Developing and Testing Models of the Auditory System With and Without Hearing Loss

开发和测试有或没有听力损失的听觉系统模型

• 批准号: 10048351
• 负责人: Laurel H. Carney
• 金额: $ 43.03万
• 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: 2010-04-01 至 2025-11-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10048351
• 关键词: Acoustic Nerve; Acoustics; Address; Affect; Anatomy; Auditory; Auditory system; Automobile Driving; Brain; Cells; Characteristics; Cochlea; Cochlear Implants; Code; Collection; Complex; Computer Models; Cues; Data; Data Set; Detection; Discrimination; Environment; Feedback; Foundations; Frequencies; Gaussian model; Goals; Hearing; Hearing Aids; Human; Inferior Colliculus; Inner Hair Cells; Knowledge; Loudness; Masks; Medial; Midbrain structure; Modeling; Nerve Fibers; Neuraxis; Neurons; Noise; Oryctolagus cuniculus; Pattern; Perception; Performance; Peripheral; Phase; Physiological; Physiology; Play; Property; Psychoacoustics; Psychophysics; Research Design; Resolution; Role; Science; Sensorineural Hearing Loss; Signal Detection Analysis; Stimulus; Structure; System; Testing; Textbooks; Time; auditory pathway; awake; base; cellular transduction; density; design; experimental study; hearing impairment; improved; innovation; insight; interest; normal hearing; novel strategies; predictive modeling; public health relevance; receptive field; relating to nervous system; response; sound; task analysis; theories

The goal of this proposal is to establish a new model for masked detection and frequency resolution, applicable to listeners with normal hearing and hearing loss, based on realistic physiological response properties. We are developing a new, fundamental framework for neural representations of acoustic stimuli that can predict a wide range of psychoacoustic phenomena. This framework is focused on neural fluctuations of auditory-nerve (AN) fibers, rather than on energy, average rates, or phase-locking to temporal fine structure. Neural fluctuations (NFs) refer to the relatively slow changes over time in AN responses (i.e., changes with rates ranging from 10s to a few 100 Hz). Neural fluctuations in this frequency range are of interest because they strongly excite, or suppress, neurons in the auditory central nervous system. The NF model is based on known nonlinear properties of inner-hair-cell and AN responses, and thus has important implications for interpreting masking results in listeners with sensorineural hearing loss. A representation of masked sounds based on the NF model is an alternative to the commonly accepted excitation-pattern representation provided by the power spectrum model of masking. The NF model successfully describes basic masking thresholds, as well as many experimental paradigms for which the power-spectrum (or energy) model fails. The NF model is not limited to low frequencies, as are models based on phase-locking to temporal fine structure. Here, the NF framework will be applied not only to masking paradigms, but also to stimulus paradigms that focus on frequency resolution, such as discrimination of the fundamental frequency of harmonic complex tones, or detection of increments in profile-analysis stimuli. Current models for the representation of these stimuli rely on a conceptual peripheral filter bank with critical bandwidths, estimated from human masking results using the power spectrum model of masking. Critical bandwidths, assumed to limit the frequency resolution of the auditory representations of complex sounds, are not consistent with known physiology. In contrast, frequency resolution according to the NF model is grounded on physiologically realistic response properties of AN fibers and sensitivity to neural fluctuations observed in the midbrain. Finally, to explain perception based on NF cues across the entire range of audible sound levels, we will extend our AN model to include NF-driven feedback gain control, guided by the known physiology and anatomy of the medial olivocochlear efferent system. The studies proposed here include: i) computational modeling to predict human thresholds, including re-examination of classical datasets that can, and those that cannot, be explained by the power-spectrum model, ii) related physiological studies in the midbrain, where cells are strongly sensitive to fluctuating inputs, and iii) new psychophysical studies designed to challenge the NF model, in listeners with normal hearing and those with sensorineural hearing loss.

本文的目标是建立一种新的掩蔽检测和频率分辨模型， 听力正常和听力损失的听众，基于现实的生理反应特性。我们 开发一个新的，基本的框架，神经表示的声音刺激，可以预测广泛的 心理声学现象的范围。该框架的重点是神经波动的神经元神经（AN） 纤维，而不是能量，平均速率，或相位锁定到时间的精细结构。神经波动 (NFs)是指AN响应随时间的相对缓慢的变化（即，变化率范围从10s 100 Hz）。在这个频率范围内的神经波动是令人感兴趣的，因为它们强烈地激发，或 抑制听觉中枢神经系统的神经元。NF模型基于已知的非线性 内毛细胞和AN反应的特性，因此对解释掩蔽具有重要意义 导致听力损失。一种基于NF模型的掩蔽音表示方法 是功率谱提供的普遍接受的激励模式表示的替代方案 面具模型NF模型成功地描述了基本的掩蔽阈值以及许多 功率谱（或能量）模型失败的实验范例。NF模型不限于 低频，如基于锁相到时间精细结构的模型。在这里，NF框架将 不仅适用于掩蔽范例，而且适用于聚焦于频率分辨率的刺激范例， 例如辨别谐波复音调的基频，或者检测 侧写分析刺激。目前的模型表示这些刺激依赖于一个概念外围 具有临界带宽的滤波器组，使用功率谱模型从人类掩蔽结果估计， 伪装临界带宽，假设限制听觉表征的频率分辨率， 复杂的声音，与已知的生理学不一致。与此相反，频率分辨率根据 NF模型基于AN纤维的生理学真实响应特性和对神经元的敏感性， 在中脑观察到的波动。最后，为了解释整个范围内基于NF线索的感知， 的可听声级，我们将扩展我们的AN模型，包括NF驱动的反馈增益控制，由 内侧橄榄耳蜗传出系统的已知生理学和解剖学。这里提出的研究 包括：i）预测人类阈值的计算建模，包括重新检查经典数据集 这可以，和那些不能，解释的功率谱模型，ii）相关的生理研究， 中脑，那里的细胞对波动的输入非常敏感，iii）新的心理物理学研究 旨在挑战听力正常和感音神经听力的听众的NF模型 损失