The effect of encoding cue reliability on the function and development of the barn owl auditory system

编码线索可靠性对仓鸮听觉系统功能和发育的影响

• 批准号: 10388026
• 负责人: Keanu Shadron
• 金额: $ 4.6万
• 依托单位: ALBERT EINSTEIN COLLEGE OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-12-01 至 2025-11-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10388026
• 关键词: effect; encoding; cue; reliability; function

Project Summary In order to accurately perceive the world and respond accordingly, the brain has to deal with noise inherent in sensory cues. One method is for the brain to learn which cues are reliable across contexts and rely on these cues in the future. Previous studies have demonstrated that sensory systems are able to actively learn recent statistics underlying cue reliability and adapt to short term changes. However, relatively little is known about whether and how sensory systems adapt to natural statistics of sensory cues that are expected to be permanent. While humans display biases based on long term patterns of sensory cue statistics, the neural mechanisms underlying this are unknown. To address these knowledge gaps, this proposal will investigate whether and how anticipated reliability of binaural spatial cues determines sound localization of barn owls. Barn owls compute the interaural time difference (ITD) to determine azimuthal location. Previous work showed that the signal-to-noise ratio of ITD varies across frequencies in a location dependent manner, based on the acoustical properties of the head. Thus, certain frequencies convey more reliable ITD cues for sounds from a given location, and the neural tuning in the midbrain is optimized to reflect this pattern. This provides a system where the reliability of natural acoustic cues is represented, allowing for the investigation of the effect of anticipating sensory statistics on perceptual discriminability and how it is learned. Recent work by our group has shown evidence that human auditory perception is also shaped by ITD statistics, potentially allowing the properties of neural coding investigated in this project to explain the bases of these findings. The overall hypothesis is that barn owls learn to anticipate cue reliability during juvenile critical periods, leading to biases in neural and behavioral processing. Behavioral and electrophysiological approaches along with computational methodology will be used to test this hypothesis. Experiments outlined in Aim 1 will determine whether barn owls show better sound discrimination for frequencies anticipated to be reliable. Aim 2 will investigate whether and how the neural population displaying the pattern of ITD-frequency tuning described in the midbrain explains spatial discriminability. Novel multi-unit recordings in the midbrain in vivo along with computational techniques will be used to address this. Aim 3 will elucidate the capacity of the barn owl to learn altered forms of cue reliability, by changing the acoustical properties of the owl’s head at different stages of development. Altogether, this proposal will provide understanding on the mechanisms used to deal with long term patterns of cue reliability. The data will offer insight into how the auditory system learns the natural statistics underlying sensory noise to optimize perception, as well as its capacity to adapt to unanticipated changes.

项目摘要 为了准确地感知世界并做出相应的反应，大脑不得不处理噪音 内在的感觉线索。一种方法是让大脑学习哪些线索在上下文中是可靠的， 在未来的这些线索。先前的研究表明，感觉系统能够积极地 学习最近的统计数据，这些数据是线索可靠性的基础，并适应短期变化。然而，相对来说， 关于感觉系统是否以及如何适应感觉线索的自然统计数据， 成为永久性的。虽然人类显示偏见的基础上长期模式的感官线索统计，神经 这背后的机制是未知的。为了弥补这些知识差距，本提案将调查 双耳空间线索的预期可靠性是否以及如何决定仓鸮的声音定位。 仓鸮计算双耳时间差（ITD）来确定方位角位置。以前的工作表明 ITD的信噪比在频率上以位置相关的方式变化，基于 头部的声学特性。因此，某些频率为来自一个或多个声音的声音传达更可靠的ITD提示。 给定的位置，中脑的神经调谐被优化以反映这种模式。这提供了一种系统， 其中代表了自然声学线索的可靠性，从而可以调查 预测感知辨别力的感官统计数据以及它是如何学习的。我们小组最近的工作 已经证明，人类的听觉感知也是由ITD统计数据塑造的，这可能会使 本项目中研究的神经编码的性质来解释这些发现的基础。整体 一种假设是仓鸮在幼年关键期学会预测线索的可靠性， 神经和行为处理的偏差。行为和电生理学方法沿着 将使用计算方法来检验这一假设。目标1中概述的实验将确定 仓鸮是否对预期可靠的频率表现出更好的声音辨别力。目标2将 研究神经群体是否以及如何显示ITD频率调谐模式， 中脑解释了空间辨别力。在体内中脑中的新的多单位记录，沿着 将使用计算技术来解决这个问题。目标3将阐明仓鸮的学习能力 通过改变猫头鹰头部在不同阶段的声学特性， 发展总而言之，这项建议将使人们了解用于处理长期 线索可靠性的术语模式。这些数据将提供深入了解听觉系统如何学习自然的声音。 统计潜在的感官噪音，以优化感知，以及它的能力，以适应意想不到的 变化