Auditory Processing of Complex Sounds

描述(申请人提供)：健康的耳朵能够在嘈杂的环境中比任何人造算法或系统更好地检测和识别信号。然而，轻到中度的感觉神经性听力损失会给嘈杂环境中的听者带来极大的困难。我们正在结合行为学、生理学和计算建模的方法来研究复杂声音的神经处理。近年来的努力主要集中在掩蔽方面，这三种方法的结果都导致了低频掩蔽检测的跨频符合检测模型的发展。我们把这个模型称为相对模型--它利用了调谐到不同频率的纤维的相对放电时间。该模型为经典功率谱(或“能量”)模型的屏蔽提供了一种生理上现实的替代方案，并解决了与该模型相关的基本问题。我们建议检验由相对立模型提出的关于低频噪声中的掩蔽检测音调的假设，并将我们的研究扩展到高频和其他掩蔽范式。在这些研究中，我们将继续结合人和兔的行为实验，清醒兔和麻醉沙土鼠的生理记录，以及计算模型。我们还将继续使用过去几年开发的建模工具，这些工具不仅可以捕捉健康和受损的听神经纤维和中枢听觉神经元的详细属性，而且还可以将基于总体模型预测的性能与行为性能的实际测量进行定量比较。这项工作的长期目标是提供新的信号处理策略，以帮助听力受损的人，基于在噪声中处理信号的独特神经策略。

DESCRIPTION (provided by applicant): The healthy ear is able to detect and identify signals in a noisy environment better than any manmade algorithm or system. However, mild-to-moderate sensorineural hearing loss causes significant difficulty for listeners in noisy environments. We are combining behavioral, physiological, and computational-modeling approaches to study neural processing of complex sounds. The efforts of recent years have focused on masking, and results from all three approaches have led to the development of a cross-frequency coincidence-detection model of masked detection at low frequencies. We refer to this model as the phase-opponency model - it takes advantage of the relative times of discharge of AN fibers tuned to different frequencies. This model provides a physiologically realistic alternative to the classical power-spectrum (or "energy") model for masking and resolves fundamental problems associated with that model. We propose to test hypotheses, suggested by the phase-opponency model regarding masked detection of tones in noise at low frequencies and to extend our studies to high frequencies and to other masking paradigms. Throughout these studies, we will continue to combine behavioral experiments in human and rabbit, physiological recordings in awake rabbit and anesthetized gerbil, and computational modeling. We will also continue to use modeling tools developed during the last few years that not only capture the detailed properties of healthy and impaired auditory-nerve fibers and central auditory neurons, but that also allow quantitative comparison of performance predicted on the basis of population models to actual measurements of behavioral performance. The long-term goal of this effort is to provide novel signal-processing strategies to aid the hearing impaired, based on the unique neural strategies for processing signals in noise.