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: 10686992
• 负责人: Florian Engert
• 金额: $ 79.63万
• 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/10686992
• 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; Neural Interconnection; 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; connectome; data modeling; design; experimental study; in vivo; member; mind body interaction; neural; neural circuit; neural model; neuroregulation; novel; optogenetics; patch clamp; reconstruction; 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.

计划2 行为算法决定了任何神经实现模型的问题和框架。从而 发现行为算法是如何实现的，并为幼虫生成一个真实的脑-体电路模型 对于斑马鱼，我们除了需要了解电路的解剖结构外，还需要了解有关 神经活动模式此外，行为算法必须在 具身动物，这是受生理需求，波动和约束。为了兼顾两者 在这些需求中，我们将估计这些生理状态变量并将其纳入我们的建模框架。 为了获得必要的数据集，我们建议增加心脏和鳃的详细测量，以及身体宽度 记录从自主神经系统（ANS）和内在心脏神经系统（ICNS）到我们的 实验剧目这些记录将与已建立的全脑成像技术相结合， 将集中在关键区域，如下丘脑和其他各种区域，已经确定发挥作用， 在行为和内部状态的调节中起重要作用。为了便于量化和控制 状态依赖性的变化，我们还设计了一套行为分析，其中环境背景被调制， 在动物的内部和自主神经状态引起特定的变化。 在这些实验中收集的综合数据集，然后允许生成一个家庭的现实电路 原则上可以实现行为算法并可以复制和仿真记录的模型 神经和心脏活动模式这些现实的模型允许产生具体的预测和假设 关于底层电路中的许多无约束参数。这些参数包括特定的连通性 模式、突触重量、膜的兴奋性等等。为了限制这种大量的 需要应用各种独立的方法。为此，我们将充分利用 在当前U19资助的背景下已经开发的实验工具集，其中包括光遗传学的使用 基于电路询问，有针对性的电生理学和稀疏连接组学跟踪覆盖的EM体积。许多 这些实验方法需要在我们的实验室中已经建立的拴系行为准备。 这样的制备为脑宽成像和靶向扰动提供了理想的设置，因此它们将 有助于为我们的各种行为分析生成进一步细化的经验证的电路模型集。 总而言之，我们的目标是首先验证和约束一组我们已经在 当前U19的背景下，将这些经过验证的模型与我们将为 动物的自主状态，并最终将它们联合收割机组合成一个复合现实的多尺度电路模型， 斑马鱼的大脑和身体这个多尺度电路模型将描述如何所有的感官刺激，从外部世界， 以及来自身体内部的信息，被编码并转化为分布式神经活动， 构成系统最终输出的序列。