Nonlinear dynamics of complex sound processing in auditory cortex

描述（由申请人提供）：所提出的实验定义了皮质神经元如何在包括背景噪声的自然收听环境中支持对复杂声音（例如语音和其他通信声音）的鲁棒感知。噪声中的信号（SIN）处理主要是从心理学的角度研究的;支持正常听力所表现出的对噪声的显著耐受性的神经机制还没有得到很好的理解。我们专注于低频包络信息的编码，因为它对于可理解的语音至关重要，并且提高听力受损者的语音可理解度是一个关键。 重要的临床目标。更广泛地说，声音包络的动态特征，如共同的起始点，偏移和调制特性，驱动听觉场景分割。这些特征在皮层神经元的反应动力学中也得到了很好的体现。然而，已经证明很难开发一个通用的框架来理解皮层包络处理，因为刺激包络和神经响应模式之间的关系通常是复杂的和基本上非线性的。我们假设，皮层反应的非线性动力学赋予他们的时间精度是必不可少的SIN处理的鲁棒性。为了验证这一假设，我们将采用一种新的非线性建模框架，估计spectrotemporal感受野（STRFs）的神经元记录从核心听觉领域的清醒行为的松鼠猴使用16通道线性探头。我们将评估非线性STRF模型的能力-包括减少（例如，线性）和修改的形式-描述皮质反应的声音与简单的，参数变化的信封（目的1）的动力学。我们将比较模型与真实的神经元在编码嵌入噪声中的复杂发声（目标2）方面的性能，并在最佳贝叶斯种群解码方法的背景下测试候选神经机制，以“去噪”这些信号。最后，我们将评估注意力过滤对SIN处理的影响，通过记录动物在从事单独任务时呈现相同的复杂刺激，其中只有一个需要注意详细的包络特征（即，调制频率变化检测与声音偏移检测），同时导出STRF模型用于随后的比较（目标3）。这些实验将提供有价值的洞察候选人的神经机制，支持自下而上和自上而下方面的听觉场景分割，并支持严格的定量模型为基础的方法来表征复杂的声音的皮层表示的层状变换。

DESCRIPTION (provided by applicant): The proposed experiments define how cortical neurons support robust perception of complex sounds, such as speech and other communication sounds, in natural listening environments that include background noise. Signal in noise (SIN) processing has primarily been studied psychophysically; the neural mechanisms that support the remarkable tolerance to noise exhibited by normal hearing are not well understood. We focus on the encoding of low frequency envelope information because it is crucial for intelligible speech and improving speech intelligibility for the hearing impaired is an important clinical goal. More broadly, dynamic features of sound envelopes, such as common onsets, offsets, and modulation characteristics, drive auditory scene segmentation. These features are also particularly well represented in the response dynamics of cortical neurons. However, it has proven difficult to develop a general framework for understanding cortical envelope processing because the relationship between the stimulus envelope and the neural response pattern is typically both complex and substantially nonlinear. We hypothesize that the nonlinear dynamics of cortical responses endow them with a temporal precision that is essential to the robustness of SIN processing. To test this hypothesis, we will employ a novel nonlinear modeling framework to estimate spectrotemporal receptive fields (STRFs) of neurons recorded from the core auditory fields of awake behaving squirrel monkeys using 16- channel linear probes. We will evaluate the ability of nonlinear STRF models - including reduced (e.g., linear) and modified forms - to describe the dynamics of cortical responses to sounds with simple, parametrically varied envelopes (Aim 1). We will compare the performances of the models against real neurons in encoding complex vocalizations embedded in noise (Aim 2), and test candidate neural mechanisms for 'denoising' those signals in the context of optimal Bayesian population decoding methods. Finally, we will assess the effect of attentional filtering on SIN processing by recording from animals presented with identical complex stimuli while engaged in separate tasks, only one of which requires attention to detailed envelope features (i.e., modulation frequency change detection versus sound offset detection), while simultaneously deriving STRF models for subsequent comparison (Aim 3). These experiments will provide valuable insight into candidate neural mechanisms that support both bottom-up and top-down aspects of auditory scene segmentation, and support rigorous quantitative model-based approaches to characterizing laminar transformations in the cortical representation of complex sounds.