Textbook descriptions of primary sensory cortex (PSC) revolve around single neurons' representation of low-dimensional sensory features, such as visual object orientation in primary visual cortex (V1), location of somatic touch in primary somatosensory cortex (S1), and sound frequency in primary auditory cortex (A1). Typically, studies of PSC measure neurons' responses along few (one or two) stimulus and/or behavioral dimensions. However, real-world stimuli usually vary along many feature dimensions and behavioral demands change constantly. In order to illuminate how A1 supports flexible perception in rich acoustic environments, we recorded from A1 neurons while rhesus macaques (one male, one female) performed a feature-selective attention task. We presented sounds that varied along spectral and temporal feature dimensions (carrier bandwidth and temporal envelope, respectively). Within a block, subjects attended to one feature of the sound in a selective change detection task. We found that single neurons tend to be high-dimensional, in that they exhibit substantial mixed selectivity for both sound features, as well as task context. We found no overall enhancement of single-neuron coding of the attended feature, as attention could either diminish or enhance this coding. However, a population-level analysis reveals that ensembles of neurons exhibit enhanced encoding of attended sound features, and this population code tracks subjects' performance. Importantly, surrogate neural populations with intact single-neuron tuning but shuffled higher-order correlations among neurons fail to yield attention- related effects observed in the intact data. These results suggest that an emergent population code not measurable at the single-neuron level might constitute the functional unit of sensory representation in PSC.

SIGNIFICANCE STATEMENT The ability to adapt to a dynamic sensory environment promotes a range of important natural behaviors. We recorded from single neurons in monkey primary auditory cortex (A1), while subjects attended to either the spectral or temporal features of complex sounds. Surprisingly, we found no average increase in responsiveness to, or encoding of, the attended feature across single neurons. However, when we pooled the activity of the sampled neurons via targeted dimensionality reduction (TDR), we found enhanced population-level representation of the attended feature and suppression of the distractor feature. This dissociation of the effects of attention at the level of single neurons versus the population highlights the synergistic nature of cortical sound encoding and enriches our understanding of sensory cortical function.

初级感觉皮层 (PSC) 的教科书描述围绕着单个神经元对低维感觉特征的表示，例如初级视觉皮层 (V1) 中的视觉对象方向、初级体感皮层 (S1) 中的体触位置和声音频率在初级听觉皮层 (A1)。通常，对 PSC 的研究会测量神经元在少数（一两个）刺激和/或行为维度上的反应。然而，现实世界的刺激通常会随着许多特征维度而变化，行为需求也在不断变化。为了阐明 A1 如何在丰富的声学环境中支持灵活的感知，我们从 A1 神经元记录，而恒河猴（一只雄性，一只雌性）执行特征选择性注意任务。我们呈现了随频谱和时间特征维度（分别为载波带宽和时间包络）而变化的声音。在一个块内，受试者在选择性变化检测任务中注意声音的一个特征。我们发现单个神经元往往是高维的，因为它们对声音特征和任务上下文表现出大量的混合选择性。我们发现参与特征的单神经元编码没有整体增强，因为注意力可能会减少或增强这种编码。然而，群体水平的分析表明，神经元的集合表现出对参与声音特征的增强编码，并且该群体代码跟踪受试者的表现。重要的，具有完整单神经元调谐但改组神经元之间高阶相关性的替代神经群体未能产生在完整数据中观察到的注意力相关效应。这些结果表明，在单神经元水平无法测量的新兴群体代码可能构成 PSC 中感觉表征的功能单元。

意义声明适应动态感官环境的能力促进了一系列重要的自然行为。我们从猴子初级听觉皮层 (A1) 的单个神经元进行录音，而受试者则关注复杂声音的频谱或时间特征。令人惊讶的是，我们发现单个神经元对参与特征的响应或编码没有平均增加。然而，当我们通过目标降维 (TDR) 汇集采样神经元的活动时，我们发现了参与特征的增强的群体级表示和干扰特征的抑制。这种在单个神经元与群体水平上注意力效应的分离突出了皮层声音编码的协同性质，并丰富了我们对感觉皮层功能的理解。