Center for Neuroscience, University of California, Davis, Davis, California 95618
Department of Otolaryngology, Head and Neck Surgery, University of California, San Francisco, California 94143.
J Neurosci. 2021 Sep 8;41(36):7561-7577. doi: 10.1523/JNEUROSCI.0693-20.2021. Epub 2021 Jul 1.
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. 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.
教科书对初级感觉皮层 (PSC) 的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) 中视觉物体的方向、初级躯体感觉皮层 (S1) 中躯体感觉的位置和初级听觉皮层 (A1) 中的声音频率。通常，PSC 的研究沿少数（一个或两个）刺激和/或行为维度测量神经元的反应。然而，现实世界中的刺激通常沿许多特征维度变化，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，我们在猕猴（一雄一雌）执行特征选择性注意任务时记录了 A1 神经元的反应。我们呈现了沿光谱和时间特征维度变化的声音（分别为载波带宽和时间包络）。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，因为它们对声音的两个特征以及任务上下文都表现出大量的混合选择性。我们没有发现单个神经元对注意特征的编码有整体增强，因为注意力既可以减弱也可以增强这种编码。然而，群体水平的分析表明，神经元的集合表现出对注意声音特征的增强编码，并且该群体编码跟踪受试者的表现。重要的是，具有完整单神经元调谐但神经元之间高阶相关性混乱的替代神经群体未能产生在完整数据中观察到的与注意力相关的影响。这些结果表明，在 PSC 中，可能是一种不可测量的涌现群体代码构成了感觉表示的功能单元。适应动态感觉环境的能力促进了一系列重要的自然行为。我们记录了猴子初级听觉皮层 (A1) 中的单个神经元的活动，同时受试者注意复杂声音的光谱或时间特征。令人惊讶的是，我们没有发现单个神经元对注意特征的反应性或编码有平均增加。然而，当我们通过有针对性的维度降低 (TDR) 对采样神经元的活动进行分组时，我们发现了对注意特征的增强群体水平表示和对分心特征的抑制。这种注意力效应在单个神经元水平与群体水平之间的分离突出了皮质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。
