Nonlinear dynamics of complex sound processing in auditory cortex

描述(申请人提供)：拟议的实验定义了在包含背景噪音的自然听力环境中，皮质神经元如何支持对复杂声音(如语音和其他通信声音)的稳健感知。噪声中信号(SIN)的处理主要是从心理生理学的角度研究的，而支持正常听力表现出的对噪声的显著耐受的神经机制还不是很清楚。我们把重点放在低频包络信息的编码上，因为它对语音的可理解性至关重要，而提高听力障碍者的语音清晰度是一种 重要的临床目标。更广泛地说，声音包络的动态特征，例如共同的起始点、偏移量和调制特性，驱动听觉场景分割。这些特征在大脑皮层神经元的反应动力学中也得到了特别好的体现。然而，由于刺激包络和神经反应模式之间的关系通常既复杂又基本上是非线性的，因此很难开发一个理解皮质包络处理的通用框架。我们假设，皮层反应的非线性动力学赋予它们时间精度，这对于SIN处理的稳健性是必不可少的。为了验证这一假设，我们将使用一种新的非线性建模框架来估计使用16通道线性探测器从清醒行为的松鼠猴子的核心听觉区域记录的神经元的频谱时间感受场(STRF)。我们将评估非线性STRF模型--包括简化的(例如，线性的)和修改的形式--描述具有简单的、参数变化的包络的声音的皮层反应的动力学的能力(目标1)。我们将比较模型和真实神经元在编码嵌入噪声中的复杂发音方面的性能(目标2)，并在最佳贝叶斯群体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，我们将通过记录在参与不同任务时被呈现相同复杂刺激的动物的记录来评估注意过滤对SIN加工的影响，其中只有一项任务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)，同时推导STRF模型用于后续比较(目标3)。这些实验将为支持自下而上和自上而下的听觉场景分割的候选神经机制提供有价值的见解，并支持基于严格的量化模型的方法来表征复杂声音的皮层表示中的层状变换。

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.