarize, our goal is to first validate and constrain a set of realistic circuit models that we have already generated in the context of the current U19, to integrate these validated models with a novel framework we will generate for the animal's autonomic state, and ultimately to combine all of them into a composite realistic multiscale circuit model of the zebrafish brain and body. This multiscale circuit model will describe how all sensory stimuli, from the outside world as well as from inside the body, are encoded and transformed into distributed neural activity that generates the motor sequences that constitute the final output of the system.

项目2 是行为算法决定了任何神经实施模型的问题和框架。因此， 发现如何实现行为算法并为幼虫生成逼真的脑体电路模型 斑马鱼，除了了解电路的解剖结构外，我们还需要 行为过程中的神经活动模式。此外，必须在 体现的动物，受生理需求，波动和约束的影响。为了容纳两者 在这些要求中，我们将估算并将这些生理状态变量纳入我们的建模框架。 为了获得必要的数据集，我们建议添加心脏和g的详细测量 自主神经系统（ANS）和内在心脏神经系统（ICN）的记录 实验曲目。这样的录音将与已建立的大脑宽成像技术相结合，我们 将重点关注关键区域，例如下丘脑和其他已经确定的其他领域 在行为和内部状态的调节中的重要作用。促进量化和控制 与国家有关的可变性，我们还设计了一组行为分析，将环境环境调节为 引起动物内部和自主状态的特定变化。 这些实验中收集的全面数据集，然后允许产生一个现实的电路家族 原则上可以实现行为算法并可以复制和模拟记录的模型 神经和心脏活动模式。这些现实的模型允许产生特定的预测和假设 关于基础电路中的许多无约束参数。这样的参数包括特定的连接 模式，突触重量，膜的兴奋性等等。为了限制这大量的 变量需要采用各种独立方法。为此，我们将利用广泛的优势 实验工具集已经在当前U19赠款的背景下开发，其中包括使用光遗传学 基于电路的电路询问，靶向电生理学和稀疏连接组学在叠加的EM体积中追踪。许多 这些实验方法需要在我们的实验室中已经建立的束缚行为准备。 这样的准备工作为大脑宽成像和有针对性的扰动提供了理想的设置，因此它们将 促进为我们的各种行为分析的进一步精制的经过验证的电路模型的生成。 总而言之，我们的目标是首先验证和约束一组我们已经生成的现实电路模型 当前U19的上下文，将这些经过验证的模型与新的框架整合在一起，我们将为该框架生成 动物的自主状态，并最终将所有这些结合在一起 斑马鱼的大脑和身体。这个多尺度电路模型将如何描述所有感官刺激如何从外界看成 以及从体内的内部编码并转化为产生电动机的分布式神经活动 构成系统最终输出的序列。