DEVELOPING AND TESTING MODELS OF THE AUDITORY SYSTEM WITH & WITHOUT HEARING LOSS

开发和测试听觉系统模型

• 批准号: 8196752
• 负责人: Laurel H. Carney
• 金额: $ 32.83万
• 依托单位: UNIVERSITY OF ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-12-01 至 2015-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8196752
• 关键词: Acoustic Nerve; Attention; Auditory; Auditory system; Brain; Brain Stem; Cochlear nucleus; Code; Complex; Computer Simulation; Cues; Data; Dependence; Detection; Development; Environment; Freezing; Frequencies; Goals; Hearing; Hearing Aids; Human; Individual; Inferior Colliculus; Knowledge; Literature; Masks; Midbrain structure; Modeling; Nerve Fibers; Neurons; Noise; Olives - dietary; Oryctolagus cuniculus; Pattern; Performance; Peripheral; Physiological; Population; Process; Psychophysiology; Reporting; Research; Sensorineural Hearing Loss; Signal Transduction; Staging; Stimulus; Structure; Synapses; Test Result; Testing; Time; auditory pathway; awake; base; computerized data processing; coping; design; experience; hearing impairment; improved; insight; neural circuit; neuromechanism; novel; programs; public health relevance; relating to nervous system; response; sound; tool; tool development

DESCRIPTION (provided by applicant): The most common problem reported by people with sensorineural hearing loss is listening in the presence of background noise. The new efforts presented in this proposal will focus on the development, testing, and application of a composite computational model for physiological and psychophysical responses to complex sounds, especially sounds in the presence of background noise. A model that explains both the impressive ability of normal-hearing listeners, and the difficulty of listeners with hearing loss, to hear sounds in noisy environments will be an invaluable tool to better understand and predict listeners' performance in difficult auditory situations. This information can then be used to design new and improved hearing-aid signal-processing strategies that are successful in noisy situations. Previous computational models have successfully described auditory processing at several levels of the auditory system with phenomenological models for the auditory periphery that include cochlear tuning, transduction, and discharge times of individual auditory-nerve fibers. More recent models describe single neurons and neural circuits in the brain stem and mid-brain, including binaural interactions and neural amplitude-modulation processing. Computational models of neural population responses have also been developed to predict the performance of listeners with and without hearing loss in basic psychophysical tasks. In the proposed project, experience with these models will be leveraged to develop a novel, composite model that ties together these different levels of processing, providing a tool for studying the interactions of stimulus cues and neural mechanisms along the auditory pathway. This computational model for monaural and binaural processing of complex sounds will be tested and refined using physiological recordings from the midbrain (inferior colliculus) of awake rabbit and psychophysical tests in human listeners. The model will be used to predict existing psychophysical data for masked detection, both with and without binaural cues, by listeners with normal hearing. These psychophysical studies will be extended to include listeners with sensorineural hearing loss. Finally, the new model will predict performance of listeners with and without hearing loss on a masked amplitude-modulation (AM) detection task using reproducible modulation maskers. Physiological tuning for amplitude-modulation frequency first emerges at the level of the midbrain, the highest level of the proposed model. Thus this task will allow direct comparison between physiological aspects of AM processing at the mid-brain and psychophysical performance. This proposal provides a systematic transition from modeling basic physiological responses to predicting performance of listeners with and without hearing loss in psychophysical detection tasks in the audio- and modulation-frequency domains. The long term goal of this research program is to develop a robust tool for the development and testing of novel signal-processing strategies for listeners with hearing loss. PUBLIC HEALTH RELEVANCE: The Public Health Relevance of this project is to develop a better understanding of the difficulties in noisy situations for listeners with hearing loss. We will build a computational model for the auditory system of listeners with and without sensorineural hearing loss. This model will be used to predict performance of listeners for detection in noisy situations. Because hearing loss typically involves difficulty understanding complex sounds, especially in noise, knowledge of how the healthy brain copes with difficult listening environments will provide new and important insights for aiding listeners with hearing loss.

描述（由申请人提供）：感音神经性听力损失患者报告的最常见问题是在存在背景噪声的情况下进行听力。本提案中提出的新努力将侧重于开发，测试和应用复合计算模型，用于对复杂声音的生理和心理物理反应，特别是存在背景噪声的声音。一个模型，解释了令人印象深刻的能力，正常听力的听众，听力损失的听众，听到声音在嘈杂的环境中的困难将是一个宝贵的工具，以更好地了解和预测听众的表现在困难的听觉情况。然后，这些信息可以用于设计新的和改进的助听器信号处理策略，这些策略在嘈杂的情况下是成功的。以前的计算模型已经成功地描述了听觉系统的几个层次的听觉处理与听觉周边，包括耳蜗调谐，转导，放电时间的个别神经纤维的现象学模型。最近的模型描述了脑干和中脑中的单个神经元和神经回路，包括双耳相互作用和神经调幅处理。神经群体反应的计算模型也被开发出来，以预测听力损失和听力损失的听众在基本心理物理任务中的表现。在拟议的项目中，这些模型的经验将被利用来开发一种新的复合模型，将这些不同层次的处理联系在一起，为研究刺激线索和沿着听觉通路的神经机制的相互作用提供一种工具。将使用清醒兔子中脑（下丘）的生理记录和人类听众的心理物理测试来测试和改进复杂声音的单耳和双耳处理的计算模型。该模型将被用来预测现有的心理物理数据掩蔽检测，双耳线索和没有，听力正常的听众。这些心理物理学研究将扩展到包括感音神经性听力损失的听众。最后，新的模型将预测性能的听众和听力损失的掩蔽幅度调制（AM）检测任务使用可再生的调制掩蔽。调幅频率的生理调谐首先出现在中脑的水平，所提出的模型的最高水平。因此，这项任务将允许在中脑和心理物理性能的AM处理的生理方面之间的直接比较。该建议提供了一个系统的过渡，从模拟基本的生理反应，以预测性能的听众和没有听力损失的心理物理检测任务中的音频和调制频域。这项研究计划的长期目标是开发一个强大的工具，用于开发和测试听力损失听众的新信号处理策略。 公共卫生相关性：该项目的公共卫生相关性是为了更好地了解听力损失听众在嘈杂环境中的困难。我们将建立一个计算模型的听觉系统的听众和没有感音神经性听力损失。这个模型将被用来预测性能的监听器检测在嘈杂的情况下。由于听力损失通常涉及难以理解复杂的声音，特别是在噪音中，健康大脑如何应对困难的听力环境的知识将为帮助听力损失的听众提供新的重要见解。