Most of the mammalian neocortex is comprised of a highly similar anatomical structure, consisting of a granular cell layer between superficial and deep layers. Even so, different cortical areas process different information. Taken together, this suggests that cortex features a canonical functional microcircuit that supports region-specific information processing. For example, the primate primary visual cortex (V1) combines the two eyes' signals, extracts stimulus orientation, and integrates contextual information such as visual stimulation history. These processes co-occur during the same laminar stimulation sequence that is triggered by the onset of visual stimuli. Yet, we still know little regarding the laminar processing differences that are specific to each of these types of stimulus information. Univariate analysis techniques have provided great insight by examining one electrode at a time or by studying average responses across multiple electrodes. Here we focus on multivariate statistics to examine response patterns across electrodes instead. Specifically, we applied multivariate pattern analysis (MVPA) to linear multielectrode array recordings of laminar spiking responses to decode information regarding the eye-of-origin, stimulus orientation, and stimulus repetition. MVPA differs from conventional univariate approaches in that it examines patterns of neural activity across simultaneously recorded electrode sites. We were curious whether this added dimensionality could reveal neural processes on the population level that are challenging to detect when measuring brain activity without the context of neighboring recording sites. We found that eye-of-origin information was decodable for the entire duration of stimulus presentation, but diminished in the deepest layers of V1. Conversely, orientation information was transient and equally pronounced along all layers. More importantly, using time-resolved MVPA, we were able to evaluate laminar response properties beyond those yielded by univariate analyses. Specifically, we performed a time generalization analysis by training a classifier at one point of the neural response and testing its performance throughout the remaining period of stimulation. Using this technique, we demonstrate repeating (reverberating) patterns of neural activity that have not previously been observed using standard univariate approaches.

中文翻译：

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多元层流尖峰分析揭示沿柱状皮层微电路的刺激特征特定信息流


大多数哺乳动物新皮质由高度相似的解剖结构组成，由浅层和深层之间的颗粒细胞层组成。即便如此，不同的皮质区域处理不同的信息。总而言之，这表明皮层具有支持特定区域信息处理的规范功能微电路。例如，灵长类动物初级视觉皮层（V1）结合两只眼睛的信号，提取刺激方向，并整合视觉刺激历史等上下文信息。这些过程在由视觉刺激开始触发的相同层流刺激序列期间同时发生。然而，我们对每种类型的刺激信息特有的层流处理差异仍然知之甚少。单变量分析技术通过一次检查一个电极或研究多个电极的平均响应提供了深刻的见解。在这里，我们专注于多变量统计来检查电极之间的响应模式。具体来说，我们将多元模式分析（MVPA）应用于层流尖峰响应的线性多电极阵列记录，以解码有关原点眼、刺激方向和刺激重复的信息。 MVPA 与传统的单变量方法不同，它检查同时记录的电极部位的神经活动模式。我们很好奇这种增加的维度是否可以揭示群体水平上的神经过程，而在没有邻近记录站点背景的情况下测量大脑活动时很难检测到这些神经过程。 我们发现，在刺激呈现的整个持续时间内，原眼信息都是可解码的，但在 V1 的最深层会减少。相反，方向​​信息是瞬态的，并且在所有层上同样明显。更重要的是，使用时间分辨 MVPA，我们能够评估单变量分析之外的层流响应特性。具体来说，我们通过在神经反应的某一点训练分类器并在剩余的刺激期间测试其性能来进行时间泛化分析。使用这种技术，我们展示了以前使用标准单变量方法未观察到的神经活动的重复（回响）模式。