Flexible normalization in ferret V1: computational modeling and 2-photon imaging

摘要 人类感知的非凡效率源于这样一个事实，即我们不会将每一种刺激视为 新奇的事件。相反，过去的经验和场景背景告诉世界的内部工作模型 使我们能够对我们的物理环境进行预测。一种领先的理论认为，感性的 预测是通过灵活的归一化完成的：局部抑制神经元群体由 远程连接，以便在未提供有关以下方面的有用信息时抑制响应 对象边界。然而，健康的人脑通过精确的神经机制 这种灵活的标准化是未知的。为了准确地了解神经细胞群体反应是如何 根据不同的场景背景，我们将对雪貂进行双光子成像 初级视皮层(V1)量化浅层兴奋性和抑制性神经元群的反应 在几种不同的视觉刺激模式下，大脑皮质的不同层次。之所以选择雪貂模型，是因为 在灵长类动物中，量化抑制性神经元反应所需的成像技术尚未得到很好的建立 模型，虽然我们目前关于神经形态和连接的知识来自于 在小鼠模型中，小鼠视皮层缺乏“柱状组织”(具有相似的神经元的空间分组 反应特性)，这是灵长类视觉皮质的一个标志，存在于雪貂身上。因此，雪貂模型 处于很好的位置，可以弥合老鼠模型和灵长类模型之间的差距。首先，为了了解 神经元行为在没有语境调节的情况下，我们将描述单个 超柱到小的、简单的刺激(正弦调制的亮度栅格)，在一定的方向和范围内 反差很大。我们假设含有小白蛋白(PV)的抑制性中间神经元将证明 最敏锐的定向调整，其次是生长抑素(SOM)和5-羟色胺阳性(5HTR) 人口。下一首。使用交叉方向抑制范式，我们将测试SOM的假设 反应跟踪刺激中的总体对比能量，而光伏种群反映抑制 单个栅格组件表示法。对自然纹理的更多实验将测试 这些行为概括为具有广泛对比度、方位和空间频率的刺激。 最后，我们将使用经典的方位依赖环绕抑制和共线促进范式 研究局部抑制池如何对场景背景做出反应。我们假设当地的反应 5HTR神经元将反映周围刺激，而不是中心刺激。总而言之，这些实验 将限制在单个神经元级别表达的开源计算模型，该模型将 限制关于人类感知行为如何与特定神经元群体联系的假设；这 模型将对理解与精神病有关的知觉偏差有多大帮助 映射到特定神经元亚群的功能。

ABSTRACT The remarkable efficiency of human perception derives from the fact that we do not process each stimulus as a novel event. Instead, past experiences and scene context inform internal, working models of the world that allow us to generate predictions for our physical environment. A leading theory suggests that perceptual predictions are accomplished via flexible normalization: local inhibitory neuronal populations are regulated by long-range connections so that responses are suppressed when they do not provide helpful information about object boundaries. However, the precise neural mechanisms by which the healthy human brain accomplishes this flexible normalization are not known. In order to understand exactly how neural population responses are suppressed or enhanced in response to different scene contexts, we will perform 2-photon imaging in ferret primary visual cortex (V1) to quantify the responses of excitatory and inhibitory neural populations in superficial layers of cortex during several different visual stimulus paradigms. The ferret model is chosen because the imaging techniques necessary to quantify inhibitory neuronal responses are not yet well established in primate models, and while our current knowledge about neural morphology and connections has been derived from mouse models, mouse visual cortex lacks the “columnar organization” (spatial grouping of neurons with similar response properties) that is a hallmark of primate visual cortex and is present in ferrets. Thus, the ferret model is well-positioned to bridge the gap between mouse models and primate models. First, in order to understand neuronal behaviors in the absence of contextual modulation, we will characterize interactions within a single hypercolumn to small, simple stimuli (sinusoidally modulated luminance gratings) at a range of orientations and contrasts. We hypothesize that parvalbumin-containing (PV+) inhibitory interneurons will demonstrate the sharpest orientation tuning, followed by somatostatin-containing (SOM+) and serotonin-positive (5HTR+) populations. Next. using a Cross Orientation Suppression paradigm, we will test the hypothesis that that SOM+ responses track the overall contrast energy in the stimulus, while PV+ populations reflect suppression of individual grating component representations. Additional experiments with naturalistic textures will test whether these behaviors generalize to stimuli with a broad range of contrasts, orientations, and spatial frequencies. Finally, we will use classical Orientation-Dependent Surround Suppression and Collinear Facilitation paradigms to study how the local inhibitory pool responds to scene context. We hypothesize that the responses of local 5HTR+ neurons will reflect the surrounding stimuli rather than the center stimuli. Together, these experiments will constrain an open-source computational model articulated at the level of the single neuron that will constrain hypotheses about how human perceptual behaviors are linked to specific neuronal populations; this model will be valuable for understanding how perceptual aberrations associated with psychosis might be mapped to the function of specific neuronal subpopulations.