From microscale structure to population coding of normal and learned behavior

摘要 这项研究旨在了解神经元群体的整体活动和网络结构 引导自然和习得的行为。该模型系统是猫头鹰的中脑定位通路。 合奏录音、微电路分析、行为测量和计算建模 用于分析听觉空间的神经表征以及由其驱动的头部定向运动。 控制这一行为的紧凑的组织体积使人完全理解信息 高通量电生理和显微解剖学方法易于处理。多么 关于声音位置的信息被读出以指导定向行为在任何 物种。这个项目有可能填补这一空白。 目的1将研究神经元群体中定向行为和活动的关系。 代表听觉空间，其中额叶空间被过度代表。这一假设是基于最近的 研究表明，声音定位可以通过统计推断来解释，通过积分计算得出 整个人口的活动。微电极阵列(MEA)将被用来绘制 当声音出现时，人口。人口解码器将被构建以确定如何 人口活动被读出以驱动行为。 在目标2中，将用光和电子来研究支持活动模式的网络结构 显微镜。网络模型将结合这些数据来解释连通性和蜂窝计算 导致群体活动和相关的激发，从而驱动行为。 当听觉-视觉线索被修改时，听觉空间的中脑表征随着时间的推移而适应， 从而驱动一种习得的行为。AIM 3将直接检查这一联系。我指的是录音， 微电路分析和行为测量将在适应棱镜的猫头鹰身上进行。 种群解码器将被用来检验在学习条件下种群活动的假设 维护一个非统一的种群代码，其中包含过多的额面空间。网络模型将 用于研究局部重新布线如何解释活动分布的变化 人口。 这将是第一次神经活动和网络体系结构的声音定位 学习前后，从全种群向下到单细胞水平。这 综合方法具有理解种群编码、可塑性和学习的原理的潜力 它们跨越物种和大脑回路运作。

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.