Sensorimotor processing, decision making, and internal states: towards a realistic multiscale circuit model of the larval zebrafish brain

感觉运动处理、决策和内部状态：建立幼虫斑马鱼大脑的真实多尺度电路模型

• 批准号: 10241477
• 负责人: Florian Engert
• 金额: $ 362.97万
• 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 2022-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10241477
• 关键词: Acetylcholine; Address; Affect; Algorithms; Animals; Area; Automobile Driving; BRAIN initiative; Behavior; Behavioral; Behavioral Assay; Behavioral Model; Biological; Biological Models; Brain; Brain imaging; Cellular Structures; Cognition; Complement; Complex; Conflict (Psychology); Decision Making; Disease; Dopamine; Dorsal; Drosophila genus; Electron Microscopy; Elements; Epinephrine; Event; Fishes; Foundations; Goals; Guidelines; Health; Human; Hunger; Hypothalamic structure; Individual; Infrastructure; Intervention; Knowledge; Larva; Left; Link; Loneliness; Measures; Medical; Mind; Modeling; Motion; Nature; Neurons; Neurophysiology - biologic function; Neurotransmitters; Organism; Output; Pattern; Pilot Projects; Process; Property; Rattus; Research; Series; Serotonin; Side; Son; Starvation; Stimulus; Stress; Swimming; Synapses; System; Testing; Transcend; Vision; Visual Fields; Work; Zebrafish; base; design; experimental study; in vivo; information processing; insight; multimodality; nanoscale; neural circuit; neurochemistry; neuroregulation; nutrient deprivation; optogenetics; relating to nervous system; repaired; response; sensory input; simulation; social deprivation; theories; ultraviolet irradiation; virtual; virtual model; visual stimulus; working group

Project Summary - A realistic multiscale circuit model of the larval zebrafish brain The working group of the BRAIN initiative (BRAIN 2025, a Scientific Vision) identified “the analysis of circuits of interacting neurons as being particularly rich in opportunity, with potential for revolutionary advances”. They further pointed out that “truly understanding a circuit requires identifying and characterizing the component cells, defining their synaptic connections with one another, observing their dynamic patterns of activity as their circuit functions in vivo during behavior, and perturbing these patterns to test their significance. It also requires an understanding of the algorithms that govern information processing within a circuit and between interacting circuits in the brain as a whole”. We propose to generate a realistic multiscale circuit model of the larval zebrafish brain – the multiscale virtual fish (MVF), which is well aligned with the BRAIN initiative's guidelines. The model will be based on algorithms inferred from behavioral assays and it will span spatial ranges across three levels: from the nanoscale at the synaptic level, to the microscale describing local circuits, to the macroscale brain-wide activity patterns distributed across many regions. The model will be constrained and validated by optogenetic interrogation and sparse connectomics of identified circuit elements 1​ ,2​. The ultimate purpose is to explain and simulate the quantitative and qualitative nature of behavioral outputs in response to sensory inputs across various timescales, and to explore how these findings might integrate with parallel work in two other important behavioral model systems, ​ the ​Drosophila larva and the rat. Our prior U01 project achieved the first instantiation of this model, whereby we successfully dissected the optomotor response (OMR)1​ ​, where a larval zebrafish will turn and swim to match the direction of a whole-field visual stimulus ​3–5.​ We will build on this model by achieving three further aims: First, we will expand the OMR project with four additional ethologically relevant behaviors: phototaxis, rheotaxis, escape, and hunting. We will extract the precise algorithms underlying each behavior and develop a version of the circuit model to understand their neural implementation. Second, we will further refine the model to account for multimodal integration and decision making, events that naturally happen when conflicting stimuli driving different behaviors are presented simultaneously. For example, a fish might be driven to execute a left turn by whole field motion moving to the left (OMR), while simultaneously being induced to turn right by increased brightness on its right side (phototaxis). Third, we will examine how internal brain states, such as hunger or stress, influence and modulate the specific behaviors (Aim 1) or behavioral interactions (Aim 2). Implementation of neurochemical modulation into the framework of the MVF will be achieved through simulation of highly conserved neuromodulatory neurotransmitter systems such as serotonin, acetylcholine, epinephrine and dopamine. To uncover generalizable principles of circuit design and function, we will compare our findings with those from two other model systems, the fruit fly larva and the rat. This will serve to elucidate the rules, motifs and algorithms of neural circuit function that transcend the potential idiosyncrasies of any given model.

项目总结--一个真实的斑马鱼幼体大脑多尺度电路模型 大脑倡议(Brain 2025，a Science Vision)工作组确定了“对大脑回路的分析” 相互作用的神经元具有特别丰富的机会，具有革命性进展的潜力“。他们还进一步 指出“真正理解一个电路需要识别和表征元件单元，定义它们的 突触之间的连接，观察它们在体内电路功能时的动态活动模式 在行为过程中，并干扰这些模式以测试其重要性。它还需要对算法的理解 它控制着一个电路内的信息处理以及整个大脑中相互作用的电路之间的信息处理“。 我们建议生成一个真实的斑马鱼幼体大脑的多尺度电路模型--多尺度虚拟鱼 (MVF)，这与大脑计划的指导方针很好地一致。该模型将基于推断出的算法 它将跨越三个级别的空间范围：从突触级别的纳米级到 描述局部电路的微观尺度，到分布在许多区域的宏观尺度全脑活动模式。这个 模型将通过光遗传询问和识别电路的稀疏连接来约束和验证 元素1​，2​。其最终目的是解释和模拟行为输出的定量和定性性质 以响应不同时间尺度上的感觉输入，并探索这些发现如何与并行 在另外两个重要的行为模型系统中工作，​​果蝇幼虫和大鼠。 我们之前的U01项目实现了该模型的第一个实例化，由此我们成功地解剖了视动机 响应(Omr)1​​，斑马鱼幼体将转身和游泳以匹配全视野视觉刺激​3-5的方向。​ 我们将在这一模式的基础上，实现三个进一步的目标：第一，我们将在OMR项目上增加四个额外的 与行为学相关的行为：趋光性、趋流性、逃逸和狩猎。我们将提取精确的算法 在每个行为的基础上，开发一个电路模型的版本来理解它们的神经实现。第二, 我们将进一步改进模型，以考虑自然发生的多模式集成和决策 当相互冲突的刺激驱动不同的行为时，同时呈现。例如，一条鱼可能被驱赶到 通过全场运动向左移动(OMR)执行左转，同时被诱导右转 右侧亮度增加(趋光性)。第三，我们将研究大脑内部状态，如饥饿或 应激、影响和调节特定的行为(目标1)或行为互动(目标2)。实施 神经化学调制进入MVF的框架将通过高度保守的模拟实现 神经调节神经递质系统，如5-羟色胺、乙酰胆碱、肾上腺素和多巴胺。 为了揭示电路设计和功能的一般性原则，我们将把我们的发现与两个 其他模型系统，果蝇幼虫和老鼠。这将有助于阐明神经网络的规则、主题和算法 超越任何给定型号的潜在特性的电路功能。