Decoding the neural mechanism underlying variable action selection in Drosophila.

解码果蝇可变动作选择的神经机制。

• 批准号: 10474509
• 负责人: Liangyu Tao
• 金额: $ 4.68万
• 依托单位: DREXEL UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-28 至 2023-09-27
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10474509
• 关键词: Action Potentials; Affect; Aggressive behavior; Anatomy; Animals; Behavior; Behavior Control; Behavioral; Brain; Cells; Complex; Computer Models; Data; Disease; Drosophila genus; Electrophysiology (science); Functional Imaging; Generations; Genetic; Goals; Horns; Human; Image; Individual; Lateral; Leg; Light; Logic; Machine Learning; Measures; Mental disorders; Methods; Modeling; Motor; Muscle; Nervous system structure; Neurons; Output; Pattern; Population; Probability; Process; Series; Signal Transduction; Source; System; Testing; Time; Training; Variant; Video Recording; Wing; Work; appendage; base; deep neural network; experimental study; flexibility; fly; genetic manipulation; in vivo; in vivo imaging; insight; male; markov model; nervous system disorder; neural circuit; neuroethology; neuroimaging; neuromechanism; neuroregulation; optogenetics; patch clamp; peanut butter; relating to nervous system; sensory integration; theories; tool

Project Summary/Abstract There is a fundamental gap in our understanding of how complex, multi-action, behavioral variability is generated by neural circuits in the brain. Understanding this process is crucial to understanding how many neurological diseases cause restricted and inappropriate selection of actions. The goal of this proposal is to understand how populations of neurons control the behavioral variability in action selection for complex behaviors. We will utilize the fruit fly to investigate this question because despite having a much simpler brain than humans, the fruit fly performs complex behaviors with high variability. We have found that activating just 24 neurons in the fruit fly brain causes variable expression of four distinct aggressive actions: stopping, wing elevation, leg extension, and lunging. The relative simplicity of this system is ideal for pin-pointing the contribution of different sources of variability: cell-autonomous stochasticity in action potential generation, variability at a network-level or variability in muscles or other neurons affected by the 24 neurons. To test these sources for variability, the project will consist of three main aims. The first is to determine whether the actions are discrete states of behavior or lie within a continuum by using advances in machine learning and neuroethology to automate the identification of the aggressive behavioral actions and the transitions between these actions. The second is to test the hypothesis that each action is caused by neurons with similar neuroanatomical connections using neuroimaging, genetic manipulation, and controlled behavioral experiments. The third is to use functional imaging, in vivo electrophysiological recordings, and computational modeling to determine the neural basis for the variability of behavior. By combining theory, experimentation, and modeling, we will be able to provide a systems level explanation of the logic behind why animals perform different actions despite consistent levels of neuronal activation.

项目总结/摘要 我们对复杂性、多动作、行为可变性的理解存在根本性的差距 由大脑中的神经回路产生。了解这一过程对于了解 神经系统疾病导致行动选择受限和不适当。本提案的目的是 了解神经元群体如何控制复杂行为选择中的行为变异性 行为。我们将利用果蝇来研究这个问题，因为尽管果蝇的大脑要简单得多， 与人类相比，果蝇的行为复杂多变。我们已经发现， 果蝇大脑中的24个神经元导致四种不同的攻击行为的可变表达：停止，翅膀 抬高、腿部伸展和弓步。该系统相对简单，非常适合精确定位 不同变异源的贡献：动作电位产生中的细胞自主随机性， 网络层面的变异性或受24个神经元影响的肌肉或其他神经元的变异性。测试这些 根据可变性的来源，该项目将包括三个主要目标。第一是确定行动是否 是离散的行为状态，或者通过使用机器学习的进步而处于连续统一体中， 神经行为学自动识别攻击性行为动作和之间的转换 这些行动。第二个是检验假设，即每个动作都是由具有相似的神经元引起的。 神经解剖学的联系，使用神经成像，遗传操作，和控制行为， 实验第三是使用功能成像、体内电生理记录和计算 建模以确定行为可变性的神经基础。通过结合理论，实验， 和建模，我们将能够提供一个系统水平的解释背后的逻辑，为什么动物的表现 尽管神经元激活水平一致，但作用不同。