权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

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。

基本信息

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
项目类别：
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

项目摘要

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/文摘