Speech Coding in the Brain: Representation of Vowel Formants by Midbrain Neurons Tuned to Sound Fluctuations.
Speech Coding in the Brain: Representation of Vowel Formants by Midbrain Neurons Tuned to Sound Fluctuations.
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DOI：
10.1523/eneuro.0004-15.2015
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发表时间：
2015-07
期刊：
影响因子：
3.4
通讯作者：
McDonough JM
中科院分区：
文献类型：
--
作者：
Carney LH;Li T;McDonough JM
目前用于元音神经编码的模型通常基于听觉外围的线性描述，并且在高声电平和背景噪声中不适用。这些模型要么依赖于听神经放电率，要么依赖于对颞叶精细结构的相位锁定。然而，放电率和锁相在中高声电平时都是饱和的，而在中高频时，中枢神经系统的锁相性能下降。语音清晰度在大范围的声级上是稳健的这一事实对于随着声级增加而恶化的代码来说是有问题的。此外，一个成功的神经编码必须能够在听众可以容忍的背景噪声中使用语音。本文提出的模型解决了这些问题，并结合了非线性听觉外围的几个关键响应特性，包括对精细结构和包络时间特征的饱和、同步捕获和锁相。该模型还包括听觉中脑的特性，其中放电率根据幅度波动率进行调整。非线性外周反应特征在整个人群的低频神经率波动的幅度上形成了对比。这些波动模式导致中脑对元音共振峰进行广泛编码的反应轮廓，并产生背景噪音。来自清醒兔下丘的电生理记录支持这一假想的编码。该模型为理解跨语言元音空间的结构提供了信息，并为听力损失的听者提供了自动共振峰检测和语音增强的策略。
Current models for neural coding of vowels are typically based on linear descriptions of the auditory periphery, and fail at high sound levels and in background noise. These models rely on either auditory nerve discharge rates or phase locking to temporal fine structure. However, both discharge rates and phase locking saturate at moderate to high sound levels, and phase locking is degraded in the CNS at middle to high frequencies. The fact that speech intelligibility is robust over a wide range of sound levels is problematic for codes that deteriorate as the sound level increases. Additionally, a successful neural code must function for speech in background noise at levels that are tolerated by listeners. The model presented here resolves these problems, and incorporates several key response properties of the nonlinear auditory periphery, including saturation, synchrony capture, and phase locking to both fine structure and envelope temporal features. The model also includes the properties of the auditory midbrain, where discharge rates are tuned to amplitude fluctuation rates. The nonlinear peripheral response features create contrasts in the amplitudes of low-frequency neural rate fluctuations across the population. These patterns of fluctuations result in a response profile in the midbrain that encodes vowel formants over a wide range of levels and in background noise. The hypothesized code is supported by electrophysiological recordings from the inferior colliculus of awake rabbits. This model provides information for understanding the structure of cross-linguistic vowel spaces, and suggests strategies for automatic formant detection and speech enhancement for listeners with hearing loss.