From microscale structure to population coding of normal and learned behavior

从微观结构到正常和习得行为的群体编码

• 批准号: 9981045
• 负责人: Wiliam McIntyre DeBello
• 金额: $ 60.11万
• 依托单位: ALBERT EINSTEIN COLLEGE OF MEDICINE
• 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 2022-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9981045
• 关键词: Achievement; Adaptive Behaviors; Address; Afferent Neurons; Anatomy; Architecture; Auditory; Automobile Driving; Axon; Barn Owls; Behavior; Behavioral; Binaural; Binding; Biological Models; Brain; Cells; Characteristics; Code; Computer Models; Contralateral; Cues; Data; Distributional Activity; Electron Microscopy; Electrophysiology (science); Experimental Designs; Goals; Head; Learning; Link; Location; Maps; Measures; Methods; Microanatomy; Microelectrodes; Microscopy; Midbrain structure; Modeling; Movement; Neurons; Noise; Optics; Pathologic; Pathway interactions; Pattern; Peripheral; Physiological; Population; Property; Research; Resolution; Shapes; Sound Localization; Stimulus; Strigiformes; Structure; System; Testing; Time; Tissues; Translating; Vertebrates; Visual; Work; approach behavior; base; behavior measurement; behavior test; cell type; cellular targeting; comparative; driving behavior; experimental study; gaze; information processing; learned behavior; light microscopy; multi-electrode arrays; multidisciplinary; network architecture; network models; neural patterning; neurotransmission; prismatic spectacles; receptive field; relating to nervous system; response; sound; theories

Abstract This study aims to understand how the ensemble activity and network architecture of a neuronal population guides natural and learned behavior. The model system is the midbrain localization pathway of the owl. Ensemble recordings, microcircuit analysis, behavioral measurements and computational modeling will be used to analyze the neural representation of auditory space and the head-orienting movement driven by it. The compact volume of tissue commanding this behavior makes a complete understanding of information processing tractable with high-throughput electrophysiological and microanatomical methods. How information about sound location is readout to guide orienting behaviors has not been demonstrated in any species. This project has the potential to fill this gap. Aim 1 will investigate the relationship between orienting behavior and activity in the neuronal population representing auditory space, in which frontal space is overrepresented. The hypothesis is based on recent work showing that sound localization can be explained by statistical inference, computed by integrating activity across the entire population. Microelectrode arrays (MEAs) will be used to map the activity of the population upon presentation of sounds. Population decoders will be constructed to determine how the population activity is readout to drive behavior. In Aim 2, the network architecture supporting the activity pattern will be studied with light and electron microscopy. Network models will combine the data to explain how connectivity and cellular computations result in the population activity and correlated firing that drives behavior. When auditory-visual cues are modified, the midbrain representation of auditory space adapts over time, and consequently drives a learned behavior. Aim 3 will directly examine this link. MEA recordings, microcircuit analysis and behavioral measurements will be made in owls adapted to prismatic spectacles. Population decoders will be used to test the hypothesis that population activity in the learned condition maintains a non-uniform population code with an overrepresentation of frontal space. Network models will be used to examine how local re-wiring may explain changes in the distribution of activity across the population. This would be the first time that neural activity and network architecture underlying sound localization are approached from the complete-population down to single-cell level, before and after learning. This integrative approach holds potential for understanding principles of population coding, plasticity and learning that operate across species and brain circuits.

抽象的 这项研究旨在了解神经元种群的整体活动和网络结构如何 指导自然和学习的行为。模型系统是猫头鹰的中脑定位途径。 整体记录，微电路分析，行为测量和计算建模将是 用于分析听觉空间的神经表示以及由其驱动的头部方向运动。 命令这种行为的紧凑型组织量使得对信息有完整的了解 使用高通量电生理学和微解剖学方法处理。如何 有关声音位置的信息已读取以指导定向行为 物种。该项目有可能填补这一空白。 AIM 1将研究神经元种群中定向行为与活动之间的关系 代表听觉空间，其中额叶空间的代表过多。该假设基于最近 工作表明，可以通过统计推断来解释声音本地化，并通过集成来计算 整个人群的活动。微电极阵列（MEA）将用于映射 呈现声音后的人口。将构建人口解码器，以确定 人口活动是读书以推动行为。 在AIM 2中，将使用光和电子研究支持活动模式的网络体系结构 显微镜。网络模型将结合数据以解释连接性和蜂窝计算的方式 导致人口活动和相关的射击，从而驱动行为。 当修改听觉视觉提示时，听觉空间的中脑表示会随着时间的推移而适应 因此推动了学习的行为。 AIM 3将直接检查此链接。 MEA录音， 微电路分析和行为测量将以适合棱镜眼镜的猫头鹰进行。 人口解码器将用于检验以下假设： 保持不均匀的人口代码，其额叶空间过多。网络模型将 用于检查局部重新布局如何解释活动分布的变化 人口。 这将是神经活动和网络体系结构的首次是声音本地化 在学习之前和之后，从完整的人群到单细胞水平接近单细胞水平。这 综合方法具有理解人口编码，可塑性和学习原理的潜力 跨物种和脑电路运行。