CAREER: Neural mechanisms of auditory object recognition

这个项目将研究听众如何能够理解语音在具有挑战性的声学条件，如鸡尾酒会。在这种情况下，语音识别技术的表现大大低于人类听觉，这一直是改善基于语音的计算机界面和假体语音到文本辅助设备的障碍。这项研究将通过检查一种名为听觉恢复的知觉错觉来解决这一问题，在这种错觉中，大脑会填补被短暂的巨大噪音打断的单词和其他声音。这种现象意味着大脑构建了强大的语音内部模型，并使用它们从有限或退化的信息中确定正在说什么。为了了解这些内部模型是如何工作的，这项研究考察了斑胸草雀（一种鸣禽）的知觉恢复。斑胸草雀使用复杂的发声来识别彼此，并且需要能够在嘈杂的社会环境中理解谁在唱歌。该项目结合了测量行为和大脑活动的方法，以探测雀听觉恢复的神经回路。通过识别这些回路以及它们是如何连接起来的，这项研究将深入了解基本的感知过程，这些过程使人类能够通过语音进行交流，更广泛地说，使大脑能够从通常不完整和不可靠的感官信息中构建连贯的体验。更好地理解听觉感知的神经机制也有望提高语音识别算法的性能，以促进交流。该提案的教育部分解决了该领域在培训早期阶段对计算能力日益增长的需求。 研究人员计划实施一门课程，以鼓励和支持早期接触计算和系统神经科学，作为系统神经科学实验室的招聘工具，并有助于提高数据素养和数据科学技能的大学目标。听觉恢复已被广泛观察到在广泛的哺乳动物和鸟类物种。它可能有助于人类在极端嘈杂的条件下可靠地从语音中解码音素的非凡能力。这项研究将通过引入一个特征良好的听觉处理动物模型来研究一个成熟的人类心理物理范式，从而促进对这一现象的理解。该方法结合了在体内细胞外和细胞内的电生理与操作行为，研究自下而上和自上而下的过程如何相互作用的听觉皮层内，以支持听觉恢复斑胸草雀。目的1探讨听觉经验如何影响行为听觉恢复。目标2确定自上而下和自下而上恢复的神经相关出现在听觉处理层次结构中。目标3通过研究特定的细胞类型及其内在的神经动力学如何有助于对噪音的耐受性来剖析机制。教育目标包括开发课程，以培训学生在数据科学和系统神经科学的最佳软件实践，并将通过免费分发，该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的评估来支持。影响审查标准。

This project will investigate how listeners are able to comprehend speech in challenging acoustical conditions like cocktail parties. Speech-recognition technology dramatically underperforms human hearing in such conditions, which has been a barrier to improving voice-based computer interfaces and prosthetic speech-to-text assistive devices. This research will address this gap by examining a perceptual illusion called auditory restoration, in which the brain fills in words and other sounds that have been interrupted by brief, loud noises. This phenomenon implies that the brain constructs robust internal models of speech sounds, and that it uses them to determine what is being said from limited or degraded information. To learn how these internal models work, this study examines perceptual restoration in zebra finches, a species of songbird. Zebra finches employ complex vocalizations to identify themselves to each other, and need to be able to understand who is singing in noisy social settings. The project combines methods of measuring behavior and brain activity to probe the neural circuits underlying auditory restoration in finches. By identifying these circuits and how they are wired up, this research will give insight into fundamental perceptual processes that allow humans to communicate through speech, and more broadly, that enable the brain to construct coherent experiences from sensory information that is often incomplete and unreliable. A better understanding of the neural mechanisms of auditory perception also promises to enhance the performance of speech-recognition algorithms to facilitate communication. The educational component of the proposal addresses a growing need in the field for computational proficiency at earlier stages in training. The investigator plans to implement a curriculum to encourage and support early exposure to computational and systems neuroscience, serve as a recruitment tool for systems neuroscience labs, and contribute to the university's goals of increasing data literacy and skills in data science.Auditory restoration has been widely observed in a broad range of mammalian and avian species. It is likely to contribute to humans’ remarkable ability to reliably decode phonemes from speech in extremely noisy conditions. The research will advance the understanding of this phenomenon by bringing a well-characterized animal model of auditory processing to investigate a well-established human psychophysical paradigm. The approach combines in vivo extracellular and intracellular electrophysiology with operant behavior to examine how bottom-up and top-down processes interact within the auditory cortex to support auditory restoration in zebra finches. Aim 1 examines how auditory experience affects behavioral auditory restoration. Aim 2 determines where the neural correlates of top-down and bottom-up restoration emerge in the auditory processing hierarchy. Aim 3 begins to dissect the mechanism by investigating how specific cell types and their intrinsic neural dynamics contribute to tolerance for noise. The educational objectives include the development of a curriculum to train students in data science and best software practices for systems neuroscience and will impact the broader community through freely distributed, interactive instructional materials and training the next generation of data scientists in neuroscience.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.