Probing the time scales of perceptual readout with white noise optogenetic inhibition

项目摘要 在视觉引导的行为中，感觉刺激与其行为之间只有数百毫秒的时间。 随后的行动，但尖峰反应整个感觉运动电路来代表刺激的特点， 指导今后的行动。由于大脑回路的巨大复杂性， 哪些尖峰（以及大脑的哪些区域）对行为有因果关系。我们需要能够解决 尖峰脉冲的瞬间波动如何驱动感知和行动。一个多功能的工具， 感觉输入和尖峰或行为之间的关系是白色噪声分析。一个随机的，动态的 提供了一个刺激，可以利用这些波动来生成对刺激的无偏估计 驱动神经元尖峰的特征（尖峰触发的平均）或驱动神经元尖峰的感觉输入的周期 知觉决定（心理物理内核）。我们已经采用了白色噪声分析用于光遗传学分析。 刺激.我们在行为过程中向视觉区域提供弱光遗传抑制的随机模式， 计算与感知报告（命中、未命中）相关的平均光遗传学信号。我们的初步数据 表明白色噪声光遗传学抑制快速产生定义了尖峰效应在 特定的神经元群体到特定的行为，具有几十毫秒的分辨率（光遗传学 行为内核（OBK）。这种精确的定时信息将是不切实际的获得与标准的 光遗传学方法（例如，脉冲或持续抑制）。我们将通过以下方式验证和应用此方法： 确定在较低和较高视觉区域中测量的OBK如何取决于视觉输入和任务需求， 并在此过程中产生新的观察结果，将尖峰模式与整个过程中的感知决策联系起来， 视觉大脑将训练小鼠在感知任务中在阈值下可靠地工作。在第一个实验中， 视觉信息将突然（超阈值强度，对比度阶跃）或逐渐（接近阈值， 对比度斜坡），并且我们将使OBK与刺激诱发的尖峰的分布的变化相关。在 在第二个实验中，将要求小鼠对目标方向做出反应并拒绝干扰物。我们将 测量皮质（纹状和纹外视区）和皮质下（上级丘）视区中的OBK 区域，以确定其各自对任务绩效的贡献的时间过程。一部分小鼠 也可以植入固定丝光极，以便于同时进行电生理记录， 光遗传学的干扰。这些数据将与刺激措施的每时每刻的减少相关联 和/或任务诱发的尖峰与感知报告，并且使得能够在群体尖峰和 OBKs。这项工作将提供前所未有的解决方案，以神经元活动的时期，在皮层和 皮层下视觉通路，对感知和行为有因果关系。我们的实验将证明 一种强大的方法，用于构建内核，该内核将不同视觉区域中的尖峰动态与视觉- 引导行为，一种可以在系统神经科学家中广泛应用的方法。

Project Abstract During visually guided behaviors, only hundreds of milliseconds elapse between a sensory stimulus and its ensuing action, yet spiking responses across sensorimotor circuits come to represent stimulus features and guide forthcoming actions. Owing to the immense complexity of brain circuits, there is great uncertainty as to which spikes (and in which brain areas) causally contribute to behavior. We need methods capable of resolving how moment-to-moment fluctuations in spiking drive perception and action. A versatile tool for examining relationships between sensory input and spiking or behavior is white noise analysis. A random, dynamic stimulus is presented and these fluctuations can be leveraged to generate unbiased estimates of the stimulus features that drive neuronal spiking (spike-triggered average) or the periods of sensory input that drive perceptual decisions (psychophysical kernel). We have adapted white noise analysis for optogenetic stimulation. We deliver random patterns of weak optogenetic inhibition to visual areas during behavior and calculate the average optogenetic signal associated with perceptual reports (hits, misses). Our preliminary data show that white noise optogenetic inhibition rapidly yields kernels that define the contribution of spiking in specific neuronal populations to specific behaviors, with a resolution of a tens of milliseconds (optogenetic behavioral kernel, OBK). Such precise timing information would be impractical to obtain with standard optogenetic approaches (e.g., pulses or sustained inhibition). We will validate and apply this approach by determining how OBKs measured in lower and higher visual areas depend on visual input and task demands, and in doing so generate new observations that link patterns of spiking with perceptual decisions throughout the visual brain. Mice will be trained to work reliably at threshold in perceptual tasks. In the first experiment, visual information will arrive suddenly (super-threshold intensity, contrast steps) or gradually (near-threshold, contrast ramps) and we will relate OBKs to changes in the distributions of stimulus-evoked spikes. In the second experiment, mice will be required to respond to a target orientation and reject distractors. We will measure OBKs in both cortical (striate and extrastriate visual areas) and subcortical (superior colliculus) visual areas to determine the time course of their respective contributions to task performance. A subset of mice will also be implanted with fixed-wire optrodes to facilitate concurrent electrophysiological recordings and optogenetic perturbations during behavior. These data will relate moment-to-moment reductions in stimulus and/or task-evoked spikes with perceptual reports and enable comparisons between population spiking and OBKs. This work will provide unprecedented resolution into the periods of neuronal activity in cortical and subcortical visual pathways that causally contribute to perception and behavior. Our experiments will establish a powerful method for constructing kernels that relate spiking dynamics in different visual areas to visually- guided behaviors, a method that could find wide application among systems neuroscientists.