Neuroscience Graduate Program, Oregon Health and Science University, Portland, Oregon 97239.
Oregon Hearing Research Center, Oregon Health and Science University, Portland, Oregon 97239.
J Neurosci. 2020 May 6;40(19):3783-3798. doi: 10.1523/JNEUROSCI.2105-19.2020. Epub 2020 Apr 9.
Statistical regularities in natural sounds facilitate the perceptual segregation of auditory sources, or streams. Repetition is one cue that drives stream segregation in humans, but the neural basis of this perceptual phenomenon remains unknown. We demonstrated a similar perceptual ability in animals by training ferrets of both sexes to detect a stream of repeating noise samples (foreground) embedded in a stream of random samples (background). During passive listening, we recorded neural activity in primary auditory cortex (A1) and secondary auditory cortex (posterior ectosylvian gyrus, PEG). We used two context-dependent encoding models to test for evidence of streaming of the repeating stimulus. The first was based on average evoked activity per noise sample and the second on the spectro-temporal receptive field. Both approaches tested whether differences in neural responses to repeating versus random stimuli were better modeled by scaling the response to both streams equally (global gain) or by separately scaling the response to the foreground versus background stream (stream-specific gain). Consistent with previous observations of adaptation, we found an overall reduction in global gain when the stimulus began to repeat. However, when we measured stream-specific changes in gain, responses to the foreground were enhanced relative to the background. This enhancement was stronger in PEG than A1. In A1, enhancement was strongest in units with low sparseness (i.e., broad sensory tuning) and with tuning selective for the repeated sample. Enhancement of responses to the foreground relative to the background provides evidence for stream segregation that emerges in A1 and is refined in PEG. To interact with the world successfully, the brain must parse behaviorally important information from a complex sensory environment. Complex mixtures of sounds often arrive at the ears simultaneously or in close succession, yet they are effortlessly segregated into distinct perceptual sources. This process breaks down in hearing-impaired individuals and speech recognition devices. By identifying the underlying neural mechanisms that facilitate perceptual segregation, we can develop strategies for ameliorating hearing loss and improving speech recognition technology in the presence of background noise. Here, we present evidence to support a hierarchical process, present in primary auditory cortex and refined in secondary auditory cortex, in which sound repetition facilitates segregation.
自然声音中的统计规律有助于听觉来源或流的感知分离。重复是驱动人类流分离的一个线索,但这种感知现象的神经基础尚不清楚。我们通过训练雄性和雌性雪貂来检测重复噪声样本(前景)嵌入随机样本(背景)中的流,在动物中证明了类似的感知能力。在被动聆听期间,我们记录了初级听觉皮层(A1)和次级听觉皮层(后外侧乙状回,PEG)的神经活动。我们使用了两种基于上下文的编码模型来测试重复刺激流的证据。第一种基于每个噪声样本的平均诱发活动,第二种基于频谱-时变感受野。这两种方法都检验了对重复和随机刺激的神经反应是否可以通过同等缩放两种流的反应(全局增益)或分别缩放前景点和背景点的反应(流特异性增益)来更好地建模。与之前关于适应的观察结果一致,当刺激开始重复时,我们发现全局增益总体降低。然而,当我们测量增益的流特异性变化时,前景点的反应相对于背景点得到增强。这种增强在 PEG 中比在 A1 中更强。在 A1 中,具有低稀疏性(即广泛的感觉调谐)和对重复样本选择性调谐的单元的增强最强。与背景相比,对前景点的反应增强提供了流分离的证据,这种分离在前听觉皮层中出现,并在 PEG 中得到完善。为了成功地与世界互动,大脑必须从复杂的感官环境中解析出对行为重要的信息。复杂的声音混合物通常同时或紧密地到达耳朵,但它们毫不费力地被分离成不同的感知来源。在听力受损的个体和语音识别设备中,这个过程会崩溃。通过确定促进感知分离的潜在神经机制,我们可以制定策略来改善听力损失,并在存在背景噪声的情况下改善语音识别技术。在这里,我们提供证据支持一个分层过程,该过程存在于初级听觉皮层中,并在次级听觉皮层中得到完善,其中声音重复促进了分离。