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

摘要 人类感知的显著效率来自于这样一个事实，即我们并不把每一个刺激都当作一种刺激来处理。 新事件。相反，过去的经验和场景环境告知了世界的内部工作模型， 让我们能够预测我们的物理环境。一种主流理论认为， 预测是通过灵活的标准化来实现的：局部抑制性神经元群体受到以下因素的调节： 长距离连接，因此当它们不能提供有关 对象边界。然而，健康的人类大脑完成的精确神经机制 这种灵活的标准化是未知的。为了准确理解神经群体的反应是如何 抑制或增强响应不同的场景上下文，我们将在雪貂进行双光子成像 初级视皮层（V1），以量化浅表神经元兴奋性和抑制性神经群的反应， 在几种不同的视觉刺激模式下的皮层层。选择雪貂模型是因为 定量抑制性神经元反应所需的成像技术在灵长类动物中尚未得到很好的建立 模型，虽然我们目前对神经形态和连接的知识来自于 在小鼠模型中，小鼠视觉皮层缺乏"柱状组织"（具有类似于神经元的空间分组）。 反应特性），这是灵长类动物视觉皮层的标志，存在于雪貂中。因此，雪貂模型 很好地填补了小鼠模型和灵长类动物模型之间的差距。首先，为了了解 神经元行为的情况下的上下文调制，我们将表征在一个单一的相互作用 超列到小的简单刺激（正弦调制亮度光栅）， 对比。我们假设，含有小清蛋白（PV+）的抑制性中间神经元将证明， 最尖锐的方向调谐，其次是含生长抑素（SOM+）和降钙素阳性（5HTR+） 人口数量。下一个使用交叉方向抑制范例，我们将测试假设，SOM + 响应跟踪刺激中的整体对比能量，而PV+群体反映了对 单独的光栅组件表示。更多的自然纹理实验将测试 这些行为概括为具有大范围的对比度、方向和空间频率的刺激。 最后，我们将使用经典的方向依赖环绕抑制和共线易化范式 研究局部抑制池对场景的反应。我们假设当地人的反应 5HTR+神经元将反映周围刺激而不是中心刺激。这些实验加在一起 将限制在单个神经元水平上表达的开源计算模型， 限制了关于人类感知行为如何与特定神经元群体联系的假设;这 模型对于理解与精神病相关的知觉畸变可能是有价值的。 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.