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: 10269928
• 负责人: Liangyu Tao
• 金额: $ 4.6万
• 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/10269928
• 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.

项目摘要/摘要 我们对我们对复杂，多行动，行为可变性的理解有一个根本的差距 由大脑中的神经回路产生。了解这一过程对于了解多少 神经疾病会导致限制和不适当的行动选择。该提议的目的是 了解神经元的种群如何控制复杂的动作选择行为变异性 行为。我们将利用果蝇来调查这个问题，因为尽管大脑要简单得多 比人类，果蝇的可变性很高。我们发现仅激活 果蝇大脑中的24个神经元导致四种不同的侵略性动作的可变表达：停止，机翼 高程，腿部伸展和弓步。该系统的相对简单性是针对销钉的理想选择 不同可变性来源的贡献：动作潜在产生中的细胞自主随机性， 肌肉或其他受24个神经元影响的网络级别或变异性的变异性。测试这些 可变性的来源，该项目将由三个主要目的组成。首先是确定动作是否 是通过使用机器学习的进步和 神经人体体体学可以自动识别侵略行为行为和之间的过渡 这些行动。第二个是测试每个动作的假设是由具有相似的神经元引起的 使用神经影像，遗传操纵和受控行为的神经解剖学连接 实验。第三是使用功能成像，体内电生理记录和计算 建模以确定行为变异性的神经基础。通过结合理论，实验， 和建模，我们将能够提供系统级别的解释，以解释动物为何执行的逻辑 尽管神经元激活水平一致，但动作不同。