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

该方案的目标是建立一种新的掩蔽检测和频率分辨率模型，适用于 对于听力正常和听力损失的听众，基于现实的生理反应特性。我们是 开发一种新的基本框架，用于声刺激的神经表示，可以预测广泛的 心理声学现象的范围。该框架主要研究听神经的神经波动。 纤维，而不是能量，平均速率，或锁相，以时间精细结构。神经波动 (NFS)是指响应中随时间的相对较慢的变化(即，从10s到10s的速率变化 到几个100赫兹)。在这个频率范围内的神经波动是有意义的，因为它们强烈地刺激，或 抑制，听觉中枢神经系统中的神经元。神经网络模型基于已知的非线性 内毛细胞的特性和AN反应，因此对解释掩蔽有重要意义 导致听者出现感音神经性听力损失。一种基于NF模型的掩蔽声音表示 是通常接受的由功率谱提供的激励模式表示的替代方案 蒙面的模型。该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)模型失效的实验范式。核因子模型并不局限于 低频，以及基于锁相到时间精细结构的模型。在这里，NF框架将 不仅应用于掩蔽范式，而且还应用于聚焦于频率分辨率的刺激范式， 例如辨别调和复音的基频，或检测 侧写分析刺激。当前用于表示这些刺激的模型依赖于概念性外围 具有临界带宽的滤波器组，从使用功率谱模型的人类掩蔽结果估计 蒙面。临界带宽，假定限制听觉表示的频率分辨率 复杂的声音，与已知的生理学不一致。相比之下，频率分辨率根据 神经营养因子模型基于神经纤维的生理真实反应特性和对神经的敏感性 在中脑观察到的波动。最后，为了解释基于整个范围内的NF线索的感知 对于可听声级，我们将扩展AN模型，以包括在 已知的内侧橄榄耳蜗传出系统的生理学和解剖学。在这里提出的研究 包括：一)用于预测人体阈值的计算建模，包括重新检查经典数据集 这可以用功率谱模型来解释，也可以用功率谱模型来解释。 中脑，那里的细胞对波动的输入非常敏感，以及iii)新的心理物理学研究 旨在挑战NF模型，在听力正常的听者和感觉神经性听力的听者中 损失。

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.