Evaluating the neural mechanisms of stream segregation using naturalistic sounds in behaving primates

项目摘要 听觉系统用来从一个声音中分离和理解单个说话者的神经机制， 其他演讲者的背景目前尚不清楚。听力正常的年轻人 在复杂的听觉环境中理解单个说话者，而听力损失甚至 一些没有听力损失的老年人在同样的环境中挣扎。最终设计的第一步 技术恢复能力，以遵循一个单一的发言者在拥挤的房间是要了解如何正常的 听觉系统实现了这一壮举。 人类语音由跨越宽频率范围的分量组成。周边听觉 系统通过频谱频率分离输入信号，但单个扬声器的感知是 单一实体。这个概念定义了一个听觉流：一组声音特征，似乎源于一个声音流。 单一来源。几十年来的开创性工作使人们对心理和 流简单的声音的生理机制，达到人口分离模型： 当对每个声源的神经反应被分离成独特的声源时， 神经群体对于简单的声音，群体分离可能是固有的频率分离， 源，或计算在外围听觉系统。然而，如何实现分离， 具有重叠频率内容的复杂声音是未知的。一种拟议的机制是时间性的 相干性--不同特征的振幅的一致波动结合在一起的想法 一个单一的感知流。 虽然时间相干性对种群分离的影响已经用合成的、周期性的 刺激，这些结果不太可能推广到自然声音，其中包含复杂的时间 信封因此，我们将使用自然主义的类语音刺激来检查听觉流：两种不同的 由从人类语音提取的时间包络调制的谐波复合音调。当 用不同的包络调制，这些刺激在时间上是不连贯的，并唤起对两个的强烈的感知。 单独的流;当用相同的包络调制时，它们是相干的，并且被视为单个 源源不断的我们将收集绒猴听觉皮层对这些刺激的单神经元反应，以评估 时间相干性（和流）对神经编码的影响。然后我们会让绒猴参与一项任务 需要这些声音的流，并量化定向注意力对神经编码的影响。这些 研究结果将有助于改进水流分离模型，为控制研究奠定基础 使用完全自然的声音。

Project Summary The neural mechanisms used by the auditory system to isolate and comprehend a single speaker out of a background of other speakers are currently unknown. Young, normal-hearing individuals are capable of understanding a single speaker in complex auditory environments, while individuals with hearing loss and even some older individuals without hearing loss struggle in the same settings. A first step in ultimately designing technology to restore the ability to follow a single speaker in a crowded room is to understand how the normal auditory system achieves this feat. Human speech is composed of components that span a broad frequency range. The peripheral auditory system separates the incoming signal by spectral frequency, yet the perception of a single speaker is that of a single entity. This percept defines an auditory stream: a group of sound features that appear to originate from a single source. Pioneering work over several decades has led to a basic understanding of the psychological and physiological mechanisms of streaming simple sounds, arriving at the population separation model: the perception of unique sound sources occurs when neural responses to each source are separated into unique neural populations. For simple sounds, population separation may be inherent in the frequency separation of sources, or computed in the peripheral auditory system. However, how separation is achieved for more complex sounds with overlapping frequency content is unknown. One proposed mechanism is temporal coherence – the idea that coincident fluctuations in the amplitude of distinct features become bound together into a single perceived stream. While the effects of temporal coherence on population separation have been tested with synthetic, periodic stimuli, these results are not likely to generalize to those of natural sounds, which contain complex temporal envelopes. Therefore, we will examine auditory streaming using naturalistic speech-like stimuli: two distinct harmonic complex tones that are modulated by temporal envelopes drawn from human speech. When modulated with different envelopes, these stimuli are temporally incoherent and evoke a strong percept of two separate streams; when modulated with identical envelopes they are coherent and are perceived as a single stream. We will collect single-neuron responses to these stimuli in marmoset auditory cortex to assess the impact of temporal coherence (and streaming) on neural encoding. We will then engage marmosets in a task requiring streaming of these sounds and quantify the effects of directed attention on neural encoding. These results will be useful in refining models of stream segregation, laying the groundwork for controlled studies using fully natural sounds.