Decoding the neural mechanism underlying variable action selection in Drosophila.

项目摘要/摘要 我们对复杂、多行动、行为变异性的理解存在一个根本性的鸿沟 由大脑中的神经回路产生。了解这一过程对于了解 神经系统疾病导致对行为的限制和不适当的选择。这项提议的目标是 了解神经元群体如何控制复杂动作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，因为尽管我们的大脑要简单得多 与人类相比，果蝇表现出高度变异性的复杂行为。我们发现，激活只是 果蝇大脑中的24个神经元导致四种不同的攻击性动作的不同表达：停止、翅膀 仰卧起坐、腿部伸展和俯冲。该系统的相对简单性非常适合用针指向 不同变异性来源的贡献：动作电位产生中的细胞自主随机性， 受24个神经元影响的网络水平的可变性或肌肉或其他神经元的可变性。为了测试这些 关于可变性的来源，该项目将由三个主要目标组成。第一个是确定行动是否 是行为的离散状态，还是存在于连续统中，通过使用机器学习和 神经行为学自动识别攻击性行为行为和之间的转换 这些行动。第二种是检验假设，即每一种行为都是由类似的神经元引起的。 使用神经成像、基因操作和受控行为的神经解剖学联系 实验。三是使用功能成像、活体电生理记录和计算 建模以确定行为变异性的神经基础。通过将理论、实验、 和建模，我们将能够提供系统级别的解释，解释为什么动物表现 尽管神经元的激活水平一致，但行动却不同。

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.