Using multiple species, stimuli, and tasks to study the neural basis of visually guided behavior

项目摘要 视觉系统必须不断地从大量的 来自环境的无关输入，使用诸如注意力和学习等认知现象来引导 不断适应过程。了解提取与任务相关的信息的机制 从神经元群的高维活动将对理解复杂的病因至关重要。 许多神经系统疾病，如注意力障碍。长期以来的一个假设是，这是 过程针对每个特定的可视任务进行了优化，从而最大限度地从 神经元群的活动。虽然这在高度简约的实验室环境中是可能的，但使用简单的 这样的具体优化几乎不可能在面对丰富而迅速的 自然界中遇到的不断变化的刺激和任务目标。我们最近的研究提出了一个新的假设： 从神经元群体活动中提取信息并不是针对每个特定的视觉任务进行优化的，而是 通常适用于在现实环境中遇到的各种刺激和任务。 在我们的每个目标中，我们都将使用功能丰富、逼真的视觉刺激、精确的心理物理测量 知觉表现，来自视觉神经元群体的同步记录，以及前沿数据 测试我们中心假设的一个预测的分析技术。在目标1中，我们将测试以下预测 具有与任务相关和与任务无关的视觉特征、神经元信息的变化的真实环境 提取通常针对遇到的所有要素更改进行优化，而不仅仅是针对与任务相关的 改变。在目标2和目标3中，我们将测试我们的中心假设在不同时间的正确程度 画框。在目标2中，我们将检验神经元信息提取过程被优化为 在短时间内灵活应对快速变化的任务目标。在目标3中，我们将测试以下预测 信息提取还可以在长时间尺度上灵活优化，解释循序渐进和高度具体的 由于知觉学习而提高了知觉能力。这些研究的结果将具有广泛的意义 对视觉感知的生物物理模型和我们对神经元如何 一般来说，机制能够灵活地适应我们不断变化的自然环境。 拟议的项目不仅将加深我们对神经元活动如何引导行为的理解 真实视觉环境的背景，但将为我提供必要的技术和分析 作为一名独立调查员开始我的职业生涯的技能。通过接受专家培训来创建和操作 复杂、功能丰富的视觉刺激，通过参数变化收集精确的心理物理测量 这些刺激的具体方面，并应用先进的计算技术来分析互补 行为和神经元数据集，我将做好充分准备，独立探索神经元如何 在我自己的研究项目中，活动指导着人们的认知和行为。

Project Summary The visual system must constantly extract behaviorally relevant stimulus information from an abundance of irrelevant inputs from the environment, using cognitive phenomena such as attention and learning to guide this continuously adapting process. Understanding the mechanisms by which task-relevant information is extracted from the high-dimensional activity of neuronal populations will be vital to understanding the complex etiology of many neurological diseases, such as disorders of attention. A longstanding assumption has been that this process is optimized for each specific visual task, maximizing the amount of information extracted from the activity of neuronal populations. While this may be possible in highly reductionist lab settings with simple stimuli, such specific optimization would be virtually impossible in the face of the abundant and rapidly changing stimuli and task goals encountered in the natural world. Our recent work suggests a new hypothesis: the extraction of information from neuronal population activity is optimized not for each specific visual task, but generally for the wide variety of stimuli and tasks encountered in realistic environments. In each of our Aims, we will use feature-rich, realistic visual stimuli, precise psychophysical measurements of perceptual performance, simultaneous recordings from populations of visual neurons, and cutting edge data analysis techniques to test one prediction of our central hypothesis. In Aim 1, we will test the prediction that in realistic environments with changes to both task-relevant and -irrelevant visual features, neuronal information extraction is optimized generally for all of the encountered feature changes, instead of just for the task-relevant changes. In Aims 2 and 3, we will test the extent to which our central hypothesis is true across different time frames. In Aim 2, we will test the prediction that the neuronal information extraction process is optimized to be flexible on short time scales, in the face of rapidly changing task goals. In Aim 3, we will test the prediction that information extraction can also be flexibly optimized on long time scales, explaining gradual and highly specific improvements in perceptual ability due to perceptual learning. The results of these studies will have broad implications both for biophysical models of visual perception and for our understanding of how neuronal mechanisms in general are able to flexibly adapt to our constantly changing natural environment. The proposed project will not only further our understanding of how neuronal activity guides behavior in the context of realistic visual environments, but will provide me with the necessary technical and analytical skills to launch my career as an independent investigator. By receiving expert training to create and operate complex, feature-rich visual stimuli, to collect precise psychophysical measurements by parametrically varying specific aspects of those stimuli, and to apply advanced computational techniques to analyze complementary behavioral and neuronal datasets, I will be fully prepared to independently pursue questions of how neuronal activity guides perception and behavior in my own research program.